Systems, devices, and methods for protecting patient health for fluid infusions

Abstract

To provide a system and method for promoting and safeguarding the wellness of patients in relation to fluid injection.SOLUTION: The system and method may perform the steps of: obtaining patient data; determining, based on the patient data, an initial risk prediction for a patient for a fluid injection to be administered to the patient, the initial risk prediction including a probability that the patient experiences at least one adverse event in response to the fluid injection; providing the initial risk prediction to a user device before the fluid injection is administered to the patient; determining, after the fluid injection is started, sensor data associated with the patient; determining, based on the sensor data determined after the fluid injection is started, a current risk prediction including a probability that the patient experiences the at least one adverse event in response to the fluid injection; and providing the current risk prediction to the user device.SELECTED DRAWING: Figure 1B

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 017,942, filed April 30, 2020, U.S. Provisional Patent Application No. 62 / 706,597, filed August 27, 2020, U.S. Provisional Patent Application No. 62 / 704,954, filed June 4, 2020, and U.S. Provisional Patent Application No. 62 / 705,613, filed July 7, 2020, the disclosures of which are incorporated by reference in their entireties. [Background technology]

[0002] With the rise of medical imaging over the past few decades, the use of radiological contrast agents has increased significantly across all modalities. Typically, 76 million computed tomography (CT) and 34 million magnetic resonance (MR) imaging examinations are performed annually, with approximately half of these examinations using intravenous contrast agents. The use of intravenous contrast agents is recognized by the radiological community, and the contrast agent materials themselves are considered safe. In addition to the drug safety of contrast agents, the actual use of contrast agents can pose various risks depending on the application itself.

[0003] For example, issues related to patient safety and contrast injection include, at least, (i) preventing, detecting, and minimizing extravasation of extravasated material; (ii) minimizing acute adverse events or documented acute adverse events resulting from contrast injection in contrast-naive patients and patients with known atopy; (iii) preventing contrast-induced nephrotoxicity and / or post-contrast renal injury; and (iv) managing patients to prevent thyroid disorders, such as thyrotoxicosis (TX).

[0004] Extravasation is a rare but serious problem in contrast medical imaging procedures. Extravasation occurs when contrast media intended for delivery to the central circulation through peripheral vascular access instead enters peripheral tissues (e.g., when contrast media leaks from the vascular lumen and infiltrates interstitial tissues during injection). The incidence of intravenous contrast media extravasation is typically reported as less than 1% and does not directly correlate with the injection flow rate. However, while some patients with extravasation may remain asymptomatic, others may report swelling, tightness, tingling, or burning pain and may exhibit edema, erythema, or tenderness at the injection site. Serious complications of extravasation include compartment syndrome, skin ulcers, and / or tissue necrosis.

[0005] Acute adverse events depend on the agent being administered. The rate of acute adverse events for low-osmolar iodinated contrast agents is approximately 0.2% to 0.7%, with a 0.04% rate for severe acute reactions. The incidence of acute adverse events to gadolinium-based contrast agents (GBCAs) is low, occurring approximately once per 10,000 to 40,000 injections. Most reactions are mild and transient, with cutaneous reactions being the most frequent. Severe, life-threatening, anaphylactoid reactions to GBCAs are rare. Risk factors for acute adverse events to contrast agents include previous reactions to iodinated contrast agents, severe allergies and reactions to drugs and / or foods, a history of asthma, bronchospasm, and / or atopy, and a history of cardiac or renal disease.

[0006] Contrast-induced nephrotoxicity can be defined as a sudden deterioration in renal function (e.g., acute kidney injury) after recent intravascular administration of a contrast agent in the absence of another nephrotoxic event. Risk factors for contrast-induced nephrotoxicity may include hypertension, proteinuria, gout, and / or previous renal surgery. The risk of contrast-induced nephrotoxicity is considered low in patients with normal, stable renal function. Similarly, postcontrast acute kidney injury is a general term used to describe a sudden deterioration in renal function within 48 hours of intravascular administration of an iodine-based contrast agent.

[0007] For the application of iodinated contrast agents, which reflects the majority of contrast agent use, patients with untreated Graves' disease and / or with multinodular goiter and thyroid autonomy, the elderly, and patients living in areas where dietary iodine deficiency is common may be at increased risk for thyrotoxicosis due to excess iodine absorption. Furthermore, the use of iodinated contrast agents before any planned radioiodine imaging or treatment may reduce radioiodine uptake.

[0008] Furthermore, the relative rarity of adverse events makes it difficult to justify the cost and time required to monitor infusions for adverse events using existing devices, and it is difficult for vigilant medical professionals to manually identify the small number of patients who may experience adverse events. Additionally, patient satisfaction has recently become an important factor in financial reimbursement for healthcare providers. Summary of the Invention [Means for solving the problem]

[0009] Thus, improved systems, devices, products, apparatus, and / or methods are provided for assessing, promoting, and protecting patient health for fluid injection (e.g., before, during, and / or after contrast injection), thereby providing sensing and / or interpretation capabilities that utilize multiple data sources to assess at least one of patient health, risk of adverse events, maintain patient health, and / or reduce or prevent the occurrence of adverse events, minimize the occurrence or severity of adverse events, detect adverse events, and / or make recommendations or actions to manage adverse events, e.g., extravasation, acute adverse events, contrast-induced nephrotoxicity and / or post-contrast renal injury, and / or thyroid disease, thereby improving patient satisfaction, reimbursement, and reducing the occurrence of complications associated with contrast injection. A further advantage of the provided systems, devices, products, apparatus, and / or methods may be that by assessing and supporting the promotion of a patient's overall health, they are applicable and useful in the medical care of all patients, not just in preventing or reducing harm to the minority who may experience serious adverse events. Thus, the systems, devices, products, apparatus, and / or methods provided are more likely to become part of normal workflow and be used for all patients, thereby providing these benefits to all patients.

[0010] Non-limiting embodiments or aspects are described in the following numbered callouts:

[0011] and providing the current risk prediction to the user device before the fluid infusion is administered to the patient.

[0012] Appendix 2. The system of Appendix 1, wherein the at least one processor is further programmed and / or configured to automatically control at least one of (i) a fluid injection system to stop the fluid injection, and (ii) an imaging system to adjust timing of imaging operations based on the current risk prediction.

[0013] 3. The system of claim 1 or 2, wherein the patient data includes at least one of the following parameters associated with the patient: age, sex, weight, previous chemotherapy status, estimated glomerular filtration rate (eGFR), thyroid-stimulating hormone (TSH) level, triiodothyronine (FT3) thyroxine (FT4) ratio (FT3 / FT4), level of environmental influence, prior response to previous fluid infusion status, atopic disease status, medical status associated with at least one of diabetes and hypertension, congestive heart failure status, hematocrit level, renal failure status, malignancy status, implanted device for central venous access status, type of medication, type of fluid medium administered in the fluid infusion, infusion protocol associated with the fluid infusion, type of imaging test, flow rate associated with the fluid infusion, catheter gauge associated with the fluid infusion, total volume of fluid associated with the fluid infusion, pressure curve associated with the fluid infusion, pressure limit curve associated with the fluid infusion, infusion site location associated with the fluid infusion, or any combination thereof.

[0014] Clause 4. The system of any one of Clauses 1 to 3, wherein the at least one adverse event comprises at least one of the following adverse events: extravasation, post-contrast acute kidney injury, an acute adverse event, contrast-induced nephrotoxicity, thyrotoxicosis, or any combination thereof.

[0015] Appendix 5. The system of any one of Appendixes 1 to 4, wherein the initial risk prediction further includes at least one of a prompt to administer medication to the patient before the fluid injection, a prompt to adjust an injection protocol for the fluid injection, a prompt to adjust an imaging protocol for an imaging scan, a prompt to prepare the patient before the fluid injection, a prompt to observe and / or follow up with the patient after the fluid injection, or any combination thereof.

[0016] Clause 6. The system of any one of Clauses 1 to 5, wherein the sensor data includes at least one of the following parameters related to the patient: heart rate, sound or vibration, temperature, oxygen saturation, ECG, body fat / water ratio, tissue impedance, vascularity level, blood vessel diameter, hydration level, hematocrit level, skin resistivity, blood pressure, muscle tension level, light absorptance level, exercise level, arm position, arm circumference, respiratory rate, radiation absorption, EMG, skin color, surface vasodilation, bioimpedance, light absorptance, hemoglobin level, inflammation level, ambient temperature of an environment surrounding the patient, air pressure of an environment surrounding the patient, ambient light level, ambient sound level, or any combination thereof.

[0017] Appendix 7. The system of any one of Appendixes 1 to 6, further comprising at least one sensor configured to determine the sensor data associated with the patient after the fluid infusion has begun.

[0018] Appendix 8. The system of any one of Appendixes 1 to 7, wherein the at least one sensor is further configured to determine the sensor data during a test injection administered to the patient prior to the fluid injection, and the at least one processor is further programmed and / or configured to: determine a test prediction based on the sensor data determined during the test injection, the test prediction including a probability that the patient will experience extravasation in response to the fluid injection; and provide the test prediction to the user device.

[0019] Appendix 9. The system of any one of Appendixes 1 to 8, wherein the at least one sensor includes three sound or vibration sensors positioned at three different locations on the patient's limb proximate an infusion site for the test infusion, and wherein the at least one processor is further programmed and / or configured to: combine, through triangulation, the data streams of sensor data from each sound or vibration sensor of the three sound or vibration sensors to create a combined data stream; and determine the test prediction based on the combined data stream.

[0020] Appendix 10. The system of any one of Appendixes 1 to 9, wherein the at least one sensor is further configured to determine the sensor data prior to the test injection, and wherein determining the initial risk prediction is further based on the sensor data determined prior to the test injection.

[0021] Appendix 11. The system of any one of Appendixes 1 to 10, wherein the at least one sensor is further configured to determine the sensor data during the fluid injection, and the at least one processor is further programmed and / or configured to: determine the current risk prediction based on the sensor data determined during the fluid injection; and provide the current risk prediction to the user device during the fluid injection.

[0022] Appendix 12. The system of any one of Appendixes 1 to 11, wherein the at least one sensor is further configured to determine the sensor data after the fluid injection, and the at least one processor is further programmed and / or configured to: determine the current risk prediction based on the sensor data determined after the fluid injection; and provide the current risk prediction to the user device after the fluid injection.

[0023] Clause 13. The system of any one of Clauses 1 to 12, wherein the at least one adverse event includes an extravasation, and wherein the at least one processor is further programmed and / or configured to, in response to determining that the patient is experiencing the extravasation, automatically control a fluid infusion system to stop the fluid infusion, thereby providing the current risk prediction.

[0024] Appendix 14. The system of any one of Appendixes 1 to 13, wherein the at least one sensor comprises at least one of the following sensors: an image capture device; an accelerometer; a strain gauge; a global positioning system (GPS); a skin resistivity or conductance sensor; a heart rate monitor; a microphone; a thermal or temperature sensor; a pulse oximeter; a hydration sensor; a dosimeter; an ultrasonic sensor; an acoustic sensor; one or more electrodes configured to measure at least one of tissue impedance, an electromyogram (EMG), and an electrocardiogram (ECG); a microwave sensor; a mechanical impedance sensor; a chemical sensor; a force or pressure sensor; or any combination thereof.

[0025] Appendix 15. The system of any one of Appendixes 1 to 14, further comprising a sensor device, wherein the at least one sensor is included in the sensor device, the sensor device including an elongated housing extending between a first end and a second end, the elongated housing configured to surround a patient's limb, the elongated housing including a flexible exterior, an interior of the elongated housing including the at least one sensor and a wireless communication device, the wireless communication device configured to wirelessly transmit the sensor data to an external device.

[0026] Addendum 16. The system of any one of Addendums 1 to 15, wherein the interior of the elongated housing containing the at least one sensor and the wireless communication device is fluidly sealed from the external environment by the flexible exterior of the elongated housing.

[0027] Appendix 17. The system of any one of Appendixes 1 to 16, wherein the at least one sensor includes a plurality of sensors spaced apart from one another along a length of the elongated housing extending from the first end of the elongated housing to the second end of the elongated housing, whereby the plurality of sensors are oriented in a circumferential pattern around the patient's limb when the elongated housing encircles the patient's limb, and the plurality of sensors are configured to determine the sensor data in a cross-section of the patient's limb.

[0028] Appendix 18. The system of any one of Appendixes 1 to 17, wherein the at least one adverse event comprises an extravasation, the at least one sensor comprises an image capture device, the image capture device configured to determine the sensor data, the sensor data determined by the image capture device is associated with a plurality of images of the patient over a period of time, and the at least one processor is further programmed and / or configured to determine the current risk prediction comprising the probability that the patient will experience the extravasation based on the plurality of images of the patient over the period of time.

[0029] Appendix 19. The system of any one of Appendixes 1 to 18, wherein the at least one processor is further programmed and / or configured to at least one of: process the plurality of images of the patient over the period of time to highlight changes in at least one of color and motion between the plurality of images; display the plurality of images including the highlighted changes to a user using a display; and determine the current risk prediction including the probability that the patient will experience the extravasation based on the highlighted changes; and in response to determining that the current risk prediction including the probability that the patient will experience the extravasation meets a threshold probability, use the at least one processor to automatically control a fluid injection system to stop the fluid injection.

[0030] and determining a current risk prediction comprising the probability that the patient will experience the extravasation based on the difference in absorption spectra between the first location on the patient and the second location on the patient; and automatically controlling a fluid injection system to stop the fluid injection in response to determining that the current risk prediction comprising the probability that the patient will experience the extravasation meets a threshold probability.

[0031] Appendix 21. The system of any one of Appendixes 1 to 20, wherein the first location on the patient includes a blood vessel of the patient and the second location on the patient includes tissue of the patient surrounding the blood vessel of the patient.

[0032] Clause 22. The system of any one of clauses 1 to 21, further comprising a sound-generating device configured to induce a sound signal in fluid delivered to the patient during the fluid injection, and wherein the at least one sensor includes a sound or vibration sensor.

[0033] Addendum 23. The system of any one of Addendums 1 to 22, wherein the sound-generating device includes an oscillator connected to at least one of a syringe and a fluid path element that delivers the fluid to the patient during the fluid injection.

[0034] Addendum 24. The system of any one of Addendums 1 to 23, wherein at least one of the frequency and amplitude of the sound signal is adjusted to improve detection by the at least one sensor.

[0035] Clause 25. The system of any one of Clauses 1 to 24, wherein the at least one sensor includes a sound or vibration sensor configured to measure at least one of a frequency and an amplitude of sound or vibration of the patient, and wherein the at least one processor is further programmed and / or configured to: determine, based on the at least one of the frequency and the amplitude of the measured sound or vibration of the patient, the current risk prediction including the probability that the patient will experience the extravasation; and, in response to determining that the current risk prediction including the probability that the patient will experience an extravasation meets a threshold probability, automatically control, using the at least one processor, a fluid infusion system to stop the fluid infusion.

[0036] Appendix 26. The system of any one of Appendixes 1 to 25, wherein the at least one processor is further programmed and / or configured to determine a patient's distress level based on the sensor data determined after the fluid injection has begun, and to provide the patient's distress level to the user device.

[0037] Addendum 27. The system of any one of Addendums 1 to 26, wherein the at least one processor is further programmed and / or configured to: compare the patient's pain level with at least one threshold level; and, in response to determining that the patient's pain level meets the at least one threshold level, provide an alert to a user device; and automatically control at least one of (i) a fluid injection system to stop the fluid injection, and (ii) an imaging system to adjust timing of imaging operations.

[0038] Appendix 28. The system of any one of Appendixes 1 to 27, wherein the at least one processor is further programmed and / or configured to determine the distress level of the patient by determining a change in one or more parameters of the sensor data over a period of time and comparing the change in the one or more parameters to at least one threshold change.

[0039] Appendix 29. The system of any one of Appendixes 1 to 28, wherein the sensor data includes at least one of the following parameters associated with the patient: heart rate, oxygen saturation, skin resistivity, skin color, exercise level, temperature proximate to the injection site, or any combination thereof.

[0040] Addendum 30. The system of any one of Addendums 1 to 29, wherein the at least one sensor includes at least one of a pulse oximeter, a skin resistance sensor, a skin color sensor, an accelerometer, a temperature sensor, or any combination thereof.

[0041] Addendum 31. The system of any one of Addendums 1 to 30, further comprising a sensor device, wherein the at least one sensor is included in the sensor device, the sensor device including a glove-shaped housing configured to be worn on the patient's hand, the housing including the at least one sensor and a wireless communication device, the wireless communication device configured to wirelessly transmit the sensor data to an external device.

[0042] Clause 32. The system of any one of Clauses 1 to 31, further comprising a sensor device, wherein the at least one sensor is included in the sensor device, the sensor device including an elongated housing extending between a first end and a second end, and a pulse oximeter connected to the elongated housing via a wire, the elongated housing configured to encircle at least one of a patient's hand and wrist, the elongated housing including a wireless communication device and at least one of a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof, wherein the pulse oximeter, the skin resistance sensor, the accelerometer, the temperature sensor, or any combination thereof, are configured to determine the sensor data, and the wireless communication device is configured to wirelessly transmit the sensor data to an external device.

[0043] Appendix 33. The system of any one of Appendixes 1 to 32, wherein the at least one processor is further programmed and / or configured to control at least one of a light, a display, a speaker, and a tactile device to provide at least one of visual, audio, and tactile instructions to guide the patient's breathing and / or positioning.

[0044] Addendum 34. The system of any one of Addendums 1 to 33, wherein the at least one processor is further programmed and / or configured to adjust the at least one of the visual instructions, the audio instructions, and the tactile instructions for guiding the breathing and / or the positioning of the patient based on timing of an imaging operation of an imaging system.

[0045] Addendum 35. The system of any one of Addendums 1 to 34, wherein the at least one processor is further programmed and / or configured to: determine a level of distress of the patient based on the sensor data determined after the fluid infusion has begun; and, in response to determining that the patient is in distress, adjust the at least one of the visual, audio, and tactile instructions for guiding the breathing and / or the positioning of the patient.

[0046] Addendum 36. A system comprising: at least one sensor configured to determine sensor data associated with a patient before a fluid infusion associated with the patient is initiated; and at least one processor programmed and / or configured to: acquire patient data associated with the patient; determine an initial risk prediction for the patient associated with a fluid infusion to be administered to the patient based on the patient data and the sensor data, the initial risk prediction including a probability that the patient will experience at least one adverse event in response to the fluid infusion; and provide the initial risk prediction to a user device before the fluid infusion is administered to the patient.

[0047] Addendum 37. A system comprising: at least one sensor configured to determine sensor data associated with a patient after a fluid infusion associated with the patient has been initiated; and at least one processor programmed and / or configured to: determine a current risk prediction for the patient associated with the fluid infusion based on the sensor data determined after the fluid infusion has been initiated, the current risk prediction including a probability that the patient will experience at least one adverse event in response to the fluid infusion; and provide the current risk prediction to the user device.

[0048] Addendum 38. A system, comprising at least one processor, programmed and / or configured to: acquire sensor data associated with a patient, the sensor data being determined after a fluid infusion associated with the patient has been initiated; determine a current risk prediction for the patient associated with the fluid infusion based on the sensor data determined after the fluid infusion has been initiated, the current risk prediction including a probability that the patient will experience at least one adverse event in response to the fluid infusion; provide the current risk prediction to the user device; and automatically control at least one of: (i) a fluid infusion system to stop the fluid infusion; and (ii) an imaging system to adjust timing of imaging operations based on the current risk prediction.

[0049] Addendum 39. A system comprising: at least one sensor configured to determine sensor data associated with a patient at least one of before, during, and after a fluid infusion associated with the patient; and at least one processor programmed and / or configured to: determine a fitness level of the patient at least one of before, during, and during the fluid infusion based on the sensor data; and provide the fitness level of the patient to a user device.

[0050] Appendix 40. A system comprising at least one processor programmed and / or configured to: acquire sensor data associated with the patient, the sensor data being determined after a fluid infusion associated with the patient has been initiated; determine a fitness level of the patient during the fluid infusion based on the sensor data determined after the fluid infusion has been initiated; provide the fitness level of the patient to a user device; and automatically control, based on the fitness level of the patient, at least one of: (i) a fluid infusion system that adjusts at least one of a maximum flow rate, a maximum pressure, an infusion duration, a total volume of fluid, or any combination thereof, of the fluid infusion; and (ii) an imaging system that adjusts timing of an imaging operation.

[0051] Addendum 41. A system, comprising at least one processor, programmed and / or configured to: provide, via an application program interface (API), to at least one user device, information related to a fluid infusion to be administered to a patient; receive, via the API, patient data related to the patient from the at least one user device prior to the fluid infusion, the patient data including at least one patient preference related to the fluid infusion, the at least one patient preference related to the fluid infusion including at least one of the following patient preferences: lighting preferences during the fluid infusion; audio preferences during the fluid infusion; temperature preferences during the fluid infusion, or any combination thereof; and automatically control, during the fluid infusion, at least one of: (i) a light source; (ii) a sound source; (iii) a tactile device; (iv) a heating, ventilation, and air conditioning (HVAC) system, or any combination thereof, based on the at least one patient preference.

[0052] Addendum 42. The system of Addendum 41, further comprising at least one of a fluid injector and a medical imaging device, wherein the at least one of the fluid injector and the medical imaging device includes at least one of (i) the light source, (ii) the sound source, (iii) the tactile device, or any combination thereof.

[0053] Clause 43. The system of clauses 41 and 42, wherein the light source includes a display of the at least one of the fluid injector and the medical imaging device.

[0054] Addendum 44. The system of any one of Addendums 41 to 43, wherein the tactile device includes a bed or table of the medical imaging apparatus.

[0055] Addendum 45. The system of any one of Addendums 41 to 44, further comprising at least one sensor configured to determine sensor data related to the patient during the fluid injection, wherein the at least one processor is further programmed and / or configured to automatically control at least one of (i) the light source, (ii) the sound source, (iii) the tactile device, or any combination thereof, to guide breathing and / or positioning of the patient during the fluid injection based on the sensor data.

[0056] Addendum 46. The system of any one of Addendums 41 to 45, wherein the at least one processor further automatically controls the at least one of (i) the light source, (ii) the sound source, (iii) the tactile device, or any combination thereof, based on timing of imaging operations of the medical imaging device during the fluid injection.

[0057] Addendum 47. The system of any one of Addendums 41 to 46, wherein the at least one processor is further programmed and / or configured to automatically control, based on the sensor data, at least one of: (i) a fluid injector to stop the fluid injection; and (ii) a medical imaging device to adjust the timing of imaging operations.

[0058] Addendum 48. A system comprising at least one processor, the processor programmed and / or configured to: provide information related to a fluid infusion to be administered to a patient to at least one user device via an application program interface (API); and receive patient data related to the patient from the at least one user device via the API prior to the fluid infusion, the patient data including at least one patient preference related to the fluid infusion, the at least one patient data being used in risk prediction to assess a probability that the patient will experience an adverse event during the fluid infusion.

[0059] Addendum 49. The system of Addendum 48, further comprising at least one of a fluid injector and a medical imaging device, wherein at least one parameter of an injection protocol of the fluid injection and an imaging protocol of the medical imaging device is adjusted based on the at least one patient data.

[0060] Addendum 50. A sensor device comprising: at least one sensor configured to determine sensor data associated with a patient at least one of before, during, and after an infusion of a fluid associated with the patient; and an elongated housing extending between a first end and a second end, the elongated housing configured to encircle a limb of the patient, the elongated housing including a flexible exterior, an interior of the elongated housing including the at least one sensor and a wireless communication device, the wireless communication device configured to wirelessly transmit the sensor data to an external device.

[0061] Addendum 51: The system described in Addendum 50, wherein the interior of the elongated housing containing the at least one sensor and the wireless communication device is fluidly sealed from the external environment by the flexible exterior of the elongated housing.

[0062] Addendum 52. The system of Addendums 50 and 51, wherein the at least one sensor includes a plurality of sensors spaced apart from one another along a length of the elongated housing extending from the first end of the elongated housing to the second end of the elongated housing, whereby the plurality of sensors are oriented in a circumferential pattern around the patient's limb when the elongated housing encircles the patient's limb, and the plurality of sensors are configured to determine the sensor data in a cross-section of the patient's limb.

[0063] Addendum 53. The system of any one of Addendums 50 to 52, wherein the at least one sensor includes at least one of the following sensors: an image capture device; an accelerometer; a strain gauge; a global positioning system (GPS); a skin resistivity or conductance sensor; a heart rate monitor; a microphone; a thermal or temperature sensor; a pulse oximeter; a hydration sensor; a dosimeter; an ultrasonic sensor; an acoustic sensor; one or more electrodes configured to measure at least one of tissue impedance, an electromyogram (EMG), and an electrocardiogram (ECG); a microwave sensor; a mechanical impedance sensor; a chemical sensor; a force or pressure sensor; or any combination thereof.

[0064] Addendum 54. A sensor device comprising: at least two sensors configured to measure at least two different parameters associated with a patient at least one of before, during, and after a fluid infusion associated with the patient; and at least one processor programmed and / or configured to: determine, based on the at least two different parameters, (i) a patient distress level, and (ii) at least one of a patient risk prediction associated with the fluid infusion, wherein the risk prediction comprises a probability that the patient will experience at least one adverse event in response to the fluid infusion; and provide the at least one of the patient distress level and the risk prediction to a user device.

[0065] Addendum 55. A sensor device comprising: at least one sensor configured to determine sensor data associated with a patient at least one of before, during, and after a fluid infusion associated with the patient; and a glove-shaped housing configured to be worn on the patient's hand, the housing containing the at least one sensor and a wireless communication device, the wireless communication device configured to wirelessly transmit the sensor data to an external device.

[0066] Clause 56. A sensor device comprising: an elongated housing extending between a first end and a second end; and a pulse oximeter connected to the elongated housing via a wire, the elongated housing configured to encircle at least one of the patient's hand and wrist, the elongated housing comprising a wireless communication device and at least one sensor, the at least one sensor comprising at least one of a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof, the pulse oximeter and the at least one of the skin resistance sensor, the accelerometer, the temperature sensor, or any combination thereof, configured to determine sensor data associated with the patient at least one of before, during, and after a fluid infusion associated with the patient, and the wireless communication device configured to wirelessly transmit the sensor data to an external device.

[0067] Using at least one processor, a method comprising: acquiring, using at least one processor, patient data associated with a patient; determining, using the at least one processor, an initial risk prediction for the patient associated with a fluid infusion to be administered to the patient based on the patient data, the initial risk prediction comprising a probability that the patient will experience at least one adverse event in response to the fluid infusion; providing, using the at least one processor, the initial risk prediction to a user device before the fluid infusion is administered to the patient; determining, using at least one sensor, sensor data associated with the patient after the fluid infusion has begun; determining, using the at least one processor, a current risk prediction for the patient associated with the fluid infusion based on the sensor data determined after the fluid infusion has begun, the current risk prediction comprising a probability that the patient will experience the at least one adverse event in response to the fluid infusion; and providing, using the at least one processor, the current risk prediction to the user device.

[0068] Appendix 58. The method of Appendix 57, wherein said patient data includes at least one of the following parameters associated with said patient: age, sex, weight, previous chemotherapy status, estimated glomerular filtration rate (eGFR), thyroid-stimulating hormone (TSH) level, triiodothyronine (FT3) thyroxine (FT4) ratio (FT3 / FT4), level of environmental influence, prior response to previous fluid infusion status, atopic disease status, medical status associated with at least one of diabetes and hypertension, congestive heart failure status, hematocrit level, renal failure status, malignancy status, implanted device for central venous access status, type of medication, type of fluid medium administered in said fluid infusion, type of said fluid infusion, type of imaging test, flow rate associated with said fluid infusion, catheter gauge associated with said fluid infusion, total volume of fluid associated with said fluid infusion, pressure curve associated with said fluid infusion, infusion site location associated with said fluid infusion, or any combination thereof.

[0069] 59. The method of claims 57 and 58, wherein the at least one adverse event comprises at least one of the following adverse events: extravasation, post-contrast acute kidney injury, an acute adverse event, contrast-induced nephrotoxicity, thyrotoxicosis, or any combination thereof.

[0070] Addendum 60. The method of any one of Addendums 57-59, wherein the initial risk prediction further includes at least one of a prompt to administer medication to the patient before the fluid injection, a prompt to adjust an injection protocol for the fluid injection, a prompt to adjust an imaging protocol for an imaging scan, a prompt to prepare the patient before the fluid injection, a prompt to observe and / or follow up with the patient after the fluid injection, or any combination thereof.

[0071] Addendum 61. The method of any one of Addendums 57-60, wherein the sensor data includes at least one of the following parameters associated with the patient: heart rate, sound or vibration, temperature, oxygen saturation, ECG, body fat / water ratio, tissue impedance, vascularity level, vascular diameter, hydration level, hematocrit level, skin resistivity, blood pressure, muscle tension level, light absorptance level, exercise level, arm position, arm circumference, respiratory rate, radiation absorption, EMG, skin color, surface vasodilation, bioimpedance, light absorptance, hemoglobin level, inflammation level, ambient temperature of an environment surrounding the patient, air pressure of an environment surrounding the patient, ambient light level, ambient sound level, or any combination thereof.

[0072] Addendum 62. The method of any one of Addendums 57 to 61, further comprising: using the at least one sensor to determine sensor data during a test injection administered to the patient prior to the fluid injection; using the at least one processor to determine a test prediction based on the sensor data determined during the test injection, the test prediction comprising a probability that the patient will experience extravasation in response to the fluid injection; and using the at least one processor to provide the test prediction to the user device.

[0073] Addendum 63. The method of any one of Addendums 57 to 62, wherein the at least one sensor includes three sound or vibration sensors positioned at three different locations on the patient's limb proximate an injection site for the test injection, the method further comprising: using the at least one processor to combine, through triangulation, the data streams of sensor data from each of the three sound or vibration sensors to create a combined data stream; and using the at least one processor to determine the test prediction based on the combined data stream.

[0074] Addendum 64. The method of any one of Addendums 57 to 63, further comprising determining the sensor data prior to the test injection using the at least one sensor, wherein determining the initial risk prediction is further based on the sensor data determined prior to the test injection.

[0075] Addendum 65. The method of any one of Addendums 57 to 64, further comprising: using the at least one sensor to determine sensor data during the fluid injection; using the at least one processor to determine the current risk prediction for the patient associated with the fluid injection based on the sensor data determined during the fluid injection; and providing the current risk prediction to the user device during the fluid injection.

[0076] Addendum 66. The method of any one of Addendums 57 to 65, further comprising: using the at least one sensor to determine sensor data after the fluid injection; using the at least one processor to determine the current risk prediction for the patient associated with the fluid injection based on the sensor data determined after the fluid injection; and providing the current risk prediction to the user device after the fluid injection.

[0077] Addendum 67. The method of any one of Addendums 57 to 66, wherein the at least one adverse event includes extravasation, and wherein providing the current risk prediction further includes, in response to determining that the patient is experiencing the extravasation, automatically controlling, using the at least one processor, a fluid injection system to stop the fluid injection.

[0078] Addendum 68. The method of any one of Addendums 57 to 67, wherein the at least one sensor comprises at least one of the following sensors: an image capture device; an accelerometer; a strain gauge; a global positioning system (GPS); a skin resistivity or conductance sensor; a heart rate monitor; a microphone; a thermal or temperature sensor; a pulse oximeter; a hydration sensor; a dosimeter; an ultrasonic sensor; an acoustic sensor; one or more electrodes configured to measure at least one of tissue impedance, an electromyogram (EMG), and an electrocardiogram (ECG); a microwave sensor; a mechanical impedance sensor; a chemical sensor; a force or pressure sensor; or any combination thereof.

[0079] Addendum 69. The method of any one of Addendums 57 to 68, wherein the at least one sensor is included in a sensor device, the sensor device including an elongated housing extending between a first end and a second end, the elongated housing configured to surround a patient's limb, the elongated housing including a flexible exterior, an interior of the elongated housing including the at least one sensor and a wireless communication device, the method further including the step of wirelessly transmitting the sensor data to an external device using the wireless communication device.

[0080] Addendum 70. The method of any one of Addendums 57 to 69, wherein the interior of the elongated housing containing the at least one sensor and the wireless communication device is fluidly sealed from the external environment by the flexible exterior of the elongated housing.

[0081] Addendum 71. The method of any one of Addendums 57 to 70, wherein the at least one sensor comprises a plurality of sensors spaced apart from one another along a length of the elongated housing extending from the first end of the elongated housing to the second end of the elongated housing, whereby the plurality of sensors are oriented in a circumferential pattern around the patient's limb when the elongated housing encircles the patient's limb, and the method further comprises using the plurality of sensors to determine the sensor data in a cross-section of the patient's limb.

[0082] Addendum 72. The method of any one of Addendums 57-71, wherein the at least one adverse event includes an extravasation, the at least one sensor includes an image capture device, and the method further includes determining, using the image capture device, the sensor data, wherein the sensor data determined by the image capture device is associated with a plurality of images of the patient over a period of time; and determining, using the at least one processor, the current risk prediction, including the probability that the patient will experience the extravasation, based on the plurality of images of the patient over the period of time.

[0083] Addendum 73. The method of any one of Addendums 57-72, further comprising at least one of the following steps: using the at least one processor, processing the plurality of images of the patient over the period of time to highlight changes in at least one of color and motion between the plurality of images; using the at least one processor, displaying the plurality of images including the highlighted changes to a user on a display; and using the at least one processor, determining the current risk prediction including the probability that the patient will experience the extravasation based on the highlighted changes; and in response to determining that the current risk prediction including the probability that the patient will experience the extravasation meets a threshold probability, using the at least one processor, automatically controlling a fluid injection system to stop the fluid injection.

[0084] Addendum 74. The method of any one of Addendums 57-73, wherein the image capture device includes an infrared (IR) camera, the method further including at least one of: processing the plurality of images with the at least one processor to determine a difference in absorption spectra between a first location on the patient and a second location on the patient in the plurality of images; displaying the difference in absorption spectra between the first location on the patient and the second location on the patient to a user on a display with the at least one processor; and determining the current risk prediction including the probability that the patient will experience the extravasation based on the difference in absorption spectra between the first location on the patient and the second location on the patient, and automatically controlling a fluid injection system to stop the fluid injection in response to determining that the current risk prediction including the probability that the patient will experience the extravasation satisfies at least one threshold probability.

[0085] Addendum 75. The method of any one of Addendums 57 to 74, wherein the first location on the patient includes a blood vessel of the patient and the second location on the patient includes tissue of the patient surrounding the blood vessel of the patient.

[0086] Clause 76. The method of any one of clauses 57 to 75, further comprising, during the fluid injection, inducing a sound signal in fluid delivered to the patient during the fluid injection using a sound-generating device.

[0087] Addendum 77. The method of any one of Addendums 57 to 76, wherein the sound-generating device includes an oscillator connected to at least one of a syringe and a tube that delivers the fluid to the patient during the fluid injection.

[0088] Addendum 78. The method of any one of Addendums 57 to 77, wherein at least one of the frequency and amplitude of the sound signal is adjusted to improve detection by the at least one sensor.

[0089] Addendum 79. The method of any one of Addendums 57-78, wherein the at least one sensor includes a sound or vibration sensor, the method further including: using the sound or vibration sensor to measure at least one of a frequency and an amplitude of sound or vibration of the patient; using the at least one processor to determine the current risk prediction including the probability that the patient will experience an extravasation based on the at least one of the frequency and the amplitude of the measured sound or vibration of the patient; and in response to determining that the current risk prediction including the probability that the patient will experience the extravasation satisfies at least one threshold probability, using the at least one processor to automatically control a fluid injection system to stop the fluid injection.

[0090] Appendix 80. The method of any one of Appendixes 57-79, further comprising: using the at least one processor to determine a patient's level of distress based on the sensor data determined after the fluid injection has begun; using the at least one processor to compare the patient's level of distress to at least one threshold level; and using the at least one processor to provide an alert to a user device in response to determining that the patient's level of distress meets the at least one threshold level; and using the at least one processor to automatically control at least one of: (i) a fluid injection system to stop the fluid injection; and (ii) an imaging system to adjust timing of imaging operations.

[0091] Addendum 81. The method of any one of Addendums 57 to 80, wherein the step of determining the patient's pain level includes determining a change in one or more parameters of the sensor data over a period of time, and comparing the change in the one or more parameters to at least one threshold change.

[0092] Addendum 82. The method of any one of Addendums 57 to 81, wherein the sensor data includes at least one of the following parameters associated with the patient: heart rate, oxygen saturation, skin resistivity, skin color, exercise level, temperature proximate to the injection site, or any combination thereof.

[0093] Addendum 83. The method of any one of Addendums 57 to 82, wherein the at least one sensor includes at least one of a pulse oximeter, a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof.

[0094] Addendum 84. The method of any one of Addendums 57 to 83, wherein the at least one sensor is included in a sensor device, the sensor device including a glove-shaped housing configured to be worn on the patient's hand, the housing including the at least one sensor and a wireless communication device, and the method further includes wirelessly transmitting the sensor data to an external device using the wireless communication device.

[0095] Addendum 85. The method of any one of Addendums 57-84, wherein the at least one sensor is included in a sensor device, the sensor device including an elongated housing extending between a first end and a second end, and a pulse oximeter connected to the elongated housing via a wire, the elongated housing configured to encircle at least one of a patient's hand and wrist, the elongated housing including a wireless communication device and at least one of a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof, the method further including determining the sensor data using the pulse oximeter and the at least one of the skin resistance sensor, the accelerometer, the temperature sensor, or any combination thereof, and wirelessly transmitting the sensor data to an external device using the wireless communication device.

[0096] Addendum 86. The method of any one of Addendums 57 to 85, further comprising using the at least one processor to control at least one of a light, a display, a speaker, and a tactile device to provide at least one of visual, audio, and tactile instructions for guiding the patient's breathing and / or positioning.

[0097] Addendum 87. The method of any one of Addendums 57 to 86, further comprising using the at least one processor to adjust the at least one of the visual instructions, the audio instructions, and the tactile instructions for guiding the breathing and / or positioning of the patient based on timing of imaging operations of an imaging system.

[0098] Addendum 88. The method of any one of Addendums 57 to 87, further comprising: using the at least one processor to determine a level of patient distress based on the sensor data determined after the fluid injection has begun; and using the at least one processor to adjust the at least one of the visual, audio, and tactile instructions for guiding the breathing and / or the positioning of the patient in response to determining that the patient is in distress.

[0099] Addendum 89. The method of any one of Addendums 57 to 88, further comprising using the at least one processor to automatically control, based on the current risk prediction, at least one of: (i) a fluid injection system to stop the fluid injection; and (ii) an imaging system to adjust the timing of imaging operations.

[0100] Further advantages and details are explained in more detail below with reference to exemplary embodiments shown in the accompanying schematic drawings. [Brief explanation of the drawings]

[0101] [Figure 1A] FIG. 1 is a diagram of a non-limiting embodiment or aspect of an environment in which the systems, devices, products, apparatus, and / or methods described herein may be implemented. [Figure 1B] FIG. 1B is a diagram of a non-limiting embodiment or aspect of an implementation of the environment of FIG. 1A. [Figure 2] FIG. 1C is a diagram of a non-limiting embodiment or aspect of components of one or more devices and / or one or more systems of FIGS. 1A and 1B. [Figure 3] 1 is a flowchart of a non-limiting embodiment or aspect of a process for protecting patient health for fluid infusion. [Figure 4A] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 4B] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 4C] 1A-1C are diagrams of non-limiting embodiments or aspects of components of a contact sensor device. [Figure 4D] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device attached to a patient's extremity. [Figure 4E] FIG. 10 is a cross-sectional view of a non-limiting embodiment or aspect of a contact sensor device attached to a patient's extremity. [Figure 5A] 1 is a flowchart of a non-limiting embodiment or aspect of a process for protecting patient health for fluid infusion. [Figure 5B] 1 is a flowchart of a non-limiting embodiment or aspect of a process for protecting patient health for fluid infusion. [Figure 6] 1 is a flowchart of a non-limiting embodiment or aspect of a process for protecting patient health for fluid infusion. [Figure 7] 1 is a perspective view of a non-limiting embodiment or aspect of an implementation of a fluid injector including a sound generating device. [Figure 8A] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 8B] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 8C] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 8D] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 8E] FIG. 1 is a perspective view of a non-limiting embodiment or aspect of a contact sensor device. [Figure 9] 1 is a flowchart of a non-limiting embodiment or aspect of a process for protecting patient health for fluid infusion. [Figure 10] 1 is a flowchart of a non-limiting embodiment or aspect of a process for protecting patient health for fluid infusion. [Figure 11] 1 illustrates a non-limiting embodiment or aspect of a visual instruction for guiding a patient's breathing. [Figure 12] FIG. 1 is a diagram of a non-limiting embodiment or aspect of data processing to protect patient health for fluid infusion. [Figure 13A] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. [Figure 13B] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. [Figure 14A] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. [Figure 14B] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. [Figure 14C] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. [Figure 15A] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. [Figure 15B] 1 illustrates non-limiting embodiments or aspects of a patient portal application accessible via a user device. DETAILED DESCRIPTION OF THE INVENTION

[0102] It is to be understood that the present disclosure may contemplate various alternative modifications and step sequences unless expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the accompanying drawings, and described in the following specification, are merely exemplary and non-limiting embodiments or aspects. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered limiting.

[0103] Similarly, it should also be understood that contrast agent injections are merely exemplary of drugs or pharmaceuticals that may be injected intravascularly, and that such injections may benefit from the use of non-limiting embodiments or aspects of the present disclosure. In addition to or in place of contrast agents, exemplary intravascular injectates may include any imaging agent, saline solution, any flushing fluid, stress agents, chemotherapeutic agents, radiotherapeutic agents, antispasmodic or antispasmodic agents, thrombolytic agents, antithrombotic agents, antibiotics, intravenous immunoglobulin (IVIG), parenteral nutrition, analgesics, and / or radiopharmaceuticals. Similarly, the use of the devices, systems, and processes of the present disclosure is not limited to imaging suites but may be useful anywhere intravascular injections occur, including, for example, other medical facilities, the patient's home, etc.

[0104] For purposes of the following description, the terms "end," "upper," "lower," "right," "left," "vertical," "horizontal," "top," "bottom," "lateral," "longitudinal," and derivatives thereof, shall refer to the embodiment or aspect as oriented in the drawings. However, it should be understood that an embodiment or aspect may assume various alternative variations and step sequences, unless expressly specified to the contrary. It should also be understood that the specific devices and processes illustrated in the accompanying drawings and described in the following specification are merely non-limiting exemplary embodiments or aspects. Accordingly, specific dimensions and other physical characteristics associated with an embodiment or aspect disclosed herein should not be considered limiting, unless otherwise indicated.

[0105] No aspect, component, element, structure, operation, step, function, instruction, etc. used herein should be construed as critical or essential unless expressly stated as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more" and "at least one." Furthermore, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.) and may be used interchangeably with "one or more" or "at least one." Where only one item is intended, the term "one" or similar language is used. Also, as used herein, terms such as "has," "have," and "having" are intended to be open-ended terms. Furthermore, the phrase "based on" is intended to mean "based at least in part on," unless otherwise specified.

[0106] As used herein, the terms “communication” and “communicating” may refer to receiving, accepting, transmitting, forwarding, providing, etc., information (e.g., data, signals, messages, instructions, commands, etc.). One unit (e.g., a device, a system, a component of a device or system, a combination thereof, etc.) communicating with another unit means that the one unit is able to directly or indirectly receive information from the other unit and / or transmit information to the other unit. This may refer to a direct or indirect connection that is wired and / or wireless in nature. In addition, two units can communicate with each other even if the transmitted information may be modified, processed, relayed, and / or routed between the first and second units. For example, a first unit can communicate with a second unit even if the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit can communicate with a second unit if at least one intermediate unit (e.g., a third unit informationally located between the first unit and the second unit) processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments or aspects, a message may refer to a network packet (e.g., a data packet, etc.) containing data. It will be understood that numerous other configurations are possible. Communication between the first unit and the second unit may occur via any medium or intermediary, including, for example, humans communicating information manually or orally.

[0107] As used herein, the term "computing device" may refer to one or more electronic devices configured to communicate with or communicate directly or indirectly through one or more networks. A computing device may be a mobile or portable computing device, a desktop computer, a server, etc. Furthermore, the term "computer" may refer to any computing device that includes the components necessary to receive, process, and output data, typically including a display, a processor, memory, input devices, and a network interface. A "computing system" may include one or more computing devices or computers. An "application" or "application program interface" (API) refers to computer code or other data organized on a computer-readable medium that can be executed by a processor to facilitate interaction between software components, such as a client-side front-end for receiving data from a client and / or a server-side back-end. An "interface" refers to a generated display, such as one or more graphical user interfaces (GUIs), with which a user may interact directly or indirectly (e.g., through a keyboard, mouse, touchscreen, etc.). Furthermore, multiple computers, e.g., servers, or other computerized devices communicating directly or indirectly in a network environment may constitute a "system" or a "computing system."

[0108] As used herein, terms such as user, physician, healthcare professional, and / or caregiver may include any person associated with the devices, systems, and processes of the present disclosure and / or anyone assisting in the care of a patient, including the patient themselves or the patient's guardian or representative. For example, these terms are intended to include persons such as doctors, attending physicians, radiologists, nurses, technicians, radiologists, oncologists, radiology technicians, social service workers, assistants, volunteers, family members, etc. User may also include employees in healthcare delivery or payment systems, such as hospital or radiology administrators, office staff, regulatory agencies, insurance or payment company employees, and others who may own, manage, and / or provide information used by non-limiting embodiments or aspects, or who may benefit from information provided by the system.

[0109] It will be apparent that the systems and / or methods described herein may be implemented in different forms, such as hardware, software, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0110] Some non-limiting embodiments or aspects are described herein in relation to thresholds. As used herein, meeting a threshold can refer to a value being greater than the threshold, more than the threshold, higher than the threshold, equal to or greater than the threshold, less than the threshold, lower than the threshold, less than the threshold, equal to or less than the threshold, etc. Unless otherwise specified, thresholds are exemplary and may depend or vary based, for example, on the patient population involved.

[0111] 1A, which is a diagram of an exemplary environment 100 in which the systems, devices, articles, apparatuses, and / or methods described herein may be implemented. As shown in FIG. 1A, the environment 100 includes a fluid injector system 102, an imaging system 104, a sensor system 106, a user device 108, a management system 110, an auxiliary system 112, and / or a communication network 114. Referring also to FIG. 1B, which is a diagram of a non-limiting embodiment or aspect of an implementation 150 of the environment 100 of FIG. 1A. 1B , implementation 150 may include injector 152, injector control computing system 154, injector user interface 156, imager 158, imager control computing system 160, imager user interface 162, one or more contact sensors 164a, one or more non-contact sensors 164b, control computing system 166, one or more hospital information systems 168, cloud computing and off-site resources 170, and / or administrative user interface 172. The systems and / or devices of environment 100 and / or implementation 150 may interconnect (e.g., communicate information and / or data, etc.) via wired connections, wireless connections, or a combination of wired and wireless connections (e.g., via communications network 114, etc.).

[0112] The fluid injection system 102 may include one or more devices, software, and / or hardware configured to set one or more injection protocols and deliver one or more fluids (e.g., contrast media, etc.) to a patient according to the one or more injection protocols. An injection protocol generally includes one or more phases, each of which defines the fluid to be injected and optionally the fluid concentration, as well as two of the flow rate, volume, and duration of that phase of the injection (e.g., injected volume = flow rate × duration, so there are only two independent variables from those three parameters). Other injection parameters may be different for different phases or constant for all phases and may include at least one of a pressure limit, a flow limit, an occlusion indication, or any combination thereof. Some injectors may be configured to have time-varying values ​​for one, some, or all of the injection parameters. For example, the fluid injection system 102 may include an injector 152, an injector control computing system 154, and / or an injector user interface 156. By way of example, the fluid injection system 102 may include a contrast injection system such as those described in U.S. Patent Nos. 6,643,537 and / or 7,937,134, and / or those described in published International Application No. WO2019046299A1, the entire contents of each of which are incorporated herein by reference. By way of example, the fluid injection system 102 may include a MEDRAD® Stellant FLEX CT Injection System, a MEDRAD® MRXperion MR Injection System, a MEDRAD® Mark 7 Arterion Injection System, a MEDRAD® Intego PET Infusion System, a MEDRAD® Spectris Solaris EP MR Injection System, a MEDRAD® Stellant CT Injection System With Certegra® Workstation, etc.

[0113] The imaging system 104 may include one or more devices, software, and / or hardware configured to set imaging protocols and acquire non-contrast and contrast-enhanced scans of a patient. For example, the imaging system 104 may include an imager 158, an imager control computing system 160, and / or an imager user interface 162. By way of example, the imaging system 104 may include a magnetic resonance imaging (MRI) system, a computed tomography (CT) system, an ultrasound system, a single-photon emission computed tomography (SPECT) system, a positron emission tomography-magnetic resonance (PET / MRI) system, a positron emission tomography-computed tomography (PET / CT) system, an angiography system, an interventional radiology (IR) system, and / or other imaging modalities used on humans or animals. By way of example, the imaging system 104 may include an imaging system such as that described in U.S. Patent Application Publication No. 2020 / 0146647 A1, filed December 11, 2019, the entire contents of which are incorporated herein by reference. In some non-limiting embodiments or aspects, the imaging system 104 may include Siemens Healthineers' Somatom Go CT Systems, General Electric's Signa MR Systems, and the like.

[0114] The sensor system 106 may include one or more sensors 164 configured to determine (e.g., determine, collect, acquire, capture, measure, sense, etc.) sensor data related to the patient and / or fluid injection (e.g., contrast injection, etc.) for the patient. For example, the sensor system 106 may include one or more contact sensors 164a (e.g., sensors that contact the patient to determine sensor data, sensors included in contact sensor devices 400 and / or 800 wearable by the patient, etc.) and / or non-contact sensors 164b (e.g., sensor devices that do not contact the patient to determine sensor data, etc.).

[0115] The contact sensor 164a may include at least one of the following sensors: an accelerometer; a strain gauge; a global positioning system (GPS); a skin resistivity or conductance sensor; a heart rate monitor; a microphone (e.g., a microphone configured to measure sounds within the patient's tissue, such as the sound of contrast agent, saline, or other drugs influx within the blood vessels); a thermal or temperature sensor (e.g., a temperature sensor configured to measure changes in tissue temperature due to injected saline and / or contrast fluid, etc.); a pulse oximeter (e.g., a pulse oximeter configured to measure pulse rate, changes in oxygenation levels, patient hydration, and / or local tissue perfusion, etc.); a hydration sensor; a dosimeter; an epiwatch; an ultrasonic sensor; an acoustic sensor (e.g., a sonic acoustic sensor, an infrasonic acoustic sensor, etc.); one or more electrodes configured to measure tissue impedance and perform an electromyogram (EMG) and / or an electrocardiogram (ECG or EKG); a respirometry sensor; a microwave sensor; a mechanical impedance sensor; a chemical sensor; a force or pressure sensor; or any combination thereof. In some non-limiting embodiments or aspects, contact sensor 164a may be included in contact sensor device 400 and / or contact sensor device 800 as described herein. In some non-limiting embodiments or aspects, contact sensor 164a may be included in at least one of the following locations: a catheter (e.g., the tip of the catheter), a patient's arm over the tip of the catheter, a patient's arm proximate an infusion site or the tip of the catheter, a connector tube upstream of the catheter, another part of the patient's body, an area surrounding an infusion site, or any combination thereof. In some non-limiting embodiments or aspects, contact sensor 164a may include a single device including a single sensor, a single device including multiple sensors, and / or multiple devices including either a single sensor or multiple sensors. Existing devices including existing sensors may be incorporated into non-limiting embodiments or aspects of sensor system 106 and / or may provide measurements to sensor system 106.Exemplary existing devices may include an Apple Watch that the patient may wear, a Fitbit exercise monitor, etc., an ECG or respiratory monitor that may be part of the imaging system 104, a pulse oximeter, or other monitoring equipment that may already be available and / or in use in the imaging room and / or medical facility.

[0116] The non-contact sensor 164b may include one or more image capture devices configured to capture multiple images of the patient over a period of time (e.g., images of the injection site and / or the area surrounding the injection site, etc.), such as a camera (e.g., a visible light camera, an infrared (IR) camera, etc.), a LiDAR sensor, or any combination thereof. The IR camera may include at least one of the following IR cameras: a near-IR camera (e.g., silicon sensing, etc.) configured to capture light having near-IR wavelengths, a short-wavelength IR camera configured as a spectral imager to capture light having short-IR wavelengths, a mid-wavelength IR camera configured to capture light having mid-IR wavelengths, a long-wavelength IR camera configured to capture light having long-IR wavelengths, or any combination thereof.

[0117] In some non-limiting embodiments or aspects, the non-contact sensor 164b may include an image capture device configured to capture images using ambient illumination. In some non-limiting embodiments or aspects, the non-contact sensor 164b may include one or more illumination devices configured to provide at least one of the following types of illumination for the image capture device for use by the image capture device in capturing images: additional ambient illumination, localized additional illumination (e.g., at the injection site), through-tissue illumination, projection pattern or grid, cross projection, or any combination thereof. For example, the non-contact sensor 164b may include a camera such as described in International Patent Application No. PCT / US2020 / 061733, filed November 23, 2020, the contents of which are incorporated herein by reference in their entirety. The non-contact sensor 164b may carry two or more cameras to provide binocular or 3D vision, which may enable 3D determination of phenomena such as swelling, gross movement in 3D, or vibration or small movement in 3D.

[0118] In some non-limiting embodiments or aspects, the non-contact sensor 164b can be mounted on the imaging device 158, the injector 152, the patient's bed, a pedestal pole, an adjustable overhead counterpoise, a ceiling, etc. In some non-limiting embodiments or aspects, the non-contact sensor 164b can be held by the patient during fluid injection (e.g., contrast injection, etc.) and / or imaging examination. In some non-limiting embodiments or aspects, the non-contact sensor 164b can be remotely controlled by a user (e.g., via the user device 108, etc.) to pan and zoom to a desired field of view. In some non-limiting embodiments or aspects, the sensor system 106 can control the non-contact sensor 164b using one or more object tracking technologies to automatically track the patient's extremities (e.g., arms, legs, hands, feet, etc.), including the injection site.

[0119] In some non-limiting embodiments or aspects, fluid injection system 102, imaging system 104, user device 108, and / or auxiliary system 112 may include one or more additional sensors (e.g., contact sensor 164a, non-contact sensor 164b, etc.) configured to determine sensor data related to the patient and / or fluid injection (e.g., contrast injection, etc.) for the patient, and / or to store and / or provide sensor data determined by one or more additional sensors configured to determine sensor data related to the patient and / or fluid injection for the patient. Exemplary sensors may include a respiratory band and / or ECG electrodes to enable injection and / or image acquisition in relation to the patient's respiration and / or heartbeat, respectively.

[0120] 4A and 4B, which are perspective views of a non-limiting embodiment or aspect of a contact sensor device 400. The sensor system 106 can include the contact sensor device 400, and / or the contact sensor device 400 may include at least one contact sensor 402 of the contact sensors 164a of the sensor system 106 configured to determine sensor data related to a patient and / or a fluid infusion for the patient. Referring also to FIG. 4C, the contact sensor device 400 may include a housing 404 that houses the contact sensor 402, a communication device 406, a processor 408, a user input / feedback device 410, and / or a battery 412. The housing 404 can provide a watertight seal between the interior of the housing 404, including the contact sensor 402, the communication device 406, the processor 408, the user input / feedback device 410, and / or the battery 412, and the exterior of the housing 404, so that the contact sensor 402, the communication device 406, the processor 408, the user input / feedback device 410, and / or the battery 412, and / or their electronic components are sealed from the external environment surrounding the contact sensor device 400 and / or so that the contact sensor device 400 can be easily sanitized and qualified for multi-patient use. The housing 404 can have an elongated shape extending between a first end 405 a and a second end 405 b. For example, as shown in FIG. 4D , the housing 404 may include a patient-wearable bracelet configuration configured to be attached proximate an injection site on the patient's extremity (e.g., arm, leg, etc.) to measure sensor data related to patient data before, during, and / or after a fluid injection and / or examination (e.g., an MRI examination, a CT examination, etc.).As an example, the housing 404 may include a flexible exterior or frame (e.g., an antimicrobial silicone exterior or frame, etc.) configured to flex or curve to surround the patient's limb, house the contact sensor 402, communication device 406, processor 408, user input / feedback device 410, and / or battery 412 and / or its electronic components therein, and fluidly seal the internal components of the contact sensor device 400 from the external environment. As an example, the housing 404 may include one or more elastic members (e.g., metal, plastic, or foam) that urge the one or more sensors against the skin with an appropriate force or pressure. The elastic members may also urge the housing into a shape that assists in gripping the patient's limb.

[0121] 4D , contact sensor device 400 may include a removable strip or sheath 450 configured to surround, cover, or separate housing 404 from skin contact to protect contact sensor device 400 from cross-contamination (e.g., from patient to patient, via a technician's hand, etc.). In some non-limiting embodiments or aspects, contact sensor device 400 may be a disposable or single-use device including printed sensor 402 and printed sensor pad or housing 404 that can be adhered (e.g., via an adhesive layer, etc.) to a patient's skin adjacent an infusion site.

[0122] In some non-limiting embodiments or aspects, the housing 404 can be configured to immobilize a patient's limb (e.g., arm, etc.) by preventing or restricting the patient from bending the limb, thereby constricting the vein and / or catheter or removing the catheter from the vein. For example, the housing 404 may be configured as an elbow brace or exoskeleton. As an example, the housing 404 may also immobilize an infusion site to facilitate observation of the infusion site by one or more non-contact sensors 164b.

[0123] In some non-limiting embodiments or aspects, the housing 404 may include removable and / or disposable attachment means, such as a flexible patch, a fabric strip, an adhesive connector, a mechanical latch, a blood pressure cuff, a hook-and-loop fastener such as a Velcro®-type attachment, and / or a suction cup. For example, the contact sensor device 400 may be configured to attach to a dressing such as a BD Tegaderm™ Transparent Film Dressing via physical alignment markings, to another device (e.g., the injector 152, the imaging device 158, a disposable dressing, etc.), and / or to a patient. By way of example, the housing 404 may have a cylindrical or hockey puck shape with an adhesive connected to attach the housing 404 to the patient. In some non-limiting embodiments or aspects, the housing 404 may include a transparent disposable band that allows a user to visually inspect the patient's skin adjacent the injection site. Depending on the shape or shapes of the various segments of the housing 404, attachment mechanisms configured to place the housing 404 and / or one or more contact sensors 164a in proper contact with the patient may include double-sided adhesive tape that conforms to the skin, a disposable strap or band, a strap with a disposable isolation patch or element (which may be particularly useful for patients with a significant amount of hair on their arms), a wrap that is inflated to a desired non-occlusive pressure similar to a blood pressure cuff, an elastic force such as a "slap" bracelet, an elastomeric band or bracelet that may optionally be disposable and / or transparent to allow visual inspection of the skin near the injection site, and / or attachment to a Tegaderm™ or similar existing device on the patient's arm via physical markings on the existing device. Additionally or alternatively, the housing 404 and / or one or more contact sensors 164a may not be mechanically attached to the patient, but may be held in contact with the patient by having the patient lie on the housing 404 and / or one or more contact sensors 164a or by placing the patient's arm on the housing 404 and / or one or more contact sensors 164a.The housing 404 and / or the one or more contact sensors 164a may also be placed loosely on the patient, in which case gravity and / or the patient's efforts may hold the housing 404 and / or the one or more contact sensors 164a in contact with the patient.

[0124] Because the non-contact sensor 164b does not need to contact the patient, sterility and / or cross-contamination concerns for the non-contact sensor 164b are relatively low. These include: a disposable mounting barrier; cleaning of the contact aspects of the contact sensor device 400 and / or the contact sensor 164a (e.g., electrodes, housing 404, etc.) with disinfectant wipes or sprays; a "home base" or mount for holding and optionally storing and / or charging the contact sensor device 400 and / or the contact sensor 164a between patients, which may also include a sterilizing device, e.g., a UV lamp, ozone treatment, or disinfectant wipe station; inclusion of a self-sterilizing surface, e.g., a silver nanoparticle surface or film; a sheath into which the contact sensor device 400 and / or the contact sensor 164a can slip prior to use; and a protective layer for contacting the patient's skin. One or more of the following approaches can provide sufficient sterility and / or cross-contamination prevention for the contact sensor device 400 and / or contact sensor 164a: an intervening disposable barrier layer disposed between the sensor device 400 and / or contact sensor 164a; and / or some or all of the contact sensor device 400 and / or contact sensor 164a can be sufficiently low cost that at least one section or portion thereof can be used once for the patient and then given to the patient as a “freebie” for subsequent medical or home / personal use or can be discarded.

[0125] Providing at least one segment or portion of contact sensor device 400 and / or contact sensor 164a can be an activity or action for good “marketing” and patient satisfaction. Applications described herein related to patient information, education, e-consent, and similar functions can be configured to interface with a Freebie segment or portion, enabling the segment or portion to be, for example, a personal pulse oximeter and / or a skin contact thermometer.

[0126] The communication device 406 may include wired and / or wireless communication devices configured to communicate patient-related sensor data to external devices and / or systems (e.g., the fluid injection system 102, the imaging system 104, the sensor system 106, the user device 108, the management system 110, the auxiliary system 112, etc.).

[0127] The processor 408 may be programmed and / or configured to control one or more operations of the contact sensor 402 and / or to determine sensor data related to the patient. In some non-limiting embodiments or aspects, the processor 408 may include a low-power microcontroller unit (MCU).

[0128] The user input / feedback device 410 may be configured to receive user input from a user and / or provide feedback to a user. For example, the user input / feedback device 410 may include at least one of a display, a light-emitting diode (LED), an audio output device (e.g., a buzzer, a speaker, a headset, etc.), a tactile output device (e.g., a vibrator, etc.), or any combination thereof. As an example, a user may establish communication (e.g., pairing, etc.) between the contact sensor device 400 and an external device and / or system via the user input / feedback device 410 and / or provide prompts and / or instructions to the patient via the user input / feedback device 410 that may be received from an external device and / or system. In some non-limiting embodiments or aspects, the user input / feedback device 410 can function as a patient call button configured to automatically call a user outside the scan room in response to being activated.

[0129] The user input / feedback device 410 may be divided among various hardware. For example, some input and / or output features or functions may be implemented on the contact sensor device 400. Some of the same and / or other functions may be accessible through a separate, dedicated, special-purpose user input / feedback device 410. Some of the same and / or other functions may be accessible through a general-purpose or multi-purpose user input / feedback device 410, e.g., an iPhone®. Some of the same and / or other functions may be accessible through the user interface of other equipment associated with the test or procedure being performed, e.g., the injector interface 156 and / or the imager user interface 162. The battery 412 may include a rechargeable battery (e.g., a battery rechargeable via inductive charging technology), a single-use battery, a replaceable battery, an external battery and / or a wired connection to a power source, or any combination thereof. The battery 412 may provide power to operate the components of the contact sensor device 400.

[0130] 4A-4D, and also with reference to FIG. 4E, the contact sensors 402 may be oriented in a pattern, for example, circumferentially around the patient's limb, such that when the contact sensor device 400 is attached to the patient's limb, the contact sensors 402 can measure sensor data, including tissue parameters, in a cross-section of the patient's limb. For example, the contact sensors 402 may be spaced apart from one another along the length of the housing 404, which extends from the first end 405a to the second end 405b. Sensing modes of the contact sensors 402 may include a transmission mode, a reflection mode, an absorption mode, a listen mode, or a passive measurement mode. Different modes may be used for different sensors. Multiple and / or hybrid modes may be used depending on ambient and / or patient conditions. Phase-gated sensing (e.g., a phase-locked loop (PLL)), synchronous sensing, and / or other existing sensing means may be used for noise / interference reduction and / or ambient signal cancellation. The sensed signal may be a narrow segment of a possible spectrum and / or a wide segment of a possible spectrum to which subsequent processing may be applied.

[0131] 8A-8E, which are perspective views of non-limiting embodiments or aspects of contact sensor device 800. Sensor system 106 may include contact sensor device 800, and / or contact sensor device 800 may include at least one contact sensor (e.g., 804, 808, 810, etc.) of one or more contact sensors 164a of sensor system 106 configured to determine sensor data related to a patient and / or a fluid injection (e.g., contrast injection, etc.) for the patient. In some non-limiting embodiments or aspects, sensor system 106 may include one of contact sensor device 400 and contact sensor device 800, contact sensor device 400 and contact sensor device 800, or neither contact sensor device 400 nor contact sensor device 800. Additionally, contact sensor device 400 may be implemented within contact sensor device 800 (or vice versa), and / or contact sensor device 400 may perform one or more functions as described as being performed by contact sensor device 800 (or vice versa).

[0132] The contact sensor device 800 may include a housing 802 and a finger sensor 804 (e.g., a pulse oximeter, etc.). The finger sensor 804 may be connected to the housing 802 via wires 806. The housing 802 may include electronic components 808, a conductive probe or electrode 810, and / or a disposable adhesive protector 812. The electronic components 808 may include the contact sensor 164a, a processor, memory, wired and / or wireless communication devices, user input / feedback devices, and / or a battery. For example, the electronic components 808 of the contact sensor device 800 may be the same as or similar to the components of the contact sensor device 400 described herein with respect to FIG. 4C .

[0133] Housing 802 may include a soft molded strap (e.g., a plastic strap, etc.) overmolded onto a stiffener (e.g., a bendable wire, a semi-flexible metal frame, etc.). In some non-limiting embodiments or aspects, housing 802 may extend between first end 805a and second end 805b and be configured to wrap around a patient's palm and / or wrist. For example, as shown in FIG. 8A , housing 802 may be configured to wrap around the center of a patient's palm with finger sensor 804 connected to housing 802 via wire 806. For example, as shown in FIG. 8B , housing 802 may be configured to wrap around a patient's wrist with finger sensor 804 connected to housing 802 via wire 806. For example, as shown in FIG. 8C , housing 802 may be configured to wrap around a patient's palm and wrist with finger sensor 804 connected to housing 802 via wire 806. In some non-limiting embodiments or aspects, first end 805a of housing 802 can be configured to connect to second end 805b of housing 802 via a connection mechanism (e.g., a strap, a hook-and-loop fastener, a button, etc.). In some non-limiting embodiments or aspects, housing 802 can include a glove configured to be worn on a patient's hand such that housing 802 completely covers the patient's hand and wrist, as shown in FIG. 8D , which allows finger sensor 804 to be connected to housing 802 without exposed wires.

[0134] The conductive probe or electrode 810 may provide direct conductive contact with the patient's skin, for example, for a skin resistance sensor configured to detect the patient's skin resistivity. The electrical properties of tissue can be measured using direct and / or alternating current, including various RF and microwave frequencies up to visible light.

[0135] The disposable adhesive protector 812 may include a disposable film configured to reduce or eliminate direct contact between the housing 802 and the patient. For example, the disposable adhesive protector 812 may include a sheet (e.g., a plastic sheet, a vinyl sheet, a latex sheet, a paper sheet, etc.) including a first portion configured to directly contact the patient and a second portion including an adhesive configured to adhere the disposable adhesive protector 812 to the portion of the housing 802 that faces the patient when the contact sensor device 800 is worn by the patient. In such a configuration, the disposable adhesive protector 812 may include openings sized and shaped to allow the conductive probes or electrodes 810 to directly contact the patient's skin through the disposable adhesive protector 812, or the adhesive protector 812 may include segments of conductive material to make or enhance contact between the skin and the contact sensor device 800.

[0136] Selected embodiments of the contact sensors 400, 800 may be disposable or single-use, while other embodiments may be reusable or multi-use, depending on the approach taken to cross-contamination reduction and prevention and / or the cost of various embodiments. This may encompass a range of options. At one end of this range, the contact sensors 400, 800 may be completely multi-use and may be decontaminated, for example, by spraying, wiping, or dipping in a cleaning solution, or may have a surface that kills any biologically active entities and / or catalyzes the destruction of contaminating chemicals. At the other end of the range of options, the sensors 400, 800 may be completely single-use and may be discarded or given to a patient to take home and use elsewhere as the patient's health and medical management needs prove useful. Intermediate aspects or embodiments of this range of usability may include those in which a single layer of material between the contact surface and the skin can be demonstrated to be single use, the sensor can be encapsulated in a single use sheath (e.g., sheath 450), some sensors or aspects of the sensor can be single use, such as thermistors or pulse oximeter photodiodes and phototransistors, the electronics reading the sensor can be reusable, all sensors and skin contacting materials can be single use, and the data processor, battery, and communications portions of contact sensor 400, 800 can be reusable.

[0137] 1A and 1B , user device 108 may include one or more devices capable of receiving information and / or data from fluid injection system 102, imaging system 104, sensor system 106, management system 110, and / or auxiliary system 112 (e.g., via communications network 114, etc.) and / or communicating information and / or data to fluid injection system 102, imaging system 104, sensor system 106, management system 110, and / or auxiliary system 112 (e.g., via communications network 114, etc.). For example, user device 108 may include one or more computing systems including one or more processors (e.g., one or more computing devices, one or more server computers, one or more mobile computing devices, one or more tablet computers, one or more mobile phones, etc.). In some non-limiting embodiments or aspects, user device 108 may include at least one of injector user interface 156, imager user interface 162, administrative user interface 172, or any combination thereof.

[0138] The user device 108 may take various forms, be referred to by various names, and / or be implemented by various specific devices or systems, depending on the user or users involved and the healthcare environment / system in which it is used. For example, the user device 108 may be a patient device, patient portal, or patient care portal where a patient enters information, signs in for a medical appointment or procedure, provides electronic consent, and / or receives information / training / support / comfort regarding any procedure to occur, or answers any questions regarding future or past procedures. The user device 108 may include a user's personal phone, tablet, and / or computer, which may be running applications or accessing web-based services to provide functionality of non-limiting embodiments or aspects described herein. The user device 108 may be part of a patient care portal provided by the patient's healthcare provider or insurer. The user device 108 may be a physician device 108 or physician portal 108 that provides patient data and / or adverse event risk assessments. The user device 108 may be specifically associated with one or more of the other devices in the system, e.g., the fluid injector system 102, the imaging system 104, or the sensor system 106. Additionally or alternatively, the user device 108 may be physically located where it is most advantageous for the user to perform a particular function or use a particular output or system aspect. For example, a patient may use a patient portal (e.g., the user device 108) to enter data or receive information, which may occur at a referring or prescribing physician's office or location, at home, in a waiting room, or in a public location such as a restaurant or parking lot. For example, a patient or caregiver may access the user device 108 whenever it is convenient to do so and functionally enabled by the particular implementation of the system. For example, a radiologist may access the user device 108, for example, in their office, a preparation room, an imaging room, or a reading room.For example, a technician may access the user device 108 through aspects of the fluid injection system 102 , the sensor system 106 , and / or the imaging system 104 .

[0139] The management system 110 may include one or more devices capable of receiving information and / or data from (e.g., via a communications network 114, etc.) and / or communicating information and / or data to (e.g., via a communications network 114, etc.) the fluid injection system 102, the imaging system 104, the sensor system 106, the user device 108, and / or the auxiliary system 112. For example, the management system 110 may include one or more computing systems including one or more processors (e.g., one or more computing devices, one or more server computers, one or more mobile computing devices, etc.). As an example, the management system 110 may include an administrative control computing system 166 and / or an administrative user interface 172. In some non-limiting embodiments or aspects, the management system 110 may be implemented within the fluid injection system 102, the imaging system 104, the sensor system 106, the user device 108, and / or the auxiliary system 112 (which may or may not be associated with the fluid injection system 102 and / or the imaging system 104).

[0140] The auxiliary system 112 may include one or more devices capable of receiving information and / or data from (e.g., via a communications network 114, etc.) and / or communicating information and / or data to (e.g., via a communications network 114, etc.) the fluid injection system 102, the imaging system 104, the sensor system 106, the user device 108, and / or the management system 110. For example, the auxiliary system 112 may include one or more computing systems including one or more processors (e.g., one or more computing devices, one or more server computers, one or more mobile computing devices, etc.). By way of example, the auxiliary systems 112 may include one or more hospital information systems (HIS) 168, cloud computing and off-site resources 170, electronic medical records (EMR), one or more radiology information systems (RIS), modality worklists (MWL), patient portals to the healthcare system, telemedicine portals, one or more picture archiving and communication systems (PACS), one or more laboratory information systems (LIS), one or more injection systems (e.g., fluid injection system 102, etc.), one or more imaging systems (e.g., imaging system 104, etc.), a smartphone, a tablet computer, or any combination thereof.

[0141] The communication network 114 may include one or more wired and / or wireless networks. For example, the communication network 114 may include a cellular network (e.g., a Long Term Evolution (LTE) network, a third-generation (3G) network, a fourth-generation (4G) network, a fifth-generation (5G) network, a code division multiple access (CDMA) network, etc.), a short-range wireless communication network (e.g., a Bluetooth network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, an optical fiber-based network, a cloud computing network, etc., and / or a combination of these or other types of networks.

[0142] The number and configuration of systems and devices shown in Figures 1A and 1B are provided as an example. There may be additional, fewer, different, or differently arranged systems and / or devices than those shown in Figures 1A and 1B. Furthermore, two or more systems or devices shown in Figures 1A and 1B may be implemented within a single system or device, or a single system or device shown in Figures 1A and 1B may be implemented as multiple distributed systems or devices. Additionally or alternatively, a set of systems or devices (e.g., one or more systems, one or more devices, etc.) of environment 100 and / or implementation 150 may perform one or more functions described as being performed by another set of systems or another set of devices of environment 100 and / or implementation 150.

[0143] 2, which is a diagram of example components of device 200. Device 200 may correspond to one or more devices of fluid injection system 102, one or more devices of imaging system 104, one or more devices of sensor system 106, user device 108 (e.g., one or more devices of the system of user device 108, etc.), one or more devices of management system 110, and / or one or more devices of auxiliary system 112. In some non-limiting embodiments or aspects, one or more devices of fluid injection system 102, one or more devices of imaging system 104, one or more devices of sensor system 106, user device 108 (e.g., one or more devices of the system of user device 108, etc.), one or more devices of management system 110, and / or one or more devices of auxiliary system 112 may include at least one device 200 and / or at least one component of device 200.

[0144] As shown in FIG. 2, device 200 may include a bus 202, a processor 204, a memory 206, a storage component 208, an input component 210, an output component 212, and / or a communication interface 214.

[0145] Bus 202 may include components that enable communication between components of device 200. In some non-limiting embodiments or aspects, processor 204 may be implemented in hardware, software, or a combination of hardware and software. For example, processor 204 may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and / or any processing component that can be programmed to perform a function (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.). Memory 206 may include random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and / or instructions for use by processor 204.

[0146] Storage component 208 may store information and / or software related to the operation and use of device 200. For example, storage component 208 may include a hard disk (e.g., a magnetic disk, optical disk, magneto-optical disk, solid-state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of computer-readable medium along with a corresponding drive.

[0147] The input components 210 may include components (e.g., a touchscreen display, a keyboard, a keypad, a mouse, buttons, switches, a microphone, etc.) that enable the device 200 to receive information, such as through user input. Additionally or alternatively, the input components 210 may include sensors for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, contact sensor 164a, non-contact sensor 164b, and / or any of the sensors described herein, etc.). The output components 212 may include components (e.g., a display, a speaker, a tactile or haptic output, one or more light-emitting diodes (LEDs), etc.) that provide output information from the device 200.

[0148] Communications interface 214 may include transceiver-like components (e.g., a transceiver, a separate receiver and transmitter, etc.) that enable device 200 to communicate with other devices via a wired connection, a wireless connection, a combination of wired and wireless connections, etc. Communications interface 214 may enable device 200 to receive information from another device and / or provide information to another device. For example, communications interface 214 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0149] The device 200 may perform one or more processes described herein. The device 200 may perform these processes based on the processor 204 executing software instructions stored by a computer-readable medium, such as the memory 206 and / or the storage component 208. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A memory device includes a memory space located within a single physical storage device or a memory space distributed across multiple physical storage devices.

[0150] Software instructions may be loaded into memory 206 and / or storage component 208 from another computer-readable medium or from another device via communications interface 214. When executed, the software instructions stored in memory 206 and / or storage component 208 can cause processor 204 to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments or aspects described herein are not limited to any specific combination of hardware circuitry and software.

[0151] The memory 206 and / or storage component 208 may include a data storage or one or more data structures (e.g., a database, etc.). The device 200 may be capable of receiving information from, storing information in, communicating information to, or retrieving information stored in the data storage or one or more data structures in the memory 206 and / or storage component 208.

[0152] The number and arrangement of components shown in Figure 2 are provided as an example. In some non-limiting embodiments or aspects, device 200 may include additional components, fewer components, different components, or components arranged differently than those shown in Figure 2. Additionally or alternatively, a set of components (e.g., one or more components) of device 200 may perform one or more functions described as being performed by another set of components of device 200.

[0153] 3, which is a flowchart of a non-limiting embodiment or aspect of a process 300 for protecting a patient's health for fluid infusion. In some non-limiting embodiments or aspects, one or more of the steps of process 300 may be performed (e.g., completely, partially, etc.) by management system 110 (e.g., one or more devices of management system 110, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 300 may be performed (e.g., completely, partially, etc.) by a user or another device or group of devices separate from or including management system 110, such as fluid injection system 102 (e.g., one or more devices of fluid injection system 102), imaging system 104 (e.g., one or more devices of imaging system 104), sensor system 106 (e.g., one or more devices of sensor system 106), user device 108 (e.g., one or more devices of the system of user device 108), and / or auxiliary system 112 (e.g., one or more devices of auxiliary system 112).

[0154] 3 , in step 302, process 300 includes acquiring patient data. For example, management system 110 may acquire patient data associated with the patient. By way of example, management system 110 may receive and / or retrieve patient data associated with the patient from at least one of fluid injection system 102, imaging system 104, sensor system 106, user device 108, auxiliary system 112, or any combination thereof. Patient data may also be acquired directly from the patient by a human or by interaction with a user interface of management system 110, which may prompt and record such data from the user and / or patient.

[0155] Patient data included the following patient-related parameters: age; sex; weight; previous chemotherapy status, such as adverse peripheral venous conditions due to long-term tumor treatment (e.g., yes, no, number of chemotherapy cycles received); estimated glomerular filtration rate (eGFR) (e.g., 45 ml / min / 1.73 m); 2eGFR less than 0.001; thyroid-stimulating hormone (TSH) level; triiodothyronine (FT3) thyroxine (FT4) ratio (FT3 / FT4); amount or level of environmental influence (e.g., regional iodine saturation or nutrient amount or level in an area or location relevant to the patient); prior response to previous fluid infusion status (e.g., yes, no, level, etc.); atopic disease status (e.g., yes, no, level, etc.); medical conditions related to the presence of diabetes and / or hypertension, such as diabetic nephropathy status (e.g., yes, no, level, etc.); congestive heart failure status (e.g., yes, no, level, etc. the patient data may include at least one of: a blood pressure (e.g., 1000 kcal ...

[0156] In some non-limiting embodiments or aspects, the management system 110 may provide and / or implement a patient care system or patient care portal accessible via an application (e.g., via the user device 108, etc.), e.g., as software on a personal device, smartphone, tablet computer, and / or other computer, a website, and / or a custom device that can be loaned or given to the patient. The patient care portal, as described herein, may promote patient health by providing information to and collecting information from the patient, primarily prior to fluid delivery and imaging studies. The application may provide patient support for imaging procedure referral, screening, preparation, access, education (e.g., video and / or written / graphical materials, etc.), health information management, and / or patient feedback to the provider regarding the user experience. For example, the application may be used to integrate and manage patient data, sensor data, and / or other information during a series of patient care steps for a diagnostic imaging procedure, from initial prescription for an imaging scan to tracking diagnostic results for future reference. The application may promote mental health by providing relevant information to the patient to help them have a more normal and positive diagnostic imaging experience. The application can make patient experience information more visible to the patient community, the treating clinician, the provider network, and / or others improving diagnostic imaging procedures, as well as to other aspects of the management system 110, such as the risk assessment aspects of the management system 110. The application can eliminate or substantially reduce the possibility of inadvertent patient missed appointments, canceled appointments due to fear of the procedure, workflow delays while the patient fills out forms, poor imaging results due to lack of proper patient preparation or the patient's inability to complete specific tasks related to the procedure, such as breathing at the correct time, and / or patient discomfort due to patient uncertainty or unfamiliarity with normal aspects of the procedure.Thus, the patient care portal or system can provide medical community-based diagnostic imaging patient support, connect the user experience to diagnostic imaging, link information to each other to help patients provide a better patient experience, improve patient referral and care for improved diagnostic imaging experiences and outcomes, increase patient compliance and comfort, more efficiently utilize diagnostic imaging center resources, and / or improve diagnostic imaging quality.

[0157] The management system 110 can obtain patient data and / or sensor data related to a patient through a patient care portal or application for the system. The application can be accessible by the physician prescribing the imaging scan to assist in scheduling the patient's imaging scan. For example, FIGS. 13A and 13B show that the application can apply one or more user-selected filters and / or a weighted sum algorithm (e.g., patient location, patient insurance type, type of scan prescribed, quality rating, etc.) to a list of available imaging center locations to determine an imaging center location to recommend to the patient. The application can provide information to the patient to help them prepare for and learn about their imaging procedure experience. FIG. 14A shows a patient portal displayed on a user device 108 that allows the patient to pre-configure imaging scan room options, such as ambiance or lighting, music, temperature, etc., prior to the imaging scan and / or provide additional patient data. This data can be automatically communicated to the imaging room at the time of the patient's scan. FIG. 14B shows a patient portal displayed on a user device 108 that provides information (e.g., videos, etc.) about what the patient can expect on the day of the scan. FIG. 14C shows a patient portal displayed on the user device 108, providing calendar notifications and directions to imaging scan locations, which may improve patient comfort, health, and / or satisfaction.

[0158] Upon checking in for an imaging scan, patient data associated with the patient may be automatically synchronized or retrieved from the auxiliary system 112 and / or the cloud and / or patient portal via the user device 108, thereby reducing the amount of time required for the patient to check in. As shown in FIG. 15A, the management system 110 can automatically retrieve medical records and patient preferences before or during check-in and present them to the patient via an application on the user device 108 to more quickly verify the accuracy and completeness of the records and patient preferences. As shown in FIG. 15B, the management system 110 can use an application to provide an automated and integrated consent process in which the patient electronically consents to one or more procedures via the application (e.g., on the user device 108), thereby improving and / or increasing a technician's quality time with the patient.

[0159] After check-in, the patient's veins may be scanned by the imaging system 104 (e.g., by one or more cameras of the imaging system 104), and the management system 110 may analyze and / or store the scanned images of the patient's veins. A system such as the systems in U.S. Patent Application Publication Nos. 2004 / 0171923 A1 and / or 2008 / 0147147 A1, filed December 6, 2003, and December 18, 2006, respectively (the entire contents of each of which are incorporated herein by reference), may be used to evaluate the patient's veins and, if desired, facilitate venous access. For example, patient data and vein analysis may be used to adjust or recommend adjustments or restrictions to the fluid injection protocol and / or imaging protocol for the patient, which may be presented to a user (e.g., a radiologist, etc.) via an application on the user device 108 for approval, or directly to the injector system 102 or the imaging system 104. The sensor system 106 (e.g., smart bed sensors, cameras, contact sensor devices 400 and / or 800, etc.) may continuously determine patient, sensor, and / or scan data associated with the patient, and the management system 110 may adjust the fluid injection and / or imaging protocols for the patient based on the measured patient, sensor, and / or scan data, which may be presented to a user (e.g., a radiologist, etc.) via an application on the user device 108 for approval by the user. Alternatively, such adjustments may be made automatically within preselected limits. For example, patient-based dose and the patient's cardiac output may allow fluid injection protocols and scan durations to be adjusted for a patient.

[0160] The management system 110 may perform one or more scan image evaluations of medical images acquired by the imaging system 104, which may be presented to a user (e.g., a radiologist, etc.) via an application on the user device 108 for review of the scan image evaluation. For example, the management system 110 may apply one or more artificial intelligence-based image evaluation tools to the patient's scanned images to evaluate the quality of the scan and / or provide diagnostic recommendations. The medical scan images and / or their analysis may be stored (e.g., in the auxiliary system 112, in the cloud, etc.) for retrieval via an application by the radiology team and / or the patient.

[0161] The management system 110 may use applications to continuously monitor equipment and / or supplies and automatically order new equipment and / or supplies if inventory levels do not meet threshold levels and / or if equipment breaks down or malfunctions.

[0162] 3, in step 304, process 300 includes determining an initial risk prediction for the patient from a fluid injection. For example, management system 110 may determine an initial risk prediction for the patient from a fluid injection (e.g., a contrast injection) to be administered to the patient based on patient data. As an example, the initial risk prediction may include a probability that the patient will experience at least one adverse event in response to the fluid injection. Such a risk prediction may be a numerical value between 0 and 100%, or may be bucketed into, for example, low, medium, and high buckets.

[0163] The adverse events may include at least one of the following adverse events: extravasation, catheter clotting, post-contrast acute kidney injury, acute adverse events (e.g., atopic or allergic reactions, hives, etc.), contrast-induced nephrotoxicity, thyroid disorders or thyrotoxicosis, headache, taste changes, visual disturbances, chest pain, enlarged blood vessels (vasodilation) and continuous hypotension, nausea, vomiting, back pain, urinary urgency, and injection site reactions such as bleeding, swelling, itching, and pain, or any combination thereof.

[0164] The management system 110 can apply an algorithm, or aspects of one or more algorithms, which may be an adaptation or implementation of individual physician practices, professional society guidelines, and / or hospital procedures into computer code, to patient data and / or sensor data associated with the patient to determine an initial risk prediction for the patient (and / or to determine a test prediction, and / or to determine the patient's level of motion that may cause artifacts in imaging scans, and / or to determine the patient's health level, and / or to determine the patient's current risk prediction, and / or to determine the patient's distress level). In such an example, different hospitals may have different algorithms or aspects of one or more algorithms based on local preferences, practices, country, and / or other factors associated with the different hospitals. In another aspect or embodiment, the management system 110 can present the patient data to a physician or healthcare provider, who can make their own mental risk, health, or distress assessment, which can be manually entered into the management system 110 for use in subsequent steps.

[0165] In some non-limiting embodiments or aspects, the management system 110 may employ algorithms that use baseline comparisons (e.g., to determine parameter changes from baseline parameters, etc.); time sequence algorithms (e.g., using mean, gradient, second moment, SPC of parameters vs. normal, etc.); monotonic continuous function transformations; algorithms that convert continuous functions to discrete functions; threshold-based algorithms (e.g., algorithms with at least one threshold that varies based on patient parameters, time, volume of fluid infused, etc.); goodness-of-fit functions; dictionary modes of curve fitting (e.g., MRF, etc.); artificial intelligence applied to the time sequence of a single data stream; artificial intelligence applied to multiple data streams simultaneously; acoustic triangulation algorithms; algorithms that classify individual parameters and combine categories of parameters; algorithms that normalize individual data streams using continuous (linear or non-linear) functions; algorithms that arrange parameters in multidimensional space. at least one of the following algorithms may be applied to the patient data and / or sensor data associated with the patient to determine an initial risk prediction for the patient (and / or to determine an exam prediction, and / or to determine the patient's level of motion that may cause artifacts in imaging scans, and / or to determine the patient's health level, and / or to determine a current risk prediction, and / or to determine the patient's level of distress).

[0166] In some non-limiting embodiments or aspects, the management system 110 may apply one or more algorithms and / or methods disclosed by U.S. Patent Application Publication No. 2016 / 0224750A1, filed January 29, 2016, the contents of which are incorporated herein by reference in their entirety, to patient data and / or sensor data associated with the patient to determine an initial risk prediction for the patient (and / or determine a test prediction, and / or determine a patient's level of motion that may cause artifacts in imaging scans, and / or determine a patient's health level, and / or determine a current risk prediction, and / or determine a patient's distress level).

[0167] Example Algorithm Tables 1 through 4 below illustrate exemplary algorithms that may be utilized to determine an initial risk prediction for a user. The exemplary algorithms may be executed by the management system 110 and / or a healthcare provider based on information provided by the management system 110 (e.g., via the user device 108, etc.) and / or by utilizing the user device 108 or a human, which may subsequently provide the initial risk prediction results to the management system 110. [Table 1]

[0168] Table 1 lists, in the leftmost column, exemplary patient-related parameters that may be considered to determine an initial risk prediction, including the probability that the patient will experience extravasation in response to a fluid injection (e.g., contrast injection, etc.). As shown, these parameters may include the patient's age (in years), the patient's gender, the patient's body mass index (BMI), the patient's previous chemotherapy status (e.g., yes, no, number of cycles, etc.), the Eastern Cooperative Oncology Group (ECOG) performance status, the patient's medication status (e.g., yes, no, current medication, etc.), etc. Each parameter may be assigned a score of 1, 2, or 3, as listed at the top of the second through fourth columns, depending on the value of each parameter. If each of the parameters for an algorithm is evaluable and / or available, the sum of the scores may provide a score used to represent the patient's initial risk prediction for extravasation, as shown in the rightmost column of Table 1. For example, a patient who is 55 years old would receive a score of 2 for age, 1 for being male, 2 for a BMI of 27, 3 for two previous cycles of chemotherapy, 2 for ECOG1 status, and 2 for being on medication but not receiving corticosteroids. Thus, the patient's total is 2+1+2+3+2+2=12, making this exemplary patient at moderate risk for extravasation. [Table 2]

[0169] Table 2 lists, in the leftmost column, exemplary patient-related parameters that may be considered to determine an initial risk prediction, including the probability that the patient will experience an acute adverse event in response to a fluid infusion (e.g., a contrast injection, etc.). As shown, these parameters may include the patient's atopic disease status (e.g., yes, no, level, etc.) and / or the patient's prior response to a previous fluid infusion (e.g., yes, no, level, etc.). Atopic disease refers to a form of allergy in which hypersensitivity reactions, such as dermatitis and / or asthma, can occur in parts of the body that have not come into contact with the allergen. The prior response to a previous fluid infusion may include indications related to the patient having had any previous allergic reactions to a previous fluid infusion. Low prior responses may include feeling, flushing, nausea, etc. High prior responses may include hives and / or anaphylactic reactions requiring treatment. If each of the parameters for the algorithm is assessable and / or available, the sum of the scores may provide a score used to represent the patient's initial risk prediction for an acute adverse reaction, as shown in the rightmost column of Table 2. [Table 3]

[0170] Table 3 lists, in the left-most column, exemplary patient-related parameters that may be considered to determine an initial risk prediction, including the probability that the patient will experience post-contrast acute kidney injury in response to a fluid infusion (e.g., a contrast injection). As shown, these parameters may include the patient's age, the patient's BMI, the level of chronic kidney disease (CKD) as assessed using a 5-point glomerular filtration rate scale, medical conditions such as those related to the patient's presence of diabetes and / or hypertension, and / or the patient's history and status of malignancy. When each of the parameters for the algorithm is assessable and / or available, the sum of the scores may provide a score used to represent the patient's initial risk prediction for post-contrast acute kidney injury, as shown in the right-most column of Table 3. [Table 4]

[0171] Table 4 lists in the left-most column exemplary patient-related parameters that may be considered to determine an initial risk prediction, including the probability that the patient will experience thyrotoxicosis in response to a fluid infusion (e.g., a contrast injection, etc.). As shown, these parameters may include the patient's age, the patient's sex, the patient's BMI, and the iodine deficiency status of the patient's geographic region. When each of the parameters for the algorithm is assessable and / or available, the sum of the scores may provide a score used to represent the patient's initial risk prediction for thyrotoxicosis, as shown in the right-most column of Table 4.

[0172] The exemplary initial risk prediction algorithms presented above with respect to Tables 1 through 4 are intended to be simple and understandable to convey the variety and flexibility of algorithms that may be used to determine initial risk predictions according to non-limiting embodiments or aspects of the present disclosure. The exemplary algorithms may be executed by the management system 110 and / or a healthcare provider based on information provided by the management system 110 (e.g., via the user device 108, etc.) and / or may be executed by utilizing the user device 108 or a human, who may subsequently provide the initial risk prediction results to the management system 110. It is anticipated that as additional data is collected from patients using data collection processes, systems, and / or devices according to non-limiting embodiments or aspects of the present disclosure, the algorithms used may be improved and / or modified. This improvement and / or modification may be created and implemented by the management system 110 in collaboration with humans and / or supervised machine learning, and / or may be performed by the management system 110 itself, which may also be referred to as unsupervised machine learning.

[0173] As another example, if each of the parameters for an algorithm cannot be evaluated and / or are unavailable for a patient, one or more alternative algorithms or functions can be used to provide an initial risk prediction based on the patient and / or sensor data parameters that are available for the patient. For example, one approach is to reduce the threshold for the initial risk prediction by an amount proportional to the parameters available for the patient. For example, Table 1 includes six data or parameters and thresholds of <9, 9-14, and >14. If one data element or parameter is missing for a patient, the threshold would be 5 / 6 of the full set of thresholds, i.e., <7.5, 7.5-11.7, and >11.7. Another exemplary approach is to automatically assume a moderate risk score, e.g., 2, for any missing parameters. A more conservative approach could automatically assume a high-risk value of 3 for any missing parameters for the patient.

[0174] As another example, as more data is collected from more patients, the weightings given to individual parameters in a scoring table may be adjusted, for example, from the uniform distribution shown in the examples of Tables 1 through 4. In Tables 1 through 4, a simple sum of the scores gives each parameter equal weight. For example, for an early risk prediction of extravasation assessed using Table 1, if analysis of data collected through use of a non-limiting embodiment or aspect of the present disclosure shows that BMI has a two times stronger relationship to the risk of extravasation than other parameters, then BMI can be given a weight of 2 / 7, and each of the other factors can be given a weight of 1 / 7, as opposed to the uniform distribution of 1 / 6, respectively, implicitly used in Table 1.

[0175] As another example, the relationship between a parameter, such as age or BMI, and the number of points assigned based on the parameter's value may be expanded to be a continuous functional relationship rather than a discrete binning relationship as shown in the examples of Tables 1 through 4. For example, such a functional relationship may be refined to the extent that the data allows without overfitting the situation, assuming reasonable expectations of human variability. By way of example, such a functional relationship may be determined using any applicable multivariate analysis technique. As noted above, in some non-limiting embodiments or aspects, an initial risk prediction may be determined by a human healthcare provider based at least in part on data collected by non-limiting embodiments or aspects of the present disclosure, which may have the advantage of allowing the healthcare provider to gradually gain trust in the system. For similar reasons, using multivariate analysis may also be advantageous, making the operation of these algorithms understandable to humans who need to use and trust the algorithms.

[0176] In some non-limiting embodiments or aspects, the management system 110 can process patient data and / or sensor data associated with the patient using a machine learning model to determine an initial risk prediction for the patient. For example, the management system 110 may generate an initial risk prediction model (e.g., an estimator, classifier, predictive model, detector model, etc.) using machine learning techniques, including, for example, supervised and / or unsupervised techniques, such as decision trees (e.g., gradient-boosted decision trees, random forests, etc.), logistic regression, artificial neural networks (e.g., convolutional neural networks, etc.), Bayesian statistics, learning automata, hidden Markov models, linear classifiers, quadratic classifiers, association rule learning, etc. The initial risk prediction machine learning model can be trained to provide an output including a probability that the patient will experience at least one adverse event in response to an input including patient data and / or sensor data associated with the patient in response to a fluid infusion (e.g., a contrast injection, etc.). In such an example, the initial risk prediction can include a probability score associated with a prediction that the patient will experience at least one adverse event in response to the fluid infusion.

[0177] The management system 110 can generate an initial risk prediction model based on patient data and / or sensor data (e.g., training data, etc.). For example, non-limiting embodiments or aspects of the present disclosure may collect patient data and / or sensor data associated with patients over a period of time to determine an initial risk prediction for the patient using one of the simpler algorithms described above, and when data has been collected from a sufficient number of patients (e.g., when the precision, prediction, and / or recall of the machine learning model generated based on the collected data meets a threshold, etc.), the machine learning model may be used to determine an initial risk prediction for the patient. In some implementations, the initial risk prediction model is designed to receive as input patient data and / or sensor data (e.g., one or more parameters of the patient data and / or sensor data, etc.) and provide as output a prediction (e.g., a probability, a binary output, a yes-no output, a score, a prediction score, a classification, etc.) regarding whether the patient will experience at least one adverse event (e.g., extravasation, post-contrast acute kidney injury, an acute adverse event (e.g., an atopic or allergic reaction, etc.), contrast-induced nephrotoxicity, a thyroid disorder, etc.) in response to a fluid injection (e.g., a contrast injection, etc.). In some non-limiting embodiments or aspects, the management system 110 stores the initial prediction model (e.g., stores the model for later use). In some non-limiting embodiments or aspects, the management system 110 stores the initial prediction model in a data structure (e.g., a database, a linked list, a tree, etc.). In some non-limiting embodiments, the data structure is located within the management system 110 or external (e.g., remote) to the management system 110 (e.g., within the auxiliary system 112, etc.).

[0178] 3 , in step 306, the process 300 includes providing an initial patient risk prediction and / or a patient health level for a fluid injection. For example, the management system 110 may provide the patient's initial risk prediction and / or the patient's health level to the user device 108 before a fluid injection (e.g., a contrast injection, etc.) is administered to the patient. As an example, the user device 108 may display the patient's initial risk prediction and / or the patient's health level to a user (e.g., a physician, etc.) before a fluid injection is administered to the patient.

[0179] In some non-limiting embodiments or aspects, the initial risk prediction and / or health level may further include at least one of a prompt to administer medication to the patient before a fluid infusion, a prompt to adjust an infusion protocol for a fluid infusion and / or an imaging protocol for an imaging scan, a prompt to prepare the patient before a fluid infusion, a prompt to consult a specialist about at least one adverse event, a prompt to observe and / or follow up with the patient after a fluid infusion and / or an imaging scan, or any combination thereof. For example, in response to determining the initial risk prediction (e.g., in response to determining an initial risk prediction that includes a probability that the patient will experience an adverse event that meets a threshold probability), the management system 110 may determine and recommend actions that a user (e.g., a healthcare professional, etc.) can take to reduce the probability that the patient will experience the adverse event. As an example, the management system 110 may consult a lookup table and / or apply an algorithm (e.g., a machine learning model, etc.) to the initial risk prediction and / or the patient data and / or sensor data used to generate the initial risk prediction to determine and provide to the user one or more prompts or recommendations that may reduce the probability that the patient will experience an adverse event. The management system 110 can adjust one or more thresholds used by the sensors to monitor for successful infusion and / or adverse events.

[0180] In some non-limiting embodiments or aspects, in response to determining an initial risk prediction (e.g., in response to determining an initial risk prediction including a probability that a patient will experience an adverse event that meets a threshold probability), the management system 110 may recommend to a healthcare provider that they control and / or adjust one or more operations of the fluid injection system 102 and / or the imaging system 104, and / or may automatically control and / or adjust one or more operations of the fluid injection system 102 and / or the imaging system 104, if legally permitted. For example, the management system 110 may recommend manual or automatic adjustments to the injection protocol for a fluid injection (e.g., adjusting the maximum flow rate, adjusting the maximum pressure, adjusting the injection duration, adjusting the total volume of fluid or contrast delivered or to be delivered (e.g., to reduce the total iodine load), etc.) and / or the imaging protocol for an imaging scan (e.g., adjusting the scan time and / or duration (e.g., to accommodate a patient who is unable to hold their breath for the originally planned scan duration), adjusting the kVp (e.g., reducing the kVp to allow adequate image contrast with a reduced total iodine load), adjusting breathing instructions, etc.). As an example, the management system 110 may consult a lookup table and / or apply an algorithm (e.g., a response surface, a machine learning model, etc.) to the initial risk prediction and / or the patient data and / or sensor data used to generate the initial risk prediction to determine one or more adjustments to the injection protocol and / or imaging protocol that can reduce the probability that the patient will experience an adverse event.

[0181] For example, based on patient data related to the patient's known adverse events (e.g., allergic reactions, etc.) after a previous fluid injection (e.g., a previous contrast injection) and / or the patient's atopic tendencies, the management system 110 may determine an initial risk prediction of an acute adverse event, including a prompt to administer medication to the patient prior to contrast injection in accordance with applicable guidelines (e.g., American College of Radiology (ACR) guidelines, etc.) and / or a prompt to observe the patient for a predetermined interval after contrast injection (e.g., for a longer period than usual for high-risk patients) while monitoring one or more patient parameters related to the predicted adverse event.

[0182] Based on patient data related to the patient's individual renal function, such as, for example, its laboratory surrogate parameters (e.g., eGFR, etc.) and / or the patient's current medications, the management system 110 may determine an initial risk prediction for contrast-induced nephrotoxicity, including prompts to prepare the patient prior to contrast injection in accordance with applicable guidelines (e.g., European Society of Genitourinary Radiology guidelines, etc.), prompts (and / or automatic control) to adjust the injection protocol for the contrast injection and / or the imaging protocol for the imaging scan, and / or prompts to follow up with the patient regarding renal function after the examination, for example, to reduce the total iodine given to the patient, by administering intravenous hydration, etc.

[0183] For example, based on patient data related to the patient's known thyroid disorder, the patient's environmental influences (e.g., regional iodine saturation or nutrition in the area where the patient lives), and / or the patient's current medications, the management system 110 may determine an initial risk prediction for a thyroid disorder (e.g., thyrotoxicosis, etc.), including a prompt to forgo contrast injection and associated imaging until consultation with an endocrinologist has been obtained, and / or a prompt to administer medication to the patient prior to contrast injection.

[0184] 3 , in step 308, process 300 includes determining sensor data. For example, sensor system 106 may determine (e.g., determine, collect, acquire, capture, measure, sense, etc.) sensor data associated with the patient before, during, and / or after a fluid infusion (and / or before, during, and / or after a test infusion administered to the patient before the fluid infusion). As an example, sensor system 106 may determine (e.g., determine, collect, acquire, capture, measure, sense, etc.) sensor data associated with the patient before, during, and / or after a fluid infusion (and / or before, during, and / or after a test infusion administered to the patient before the fluid infusion).

[0185] The sensor data may include the following parameters related to the patient, i.e., firstly, parameters that may be affected by changes in the infusion and / or the patient's health, such as heart rate; sound or vibration (e.g., sound or vibration associated with fluid inflow, sound or vibration proximate to the infusion site, etc.); temperature (e.g., temperature of fluid inflow, temperature proximate to the infusion site, local temperature, tissue temperature, etc.); oxygen saturation (e.g., oxygen saturation of fluid inflow, oxygen saturation proximate to the infusion site, etc.); pulse rate; ECG; body fat / water ratio; tissue impedance; vascularity level; blood vessel diameter; hydration level; hematocrit level; skin resistivity; blood pressure; muscle tension level; light absorbance level; shaking or trembling or movement / motion (e.g., yes, no, level, etc.); arm position; arm circumference; respiratory rate; respiratory depth; amount of absorbed radiation; tightness, positional stability, and / or contact integrity of contact sensor device 400 and / or 800; amount of swelling and / or displacement; EMG; skin color; amount of surface vasodilation (flushing); bioimpedance; light absorption rate; inflammation level; secondly, parameters that are unlikely to be immediately affected by changes in the injection and / or patient's health, such as fat / muscle ratio; hemoglobin level; thirdly, environmental parameters, such as ambient temperature of the patient's surrounding environment, air pressure of the patient's surrounding environment, ambient light level, ambient sound level; or any combination thereof.

[0186] As described in more detail herein below, the management system 110 can determine patient-related predictions (e.g., initial risk predictions, test predictions, current risk predictions, etc.) and / or patient health based on patient data and / or patient-related sensor data. Overall patient health or patient comfort can be considered to include multiple aspects or dimensions. One aspect can be medical health, which is generally considered to be free of adverse events. A patient can be said to be comfortable if they are not having adverse events. A patient may be mildly uncomfortable, for example, experiencing a feeling of heat or hot flushes, a sense of the need to urinate, stomach dryness, or itchy skin, or the patient may have a serious or severe reaction (e.g., nausea, hives, or anaphylactic shock requiring timely medical intervention, for example with epinephrine). Another aspect of patient health is physical health. A patient may be comfortable lying on the infusion bed or table of an imaging system; may experience mild discomfort with some aching or pain that motivates movement to alleviate the discomfort; or may experience severe discomfort that may cause the patient to move involuntarily or uncontrollably, leading to poor image quality. A third aspect of patient health is the patient's mental state. A patient may be at ease and comfortable, accepting treatment and cooperating as needed; anxious and alert, overreacting to unexpected noises or movements, or in an agitated mental state, having difficulty controlling their reactions. While these three aspects of health may overlap and are somewhat arbitrary, they are clearly beneficial for the purposes of this specification. Those skilled in the art will recognize that physiological parameters such as heart rate, respiratory rate, and skin conductance can be used to assess patient comfort, and increases in these parameters can be used by the management system 110 to alert medical personnel to check on the patient, for example, when the patient's condition transitions from comfortable to moderate with respect to one or more of these aspects.It is difficult for a medical professional to manually or mentally attend to these subtle changes, and it is an object of non-limiting embodiments or aspects of the present disclosure to synthesize these measurements into a simple alert system for use by a medical professional or the entire system, including the fluid delivery system and / or imaging system. It is also an object of non-limiting embodiments or aspects of the present disclosure to provide an aspect that preventatively and proactively promotes patient health, such as proactive education or a more comfortable environment.

[0187] 12 , multiple data processors, data paths, and / or data analysis algorithms can be used to refine various forms and / or streams of patient and / or sensor data, and / or combine various forms and / or streams of patient and / or sensor data into new forms and / or new streams of data, and / or distinguish additional streams of data. The patient and / or sensor data and / or their data streams can be used by the management system 110 to make recommendations to a user and / or control the operation of the fluid injection system 102 and / or imaging system 104 (e.g., determine an initial risk prediction, determine a test prediction, determine whether the injection is progressing normally as expected, determine a patient's health level, generate a current risk prediction, control the fluid injection system 102 and / or imaging system 104 in response to such determinations, etc.). For example, the recommendations and / or system controls may assess risk, guide preventative measures, minimize the occurrence of, detect and / or manage extravasation, post-contrast acute kidney injury, acute adverse events, contrast-induced nephrotoxicity, and / or thyroid disorders or thyrotoxicosis, thereby reducing one or more serious complications that may be associated with contrast injection.

[0188] As an example, to detect extravasation, the management system 110 may receive three data streams representing sound or vibration measured by three different sensors positioned at three different locations on a patient's limb (e.g., arm, leg, hand, foot, etc.) proximate to and / or surrounding an injection site for test injection and / or fluid injection (e.g., contrast injection, etc.). For example, with further reference to FIG. 12 and again to FIG. 4E, the right-most image in the series of images in FIG. 4E shows three sensor array covers of a cross-section of a patient's limb that may be used to capture three data streams of sensor data, e.g., data related to sound or vibration. The management system 110 can combine the three audio data streams 1201, 1202, 1203 via a data combination technique 1204 (e.g., triangulation, etc.) to generate a combined data stream 1205 indicative of the center of one or more sound sources in space. Additionally or alternatively, the management system 110 can apply a data extraction process 1206 to one or more of the three acoustic data streams to generate one or more additional data streams 1207. For example, the management system 110 may apply a real-time Fourier transform to the data streams to generate a multidimensional data stream of amplitude as a function of frequency over time. Such signals may be combined by the management system 110 with information about the infusion fluid, such as fluid type, viscosity, temperature, flow rate, and catheter or other fluid path element characteristics, to assess the acoustic spectrum and determine, for example, whether the acoustic spectrum indicates a normal or adequate infusion, a marginal infusion, or an abnormal or inadequate infusion that may lead to an adverse event.

[0189] In such an example, the management system 110 may receive a data stream 1208 containing data that changes over time and use the data "as is" or without processing the data in any additional way before using the data to make recommendations to the user and / or control the operation of the fluid injection system 102 and / or the imaging system 104. For example, a data stream of sensor data containing parameters related to a patient's skin temperature may contain data that changes over time and that is used "as is" without additional processing of the data.

[0190] In such an example, the management system 110 may receive data 1209 that does not change over time. For example, the received data may include information related to the patient, such as the patient's age, the patient's chemotherapy status (which may indicate, for example, a higher likelihood of weaker veins), etc. As an example, the data may include fixed information, e.g., catheter gauge, contrast injection and / or fluid pathway information, such as contrast concentration.

[0191] In such an example, the management system 110 may receive a data stream 1210 of sensor data and / or patient data from at least one of the fluid infusion system 102, the imaging system 104, the sensor system 106, the user device 108, the auxiliary system 112, or any combination thereof. For example, the management system 110 may receive data related to programmed flow rate, actual or measured flow rate, pressure, concentration, and / or other infusion-related data from the fluid infusion system 102.

[0192] In such examples, the management system 110 may apply one or more algorithms 1211, as described herein, to the data stream of patient data and / or sensor data to determine recommendations and / or system controls 1212 (e.g., determine a risk prediction, determine a test prediction, determine a patient's health level, generate a current risk prediction, control the fluid injection system 102 and / or the imaging system 104 in response to such determinations, etc.). For example, the sound data stream may be affected by unknown variables or factors, such as contrast concentration, temperature, flow rate, and catheter type and / or size, as well as the patient's venous anatomy and / or the position of the catheter within the patient's veins. At the start of a test or contrast injection, the management system 110 may expect the sound frequency and amplitude to be within certain normal or expected ranges, which may be learned and / or determined from previous studies and fixed in the algorithm. Additionally or alternatively, the algorithm may employ ongoing learning and adaptation. If the sound at the start of the test or contrast injection is outside of the normal or expected range, the algorithm may cause the management system 110 to indicate to the user that the sound is outside of the normal or expected range, which may indicate that the wrong catheter is being used and / or the wrong fluid is being used for the contrast injection. For example, the frequency of the sound (e.g., a "whoosh," "whistle," or "trill") may depend on the catheter gauge, length, and stiffness, as well as the fluid and its properties, flow rate, vessel properties, and the position of the catheter or needle within the patient's vein or tissue (e.g., pressed against the vessel wall). For example, this information on which the sound frequency depends may be entered as data into the management system 110 manually by the user and / or automatically from the fluid injection system 102.

[0193] In such an example, during an injection, the algorithm may expect the acoustic data to remain relatively consistent until there is a change in fluid concentration, fluid temperature, and / or fluid flow rate. For example, the management system 110 may alert the user if there is a change in fluid concentration, fluid temperature, and / or fluid flow rate that meets a threshold change or magnitude when no change is expected by the algorithm. The management system 110 may alert the user if a condition changes (e.g., a change in contrast agent concentration) when there is no expected change in the acoustic data. Similarly, during a proper contrast agent injection, the sound spectrum and / or the location in space of the sound source may remain relatively constant (e.g., the catheter tip does not move except at the very beginning of the injection). For example, the management system 110 may allow for modest spectral changes or movement at the beginning of the injection or when there is a change in total volumetric or mass flow rate, but if the initial movement meets a threshold movement level at the relevant time, the management system 110 may provide movement instructions to the user (e.g., as part of the patient's health level, etc.). In such examples, the management system 110 may use parameters such as the patient's venous status to set one or more thresholds used to determine one or more alerts, e.g., setting lower thresholds for more at-risk patients. In such examples, the management system 110 may use multiple data streams and sub-algorithms as "double checks" against each other, e.g., only alerting a user if two or more sub-algorithms indicate an alert, thereby reducing the likelihood of false alarms. Other methods of combining sub-algorithm results, such as response surfaces and non-linear functions, may also be utilized.

[0194] As shown in FIG. 3 , in step 310, process 300 includes determining a test prediction. The test prediction determination in step 310 may be optional, e.g., performing a test injection and determining sensor data during the test injection may be optional. For example, management system 110 may determine the test prediction based on sensor data determined during a test injection administered to the patient before fluid injection (e.g., before contrast injection). As an example, the test prediction may include the probability that the patient will experience extravasation in response to a fluid injection (e.g., contrast injection). For example, the patient's intravenous access may be checked in a standardized manner, a standardized stepwise saline test injection for the patient (manually and / or mechanically) is performed, and uncomplicated high-flow and high-pressure contrast application is predicted by determining sensor data related to the patient during the test injection using sensor system 106. In such an example, changes in the patient's sound or measured parameters (e.g., changes in blood vessels and / or tissues proximate to the injection site) may be determined under controlled, dynamic, low-flow and low-pressure conditions of the test injection. For example, the management system 110 may determine test predictions (e.g., probability of extravasation under high flow rates and pressures of contrast injection) and / or assess the sufficiency of the test injection based on turbulence sounds (caused by fluid entering the blood vessels) measured by at least one sound or vibration sensor positioned proximate to the injection site, changes in temperature, changes in oxygenation levels, comparison with the load pressure of the injector 152, etc.

[0195] The management system 110 can apply algorithms or aspects of algorithms, which may be adaptations and / or implementations of professional society guidelines and / or hospital procedures into computer code, to patient data and / or sensor data associated with the patient to determine test predictions for the patient. In such examples, different hospitals may have different algorithms or aspects of one or more algorithms based on local preferences, practices, countries, and / or other factors associated with different hospitals. In some non-limiting embodiments or aspects, the management system 110 can use scoring tables, such as those described herein above with respect to the examples of Tables 1 through 4, to determine test predictions for the patient based on one or more parameters (e.g., change in temperature, change in oxygenation level, etc.) of the patient data and / or sensor data associated with the patient.

[0196] In some non-limiting embodiments, the management system 110 can generate the test predictive machine learning model in the same or similar manner as the initial risk predictive machine learning model (e.g., as described herein). In some non-limiting embodiments or aspects, the test predictive machine learning model may differ from the initial risk predictive machine learning model. For example, the inputs provided to or outputs provided by the test predictive machine learning model may differ from the inputs provided to or outputs provided by the initial risk predictive machine learning model. As an example, the test predictive model may be designed to receive as input patient data and / or sensor data (e.g., one or more parameters of the patient data and / or sensor data measured during a test injection, etc.) and provide as output a prediction (e.g., a probability, a binary output, a yes-no output, a score, a prediction score, a classification, etc.) regarding whether a patient has or will experience extravasation in response to a contrast injection.

[0197] 3 , in step 312, the process 300 includes providing a test prediction. Providing the test prediction in step 312 may be optional, e.g., performing a test injection and determining sensor data during the test injection may be optional. For example, the management system 110 may provide the user device 108 with a test prediction that includes a probability that the patient will experience an adverse event, e.g., extravasation, in response to a fluid injection (e.g., a contrast injection, etc.). As an example, the user device 108 may display the test prediction to a user (e.g., a physician, etc.) before a fluid injection (e.g., a contrast injection, etc.) is administered to the patient and an imaging study is performed.

[0198] In some non-limiting embodiments or aspects, the exam prediction may further include at least one of a prompt to administer medication to the patient before contrast injection, a prompt to adjust an injection protocol for the contrast injection and / or an imaging protocol for the imaging scan, a prompt to prepare the patient before contrast injection, a prompt to consult a specialist regarding a predicted extravasation, a prompt to observe and / or follow up with the patient after the contrast injection and / or the imaging scan, or any combination thereof. For example, in response to determining the exam prediction (e.g., in response to determining an exam prediction that includes a probability that the patient will experience an extravasation that meets a threshold probability, etc.), the management system 110 may determine and recommend actions that a user (e.g., a medical professional, etc.) can take to reduce the probability that the patient will experience an extravasation. As an example, the management system 110 may consult a lookup table and / or apply an algorithm (e.g., a machine learning model, etc.) to the test prediction and / or the patient data and / or sensor data used to generate the test prediction to determine and provide to the user one or more prompts or recommendations that may reduce the probability that the patient will experience an extravasation.

[0199] In some non-limiting embodiments or aspects, in response to determining an examination prediction (e.g., in response to determining an examination prediction including, for example, a probability that a patient will experience an extravasation that meets a threshold probability), management system 110 can automatically control and / or adjust one or more operations of fluid injection system 102 and / or imaging system 104. For example, management system 110 may automatically adjust an injection protocol for a contrast injection (e.g., adjusting a maximum flow rate, adjusting a maximum pressure, etc.) and / or an imaging protocol for an imaging scan (e.g., adjusting a scan time, etc.). As an example, management system 110 may consult a lookup table and / or apply an algorithm (e.g., a machine learning model, etc.) to the examination prediction and / or the patient data and / or sensor data used to generate the examination prediction to determine one or more automatic adjustments to the injection protocol and / or imaging protocol that may reduce the probability that a patient will experience an extravasation.

[0200] As shown in FIG. 3 , in step 314, process 300 includes determining at least one of a current risk prediction and a health level of the patient. For example, management system 110 may determine at least one of a current risk prediction and a health level of the patient based on sensor data determined after a fluid injection (e.g., a contrast injection, etc.) has been initiated (e.g., during and after the fluid injection, simultaneously with starting the fluid injection, etc.). As an example, the current risk prediction may include a probability that the patient will experience at least one adverse event in response to the fluid injection (e.g., a probability that the patient is currently experiencing at least one adverse event, a probability that the patient will experience at least one event in the future, etc.). In such an example, a probability that the patient will experience at least one adverse event that meets at least one threshold probability (e.g., a 90 percent probability, a 100 percent probability, etc.) may indicate that the patient is currently experiencing at least one adverse event, and / or a probability that the patient will experience at least one adverse event that does not meet at least one threshold probability may indicate a probability that the patient will experience at least one adverse event in the future. In some non-limiting embodiments or aspects, the management system 110 may further determine at least one of a current risk prediction and health level of the patient during and / or after the fluid infusion based on patient data associated with the patient.

[0201] In some non-limiting embodiments or aspects, the management system 110 can determine (e.g., during fluid injection) at least one of the patient's current risk prediction and health level based on sensor data determined during fluid injection. For example, even in the case of a test prediction indicating a low probability of patient extravasation for contrast injection, extravasation may still occur during contrast injection due to, for example, an intravenous access being incorrectly placed outside the vein, misalignment and / or kinking after placement or during contrast injection, and / or patient movement and / or rupture of the patient's blood vessels due to high pressure and high flow conditions.

[0202] In some non-limiting embodiments or aspects, the management system 110 may determine at least one of the patient's current risk prediction and health level based on sensor data determined after the fluid infusion is completed (e.g., determined after the fluid infusion is completed, during and / or after the imaging scan, etc.).

[0203] The management system 110 may apply an algorithm or aspects of an algorithm, which may be an adaptation and / or implementation of professional society guidelines and / or hospital procedures into computer code, to the sensor data associated with the patient to determine at least one of the patient's current risk prediction and health level. In such an example, different hospitals may have different algorithms or aspects of one or more algorithms based on local preferences, practices, countries, and / or other factors associated with the different hospitals. In some non-limiting embodiments or aspects, the management system 110 may use a scoring table, such as those described herein above with respect to the examples of Tables 1 through 4, to determine at least one of the patient's current risk prediction and health level based on one or more parameters of the sensor data associated with the patient (e.g., change in temperature, change in oxygenation level, movement level, heart rate, etc.).

[0204] In some non-limiting embodiments or aspects, the management system 110 can generate the current risk predictive machine learning model in the same or similar manner as the initial risk predictive machine learning model and / or the test predictive machine learning model (e.g., as described herein). In some non-limiting embodiments, the current risk predictive machine learning model can differ from the initial predictive machine learning model and / or the test predictive machine learning model. For example, the inputs provided to and / or outputs provided by the current risk predictive machine learning model may differ from the inputs provided to and / or outputs provided by the initial predictive machine learning model and / or the test predictive model. As an example, the current risk predictive machine learning model may be designed to receive as input sensor data (e.g., one or more parameters of the sensor data measured during and / or after a contrast injection, etc.) and provide as output a prediction (e.g., a probability, a binary output, a yes-no output, a score, a prediction score, a classification, etc.) regarding whether a patient will experience at least one adverse event in response to a fluid injection. As an example, a current risk prediction machine learning model may be designed to receive sensor data as input (e.g., one or more parameters of the sensor data measured during and / or after contrast injection) and provide as output a classification (e.g., a probability, a binary output, a yes-no output, a score, a prediction score, a classification, etc.) regarding the patient's health level.

[0205] 3 , in step 316, the process 300 includes providing the patient's current risk prediction and / or health level. For example, the management system 110 may provide the patient's current risk prediction and / or health level to the user device 108 during and / or after the fluid infusion (e.g., after the fluid infusion has begun). As an example, the management system 110 may provide the patient's current risk prediction and / or health level to the user device 108 during the fluid infusion, and / or the user device 108 may display the current risk prediction and / or health level during the fluid infusion to a user (e.g., a physician, etc.) during the fluid infusion. As an example, the management system 110 may provide the patient's current risk prediction and / or health level to the user device 108 after the fluid infusion, and / or the user device 108 may display the patient's current risk prediction and / or health level after the fluid infusion to a user (e.g., a physician, etc.) after the fluid infusion.

[0206] In some non-limiting embodiments or aspects, the current risk prediction may include an alert generated in response to and / or associated with at least one of catheter tip movement meeting a threshold movement, a change in fluid concentration meeting a threshold change, a fluid temperature meeting a threshold temperature, a fluid flow rate meeting a threshold magnitude, or any combination thereof. For example, the management system 110 may provide an alert with the current risk prediction in response to the current risk prediction meeting at least one threshold probability that the patient will experience at least one adverse event, e.g., to alert a user of a condition that may lead to the patient experiencing at least one event.

[0207] In some non-limiting embodiments or aspects, the current risk prediction may include visualization of changes to the patient's tissues associated with, caused by, and / or reflecting fluid influx from the fluid injection.

[0208] In some non-limiting embodiments or aspects, in response to determining a current risk prediction that meets at least one threshold level (e.g., indicating that the patient is experiencing and / or will experience an adverse event (e.g., extravasation, etc.) during the fluid injection (and / or test injection)), the management system 110 can automatically control the fluid injection system 102 to stop the fluid injection (and / or test injection) (e.g., controlling the injector 152 to stop the injection or delivery of contrast agent or fluid to the patient) and / or control or cause the imaging system 104 to abort the imaging procedure, thereby protecting the patient from unproductive radiation exposure due to a possible lack of or insufficient contrast agent required for the procedure and / or patient movement or other imaging disturbances associated with the adverse event.

[0209] In some non-limiting embodiments or aspects, in step 316, process 300 may cause management system 110 to receive feedback from a user or operator that the management system 110 may use to update and / or adjust one or more of the algorithms described herein with respect to steps 308-314. For example, a user may inform management system 110 whether an assessment or determination of a successful infusion or occurrence of an adverse event made by management system 110 is correct or if reality is inconsistent with the assessment or determination, thereby allowing one or more algorithms to be improved as more experience is gained in actual practice with the wide variety of patients encountered.

[0210] 5A, which is a flowchart of a non-limiting embodiment or aspect of a process 500 for assessing the normality or abnormality of a patient and / or an infusion, thereby protecting the patient's health before, during, and / or after a fluid infusion. In some non-limiting embodiments or aspects, one or more of the steps of process 500 may be performed (e.g., completely, partially, etc.) by management system 110 (e.g., one or more devices of management system 110, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 500 may be performed (e.g., completely, partially, etc.) by a user or another device or group of devices separate from or including management system 110, such as fluid injection system 102 (e.g., one or more devices of fluid injection system 102), imaging system 104 (e.g., one or more devices of imaging system 104), sensor system 106 (e.g., one or more devices of sensor system 106), user device 108 (e.g., one or more devices of the system of user device 108), and / or auxiliary system 112 (e.g., one or more devices of auxiliary system 112).

[0211] 5A, in step 502, process 500 includes capturing an image of the patient. For example, sensor system 106 may capture the image of the patient. As an example, sensor system 106 may include non-contact sensor 164b including an image capture device (e.g., a camera, etc.), and the sensor data captured by the image capture device may include multiple images (e.g., video streams, etc.) of the patient captured over a period of time (e.g., images including the patient's infusion site and / or areas proximate to and / or surrounding the patient's infusion site, etc.). In some non-limiting embodiments or aspects, step 502 of process 500 may be performed as part of step 308 of process 300 and / or in the same or similar manner. In such an example, the at least one adverse event may include extravasation.

[0212] 5A, in step 504, process 500 includes processing the images to emphasize, magnify, or amplify changes in color and / or motion between the images. For example, management system 110 may process multiple images of a patient captured over a period of time to emphasize, magnify, or amplify changes in at least one of color and motion (e.g., low-frequency motion, high-frequency sound-based motion, or vibration) between the multiple images (e.g., between one or more objects and / or regions within the multiple images). As an example, management system 100 may process the multiple images using Eulerian Video Magnification techniques, such as those described in the article "Eulerian Video Magnification for Revealing Subtle Changes in the World" by Wu et al., published in July 2012, the disclosure of which is incorporated herein by reference in its entirety.

[0213] 5A , in step 506, process 500 includes displaying an image including the enhanced changes. For example, management system 110 may display multiple images including the enhanced changes to a user (e.g., via user device 108, etc.). Thus, a user (e.g., a physician, etc.) viewing the enhanced images can more easily detect whether the patient is experiencing extravasation due to the enhanced changes in color and / or motion within the images. In some non-limiting embodiments or aspects, step 506 of process 500 may be performed as part of, and / or in the same or similar manner as, step 312 and / or step 316 of process 300. In some non-limiting embodiments or aspects, the enhanced images are not displayed to the user, and / or management system 110 processes and evaluates the enhanced images internally.

[0214] As shown in FIG. 5A , in step 508, process 500 includes determining a current risk prediction and / or health level for the patient. For example, management system 110 may determine whether the infusion is progressing as expected, whether the patient is experiencing discomfort, and / or whether the patient is experiencing or is likely to experience an extravasation or another adverse event. As an example, management system 110 may determine a current risk prediction (e.g., extravasation probability, etc.) and / or a health level for the patient based on the enhanced changes. As an example, management system 110 may apply an algorithm or aspects of one or more algorithms (e.g., a machine learning model, etc.) to the plurality of images including the enhanced changes to determine a current risk prediction (e.g., extravasation probability, etc.) and / or a health level for the patient. In some non-limiting embodiments or aspects, step 508 of process 500 can be performed as part of step 310 and / or step 314 of process 300 and / or in the same or similar manner.

[0215] As shown in FIG. 5A , in step 510, process 500 includes automatically responding to the patient's current risk prediction and / or health level. For example, management system 110 may perform one or more operations in response to determining that the infusion is progressing as expected, determining that the patient is experiencing discomfort, or determining that the patient is experiencing extravasation or another adverse event. For example, management system 110 may perform one or more desired steps or actions in response to determining that an adverse event is occurring, is about to occur, or is likely to occur (e.g., in response to a current risk prediction that includes a probability of meeting at least one threshold probability). By way of example, the one or more desired steps or actions may be set by a user, a hospital, or any suitable body through management system 110. Such actions may include alerting an operator for evaluation or decision, automatically controlling injector 152 to slow the injection rate of the injection, automatically controlling injector 152 to pause the injection, and / or automatically stopping a fluid injection (e.g., contrast injection, etc.). As an example, the management system 110 may automatically stop fluid injection (e.g., control the fluid injection system 102 to stop the flow and / or delivery of fluid or contrast agent) in response to determining that the patient is experiencing an extravasation or another adverse event and / or in response to determining that the patient's health status level meets at least one threshold level.

[0216] 5B, which is a flowchart of a non-limiting embodiment or aspect of a process 550 for assessing the normality or abnormality of a patient and / or an infusion, thereby assessing and / or protecting the patient's health before, during, and / or after a fluid infusion. In some non-limiting embodiments or aspects, one or more of the steps of process 550 may be performed (e.g., completely, partially, etc.) by management system 110 (e.g., one or more devices of management system 110, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 550 may be performed (e.g., completely, partially, etc.) by a user or another device or group of devices separate from or including management system 110, such as fluid injection system 102 (e.g., one or more devices of fluid injection system 102), imaging system 104 (e.g., one or more devices of imaging system 104), sensor system 106 (e.g., one or more devices of sensor system 106), user device 108 (e.g., one or more devices of the user device 108 system), and / or auxiliary system 112 (e.g., one or more devices of auxiliary system 112).

[0217] 5B , in step 552, process 500 includes capturing an image of the patient. For example, sensor system 106 may capture the image of the patient. As an example, sensor system 106 may include non-contact sensor 164b including an image capture device (e.g., a camera, an IR camera, etc.), and the sensor data determined by the image capture device may include multiple images (e.g., multiple IR images, etc.) of the patient (e.g., of the patient's infusion site and / or an area proximate to and / or surrounding the patient's infusion site) captured over a period of time. In some non-limiting embodiments or aspects, step 552 of process 500 may be performed as part of step 308 of process 300 and / or in the same or similar manner. In such an example, the at least one adverse event may include extravasation.

[0218] As shown in FIG. 5B , in step 554, process 500 includes processing the images to determine a difference in absorption spectrum between a first location and a second location on the patient, and / or between two time points at the same location. For example, sensor system 106 may include an image capture device including an infrared (IR) camera, and the plurality of images may include a plurality of IR images. As an example, management system 110 may process the plurality of IR images to determine a difference in absorption spectrum between a first location on the patient (e.g., a location associated with the patient's blood vessel) and a second location on the patient (e.g., a location associated with the patient's tissue outside the patient's blood vessel) within the plurality of images. For example, because fluids injected via contrast injection are typically cooler than the patient's body temperature, a longer wavelength IR spectrum may be used to assess temperature. As an example, management system 110 may process the plurality of images using the method described in U.S. Patent Application Publication No. 2006 / 0173360 A1, filed January 7, 2005, the contents of which are incorporated herein by reference in their entirety.

[0219] 5B , in step 556, process 550 includes displaying an image including the difference in absorption spectra and / or the absorption spectra over time. For example, management system 110 may display multiple images including the difference in absorption spectra to a user in a manner visible to the user (e.g., via user device 108, etc.). Thus, a user viewing the images (e.g., a physician, etc.) can more easily detect whether the patient is experiencing a normal infusion or extravasation based on the difference in absorption spectra depicted in the displayed images. As an example, the first location of the patient may include a blood vessel or vein of the patient, and the second location of the patient may include tissue of the patient surrounding the blood vessel or vein of the patient. In some non-limiting embodiments or aspects, step 556 of process 550 may be performed as part of, and / or in the same or similar manner as, step 312 and / or step 316 of process 300.

[0220] As shown in FIG. 5B , in step 558, process 550 includes process 500 determining the patient's current risk prediction and / or health level. For example, management system 110 may determine whether the infusion is progressing as expected, whether the patient is experiencing discomfort, and / or whether the patient is experiencing extravasation and / or another adverse event. For example, management system 110 may determine whether the patient is experiencing extravasation and / or another adverse event based on a difference in absorption spectra between a first location on the patient and a second location on the patient. As an example, management system 110 may compare the difference in absorption spectra to one or more thresholds to determine whether the patient is experiencing extravasation and / or another adverse event and / or to determine the patient's health level. In some non-limiting embodiments or aspects, step 558 of process 550 may be performed as part of, and / or in the same or similar manner as, step 310 and / or step 314 of process 300.

[0221] As shown in FIG. 5B , in step 560, process 560 includes automatically responding to the patient's current risk prediction and / or fitness level. For example, management system 110 may automatically perform an operation in response to a determination that the infusion is progressing as expected, a determination that the patient is experiencing discomfort, or a determination that the patient is experiencing an extravasation or another adverse event. For example, management system 110 may perform one or more desired steps or actions in response to a determination that an extravasation or another adverse event is occurring, is likely to occur, or has a high probability of occurring (e.g., in response to a current risk prediction that includes a probability of meeting at least one threshold probability). The desired steps or actions can be set by a user, a hospital, and / or an appropriate body through management system 110. Such actions can include alerting an operator for evaluation or decision, automatically controlling injector 152 to slow the infusion rate, automatically controlling injector 152 to pause the infusion, and / or automatically stopping fluid infusion. For example, the management system 110 may automatically stop fluid injection (e.g., control the fluid injection system 102 to stop the flow and / or delivery of fluid or contrast agent) in response to determining that the patient is experiencing an extravasation or another adverse event and / or in response to determining that the patient's health status level meets at least one threshold level.

[0222] 6, which is a flow diagram of non-limiting embodiments or aspects of a process 600 for assessing the normality or abnormality of a patient and / or an infusion, thereby assessing and / or protecting the patient's health for a fluid infusion. In some non-limiting embodiments or aspects, one or more of the steps of process 600 may be performed (e.g., completely, partially, etc.) by management system 110 (e.g., one or more devices of management system 110, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 600 may be performed (e.g., completely, partially, etc.) by a user or another device or group of devices separate from or including management system 110, such as fluid injection system 102 (e.g., one or more devices of fluid injection system 102), imaging system 104 (e.g., one or more devices of imaging system 104), sensor system 106 (e.g., one or more devices of sensor system 106), user device 108 (e.g., one or more devices of the system of user device 108), and / or auxiliary system 112 (e.g., one or more devices of auxiliary system 112).

[0223] As shown in FIG. 6 , at step 602, process 600 includes inducing a sound or vibration signal in a fluid delivered to a patient during a fluid injection (and / or test injection). For example, referring also to FIG. 7 , an implementation 700 of fluid injector 152 may include a sound-generating device 702 (e.g., an oscillator, speaker, vibrator, whistle, etc.) connected to at least one of an injector, a syringe, and a fluid path that delivers fluid to a patient during a fluid injection (e.g., a contrast injection, etc.) (and / or test injection). As an example, sound-generating device 702 may induce a sound signal in a fluid (e.g., contrast agent, saline, etc.) delivered to a patient during a fluid injection (and / or test injection) into a patient, thereby inducing or transmitting sound waves or pulses in the patient's vasculature, surrounding tissue, and / or blood to improve assessment of injection health, injection progress, and / or extravasation detection. While shown in FIG. 7 as being connected to a contrast injector used for CT imaging procedures, non-limiting embodiments or aspects are not so limited, and the sound-generating device 702 may be incorporated into any type of fluid delivery device. In another non-limiting embodiment or aspect of the present disclosure, the sound-generating device 702 may generate sound as the fluid being delivered to the patient flows through the fluid pathway and / or the patient. A special device or element in the fluid pathway may be used to generate this sound, similar to how a whistle generates sound in the air as air flows over it. One phenomenon that may be used is vortex shedding. With vortex shedding, the frequency of the sound may depend on the fluid flow rate, velocity, and other characteristics. This has the advantage of providing the management system 110 with a qualitative and potentially quantitative indication of one or more local characteristics of the infusion, which can be used to assess the health, progression, and / or risk of an adverse event. Sound or vibration may include any mechanical oscillation or vibration phenomenon of any duration, whether within or outside the normal range of human hearing, externally generated, imposed and / or inserted, or inherently created and / or generated.

[0224] As shown in step 604 of FIG. 6 , process 600 includes measuring sound or vibrations in an area or portion of the patient. For example, sensor system 106 may measure sound or vibrations in the patient (e.g., simultaneously with inducing a sound signal into the fluid during fluid infusion). As an example, sensor system 106 may include a sound or vibration sensor (e.g., implemented in contact sensor device 400 positioned proximate an infusion site on the patient), and sensor system 106 may use the sound or vibration sensor to measure, for example, at least one of the frequency and amplitude of the sound or vibrations in the patient (e.g., proximate an infusion site on the patient) as sensor data related to the patient. In such an example, the sound waves or pulses induced in the patient may improve the quality or signal-to-noise ratio of the sound or vibration signal captured by the sound or vibration sensor, which may allow management system 110 to more easily determine extravasation during fluid infusion based on the sound or vibrations captured by the sound or vibration sensor. For example, the frequency and / or amplitude of the sound signal may be adjusted to improve detection by the sound or vibration sensor. In some non-limiting embodiments or aspects, step 604 of process 600 may be performed as part of step 308 of process 300 and / or in the same or similar manner.

[0225] As shown in FIG. 6 , in step 606, process 600 includes determining the patient's current risk prediction and / or health level. For example, management system 110 may determine whether the infusion is progressing as expected, whether the patient is experiencing discomfort, and / or whether the patient is experiencing extravasation or another adverse event. For example, management system 110 may determine the status of the infusion (e.g., the patient's current risk prediction and / or health level) based on at least one of the frequency, amplitude, apparent center or location of the sound, and / or changes over time in any of the patient's measured sound- or vibration-related characteristics. As an example, infused fluid that pools under the patient's skin during extravasation may generate a different sound signature compared to fluid flowing through the patient's vascular system, which may manifest as an increase in localized sound loudness where the fluid pools and / or a shift in sound frequency due to the Doppler effect. In such examples, management system 110 may determine whether the patient is experiencing extravasation by comparing the measured frequency and / or amplitude to one or more thresholds, a library of known frequencies and / or amplitudes, and / or reference frequencies and / or amplitudes determined during a test injection. Thus, non-limiting embodiments or aspects of the present disclosure may address the signal-to-noise limitations of passively monitoring fluid injections and associated fluid extravasation using an external sensor array by generating and measuring an enhanced signal that is more detectable in the patient's vasculature. In some non-limiting embodiments or aspects, step 606 of process 600 may be performed as part of, and / or in the same or similar manner as, step 310 and / or step 314 of process 300.

[0226] As shown in FIG. 6 , in step 608, process 600 includes automatically responding to the patient's current risk prediction and / or fitness level. For example, management system 110 may automatically perform one or more operations in response to determining that the infusion is progressing as expected, determining that the patient is experiencing discomfort, or determining that the patient is experiencing an extravasation or another adverse event. For example, management system 110 may perform one or more desired steps or actions in response to determining that an extravasation has occurred, is about to occur, or is likely to occur (e.g., in response to a current risk prediction that includes a probability of meeting at least one threshold probability). The desired steps or actions can be set by a user, a hospital, or an appropriate body through management system 110. Such actions can include alerting an operator for evaluation or decision, automatically controlling injector 152 to slow the injection rate of the injection, automatically controlling injector 152 to pause the injection, and / or automatically stopping the fluid injection. For example, the management system 110 may automatically stop fluid injection (e.g., control the fluid injection system 102 to stop the flow and / or delivery of fluid or contrast agent) in response to determining that the patient is experiencing an extravasation or another adverse event and / or in response to determining that the patient's health status level meets at least one threshold level.

[0227] Medical imaging can be a stressful experience for patients. Stress can begin when a patient learns they have a condition that requires further “testing.” The term “testing” can imply a potentially very serious illness to the patient, which can be frightening because the patient may not know what the test entails. Stress can increase when a specific test is prescribed and the patient receives secondhand and incorrect information about the medical imaging to be performed. Medical imaging can also be a potentially painful experience for patients, for example, if the patient experiences an extravasation. However, focus on detecting and / or reducing a specific adverse event, such as an extravasation, can overlook more general patient distress and causes of patient distress. Therefore, there is a need for systems and methods that provide solutions for assessing or determining general patient distress and improving the level of patient care in response.

[0228] 9, which is a flowchart of a non-limiting embodiment or aspect of a process 900 for protecting a patient's health for fluid infusion. In some non-limiting embodiments or aspects, one or more of the steps of process 900 may be performed (e.g., completely, partially, etc.) by management system 110 (e.g., one or more devices of management system 110, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 900 may be performed (e.g., completely, partially, etc.) by a user or another device or group of devices separate from or including management system 110, such as fluid injection system 102 (e.g., one or more devices of fluid injection system 102), imaging system 104 (e.g., one or more devices of imaging system 104), sensor system 106 (e.g., one or more devices of sensor system 106), user device 108 (e.g., one or more devices of the user device 108 system), and / or auxiliary system 112 (e.g., one or more devices of auxiliary system 112).

[0229] 9 , in step 902, process 900 includes determining a change in one or more parameters of sensor data associated with the patient over a period of time. For example, management system 110 may determine a change in one or more parameters of sensor data associated with the patient over a period of time. As an example, contact sensor device 800 (and / or contact sensor device 400) may determine sensor data (e.g., heart rate, oxygen saturation, skin resistivity, movement or exercise level, temperature proximate the injection site, etc.) (e.g., using a pulse oximeter, skin resistance sensor, accelerometer, temperature sensor, etc.) and transmit the sensor data to management system 110. Alternatively, or in addition, non-contact sensor 164b may measure patient movement, flushing, swelling, and / or any other measurement described herein as measured by non-contact sensor 164b and transmit the sensor data to management system 110.

[0230] As shown in FIG. 9 , in step 904, process 900 includes determining a patient distress level associated with the patient. For example, management system 110 may determine whether the patient is comfortable or in distress, or potentially in distress. For example, management system 110 may determine whether the patient is in distress based on sensor data determined after fluid infusion has begun. As an example, management system 110 may determine changes in one or more parameters of the sensor data over a period of time, compare the changes in the one or more parameters to at least one threshold change, and determine the patient's distress level and / or that the patient is in distress in response to changes in the one or more parameters that meet the at least one threshold change. In some non-limiting embodiments or aspects, step 904 of process 900 may be performed as part of, and / or in the same or similar manner as, steps 310 and / or 314 of process 300.

[0231] 9 , in step 906, process 900 includes providing an alert. For example, in response to determining that the patient is in pain (e.g., in response to the patient's pain level meeting at least one threshold level), the management system 110 may provide an alert to the user device 108 indicating that the patient is in pain. As an example, the user device 108 may display an alert to a user (e.g., a physician, etc.), which may include information and / or data related to the type of discomfort and / or pain experienced by the patient. For example, the user may take one or more actions to help improve the patient's comfort, such as to reduce the patient's pain level. In some non-limiting embodiments or aspects, step 906 of process 900 may be performed as part of, and / or in the same or similar manner as, step 312 and / or step 316 of process 300.

[0232] 9 , in step 908, process 900 includes controlling the fluid injection system and / or the imaging system. For example, in response to determining that the patient is in distress (e.g., in response to the patient's distress level meeting at least one threshold level), management system 110 may automatically control at least one of: (i) the fluid injection system to modify, pause, or stop the fluid medium injection (and / or test injection), and (ii) the imaging system to adjust the timing of imaging operations (e.g., to delay or pause imaging until management system 110 determines that the patient is no longer in distress). In some non-limiting embodiments or aspects, step 908 of process 900 may be performed as part of, and / or in the same or similar manner as, step 312 and / or step 316 of process 300.

[0233] In some non-limiting embodiments or aspects, the management system 110 can automatically or semi-automatically take action to control one or more devices of the fluid infusion system 102, the imaging system 104, and / or the sensor system 106 to distract a patient experiencing distress. For example, in response to determining that a patient is in distress (e.g., in response to the patient's distress level meeting at least one threshold level), the management system 110 can automatically control a tactile device (e.g., the patient's bed or table, the contact sensor devices 400 and / or 800, a vibrator, an acupressure device, etc.), a speaker, and / or a display to distract the patient (e.g., distract a patient experiencing nausea, distract a patient from an IV placement, etc.).

[0234] 10, which is a flowchart of non-limiting embodiments or aspects of a process 1000 for promoting and / or protecting patient health for fluid infusion. In some non-limiting embodiments or aspects, one or more of the steps of process 1000 may be performed (e.g., completely, partially, etc.) by management system 110 (e.g., one or more devices of management system 110, etc.). In some non-limiting embodiments or aspects, one or more of the steps of process 1000 may be performed (e.g., completely, partially, etc.) by a user or another device or group of devices separate from or including management system 110, such as fluid injection system 102 (e.g., one or more devices of fluid injection system 102), imaging system 104 (e.g., one or more devices of imaging system 104), sensor system 106 (e.g., one or more devices of sensor system 106), user device 108 (e.g., one or more devices of the system of user device 108), and / or auxiliary system 112 (e.g., one or more devices of auxiliary system 112).

[0235] 10, in step 1002, process 1000 includes providing breathing, posture (e.g., stillness, holding posture, etc.), and / or other behavioral guidance. For example, management system 110 may, using at least one processor, control at least one of a light (e.g., a light on injector 152, etc.), a display (e.g., injector user interface 156, etc.), a speaker, and a tactile device (e.g., a patient bed or table, contact sensor devices 400 and / or 800, etc.) to provide at least one of visual, audio, and tactile instructions to guide the patient's breathing (e.g., before, during, or after contrast injection).

[0236] The ability of a patient to control their breathing and remain still is useful in radiological imaging procedures. The design of power injectors is typically highly focused on the needs and interactions of the user (e.g., a medical professional). However, less attention is typically paid to the patient and their interactions and perceptions, which may be overlooked during the development of medical devices. In a typical procedure, respiratory guidance is typically instructed by a medical professional (e.g., via an intercom). Furthermore, many medical environments are perceived as cold (e.g., lacking emotionality and warmth). There are many patients who are overwhelmed by stress and anxiety, leading to and undergoing medical procedures.

[0237] Improving meditation practice using illustrations, lighting, and sound within a medical environment, such as a radiology suite scanning room, can transform the patient's experience throughout the procedure. Patients typically lie supine during imaging procedures, primarily looking at the ceiling and walls of the scanner's inner bore. In the absence of direct line of sight, the use of colored lighting (e.g., blue = calm, etc.) and ambient sounds (e.g., calming voice prompts and white noise) can reach the patient to help them relax. Also referring to FIG. 11 , FIG. 11 illustrates a non-limiting embodiment or aspect of instructions 1100 for guiding a patient's breathing, which may be delivered in animated form to the patient in the scanning room via a display (e.g., an injector display, an imager display, a user device, etc.). As shown in FIG. 11 , an animated halo (e.g., on a graphical user interface, as lighting from the injector 152 and / or imager 158) indicating shallow and deep breathing with expansion and contraction may be displayed to the patient.

[0238] As the halo expands from the starting state, audio instructions may prompt the patient to take a deep breath, illumination from the injector intensifies, and colored lights shine into the scanner bore. At the fully expanded state, the display may remain fully illuminated as an audio prompt prompts the patient to hold their breath. Once the patient is able to exhale (e.g., due to the end or pause of an imaging operation), the halo may shrink back to the starting state and illumination may become less intense. Optionally, the halo may include a countdown number to inform the patient when they can breathe again, thereby providing information to the patient about what is expected of them. Thus, visual outputs accompanied by audio prompts may be used to demonstrate proper breathing for patients undergoing and / or about to undergo a radiological procedure, such as a contrast injection, imaging scan, etc., to calm the patient prior to the procedure, and / or to provide respiratory guidance during the procedure. In this manner, visual outputs and audio prompts can be used to calm patients from the radiology scanning room by using mood lighting, soothing audio prompts, ambient noise, and / or haptic feedback (e.g., a vibrator in contact sensor device 400 and / or 800 that vibrates in time with animation), which are commonly used in meditation to relax patients experiencing tension and anxiety. Variations in the guidance and / or its presentation can be used to accommodate different patients (e.g., everyday, pediatric, cognitively impaired, phobic, etc.).

[0239] In some non-limiting embodiments or aspects, instructions for guiding the patient's breathing can be provided before and / or outside of the patient's entry into the procedure or scanning room. For example, the instructions may be used as an educational tool to inform and / or prepare the patient and may be provided to the patient via a patient care portal or application for the system as described herein. In some non-limiting embodiments or aspects, the guidance can be used to practice before the procedure, if desired, in the imaging room. The management system 110 in combination with sensors 164a and 164b can assess the patient's ability to follow the planned instructions. If the management system 110 determines that the patient is unable to follow the planned instructions, the contrast injection and imaging procedure may be modified to accommodate the patient.

[0240] In some non-limiting embodiments or aspects, the management system 110 may adjust at least one of visual, audio, and haptic instructions for guiding the patient's breathing based on the timing of the imaging operation of the imaging system 104. For example, the management system 110 may automatically adjust instructions for instructing the patient to hold their breath when the imaging system 104 (e.g., imager 158, etc.) is actively imaging the patient.

[0241] As shown in FIG. 10 , in step 1004, process 1000 includes determining a pain level associated with the patient. For example, management system 110 may determine whether the patient is comfortable, in pain, or in pain (e.g., whether the patient's pain level meets at least one threshold level, etc.). For example, management system 110 may determine the patient's pain level and / or whether the patient is in pain based on sensor data determined after fluid infusion has begun (e.g., based on sensor data determined via contact sensor device 400, based on sensor data determined via contact sensor device 800, etc.). As an example, management system 110 may determine changes in one or more parameters of the sensor data over a period of time, compare the changes in the one or more parameters to at least one threshold change, and determine the patient's pain level and / or whether the patient is in pain in response to changes in the one or more parameters that meet at least one threshold change. In some non-limiting embodiments or aspects, step 1004 of process 1000 may be performed as part of step 904 of process 900 and / or in the same or similar manner.

[0242] In some non-limiting embodiments or aspects, the management system 110 may determine whether the patient's fluid injection and / or imaging scan meets one or more compliance thresholds (e.g., thresholds related to patient movement during the imaging scan, thresholds related to the quality of the images acquired during the imaging scan, etc.) based on sensor data determined after the fluid injection has begun. For example, the management system 110 may process sensor data related to patient movement / motion during the scan and artifact generation in the images of the scan to determine the effect of table motion.

[0243] 10 , in step 1006, process 1000 includes adjusting behavioral guidance. For example, management system 110 may adjust at least one of visual, audio, and tactile instructions to guide the patient's breathing and / or positioning in response to determining that the patient is in pain (e.g., in response to the patient's pain level meeting at least one threshold level). As an example, management system 110 may provide audio and / or visual feedback to the patient related to the patient's measured breathing and / or adjust the timing of instructions and / or imaging scan time corresponding to the instructions. In some non-limiting embodiments or aspects, step 1006 of process 1000 may be performed as part of, and / or in the same or similar manner as, steps 312 and / or 316 of process 300.

[0244] In some non-limiting embodiments or aspects, the contact sensor device 400, 800 may remain in contact with the patient for a period of time after fluid infusion to provide information to the management system 110, allowing the management system 110 to assess the patient's ongoing post-infusion health and monitor for possible delayed adverse events, such as delayed allergic reactions. The management system 110 may notify the user (e.g., via the user device 108, etc.) of the patient's condition and can recommend additional monitoring time and / or other actions if the patient's condition or health is not optimal or sufficient for release.

[0245] The present disclosure anticipates continuous improvement of the devices, systems, and processes described herein. As measurements are taken, data is collected, and predictions are compared to actual outcomes for more patients, the algorithms may be improved or replaced with more sophisticated algorithms; for example, a trained neural network may replace the total scores used in Tables 1 through 4. Initially, the management system 110 may only provide predictions to healthcare professionals and alert them to potential adverse events or discomfort. The management system 110 may do more, become more intelligent, and become less reliant on healthcare professionals as data is collected by the management system 110.

[0246] As an example, consider sound or vibration measurements via contact sensor 164a and / or non-contact sensor 164b. While it is common for medical personnel to place two fingers on the skin over the catheter exit or tip to “feel” the vibrations of the fluid exiting the catheter at the beginning of a test or fluid injection (e.g., contrast injection), medical personnel generally cannot continue doing so because they must leave the imaging room when the imaging itself occurs. Thus, there is little data on how these vibrations evolve over the time course of the injection. Some data has been obtained in phantom and phantom / human hybrid settings. However, because adverse events such as extravasation or allergic reactions are so rare, and phantom or animal models can only go so far, it is anticipated that the management system 110 may initially offer a feature that could be described as a “remote electronic stethoscope,” allowing medical personnel to remotely hear or feel the injection throughout the entire injection, rather than just feeling the injection site for a few seconds at the beginning of the injection. Initially, the management system 110 may not make any decisions or take any action to alter the injection or imaging study itself. However, as successful and abnormal injections are monitored, measured, and correlated to outcomes, the management system 110 may provide the ability to alert healthcare professionals to the possible presence of an adverse event and / or to alert healthcare professionals to the prediction of the onset of an adverse event, similar to the devices and systems of U.S. Patent Application Publication No. 2016 / 0224750 A1, filed January 29, 2016, the entire contents of which are incorporated herein by reference. The management system 110 may recommend actions to healthcare professionals or take actions that can be reversed by healthcare professionals. As successful and abnormal injections continue to be monitored, measured, and correlated to outcomes, the management system 110 can become sufficiently sophisticated and / or trained in one or more areas that it can sense and act in response to situations in ways that humans cannot.Additionally, over time and with improvement, healthcare professionals can gain confidence in the management system 110, allowing for more automated recommendations and actions. Additionally, the electrical or physical configuration of various sensors can be improved based on learning from accumulated data.

[0247] The description and disclosure herein demonstrates that devices, systems, and methods can collect and evaluate information about any infusion and ultimately evaluate any infusion along a continuum of good or normal versus the presence of an adverse event, going beyond the typical two-bucket, single-threshold distinction between normal and abnormal used in past devices looking for abnormalities.

[0248] In many of the aspects and embodiments described herein, sensors 164a and / or 164b and management system 110 are considered to be in association with and in communication with other systems. In some non-limiting embodiments or aspects, sensors 164a and / or 164b management system 110 may be standalone systems. An example of this may include the remote electronic stethoscope functionality described herein. This may be a simple system that senses sound and amplifies and transmits the sensed and amplified sound so that medical personnel can hear the sounds emanating from the injection. A second example of a simple standalone system is a non-contact sensor 164b that monitors the injection site, enhances selected aspects of the image, and transmits them for medical personnel to view.

[0249] While embodiments or aspects have been described in detail for purposes of illustration and description, it should be understood that such detail is for that purpose only and that the embodiments or aspects are not limited to the disclosed embodiments or aspects, but rather are intended to cover modifications and equivalent arrangements within the spirit and scope of the appended claims. For example, it should be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment or aspect may be combined with one or more features of any other embodiment or aspect. Indeed, many of these features may be combined in ways not specifically recited in the claims and / or disclosed herein. While each dependent claim listed below may depend directly on only one claim, the disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim set. [Explanation of symbols]

[0250] 100 Environment 102 Fluid Injector System 104 Imaging System 106 Sensor System 108 User Devices 110 Management System 112 Auxiliary Systems 114 Communication Network 150 Implementation 152 injector 154 Injector Control Calculation System 156 Injector User Interface 158 Imaging Device 160 Imaging Device Control and Computing System 162 Imaging Device User Interface 164 one or more sensors 164a One or more contact sensors 164b One or more non-contact sensors 166 Control and Calculation Systems 168 One or more hospital information systems 170 Cloud Computing and Offsite Resources 172 Administrative User Interface 200 devices 202 Bus 204 processors 206 memory 208 Memory Components 210 Input Components 212 Output Components 214 Communication Interface 300 processes 400 Contact Sensor Device 402 Contact Sensor 404 Housing 405a first end 405b second end 406 Communication Devices 408 processors 410 User Input / Feedback Devices 412 Battery 450 sheath 500 processes 550 processes 600 processes 700 Implementation 702 Sound Generating Device 800 Contact Sensor Device 802 Housing 804 Finger Sensor 805a First end 805b second end 806 Wire 808 Electronic Components 810 Conductive probe or electrode 812 Disposable Adhesive Protector 900 processes 1000 processes 1100 instructions 1201 Audio Data Stream 1202 Audio Data Stream 1203 Audio Data Stream 1204 Data Combination Technology 1205 Combined Data Streams 1206 Data Extraction Process 1207 Additional Data Streams 1208 Data Stream 1209 Data 1210 Data Stream 1211 Algorithm 1212 System Control

Claims

1. 1. A system comprising: at least one processor, the processor comprising: obtaining patient data associated with the patient; determining, based on the patient data, an initial risk prediction for the patient associated with a fluid infusion administered to the patient, the initial risk prediction including a probability that the patient will experience at least one adverse event in response to the fluid infusion; providing the initial risk prediction to a user device before the fluid injection is administered to the patient; determining, using at least one sensor, sensor data related to the patient, determined after the fluid infusion is initiated; determining a current risk prediction for the patient associated with the fluid infusion based on the sensor data determined after the fluid infusion has begun, the current risk prediction including a probability that the patient will experience the at least one adverse event in response to the fluid infusion; providing the current risk prediction to the user device; and determining a patient's pain level based on the sensor data determined after the fluid infusion has begun; providing the patient's pain level to the user device; 1. A system programmed and / or configured to:

2. the at least one processor comparing the patient's pain level to at least one threshold level; in response to determining that the patient's pain level meets the at least one threshold level; Providing alerts to user devices; and Automatically controlling at least one of (i) a fluid injection system to stop the fluid injection, and (ii) an imaging system to adjust the timing of imaging operations. and at least one of The system of claim 1 , further programmed and / or configured to:

3. The at least one processor: determining a change in one or more parameters of the sensor data over a period of time; comparing the change in the one or more parameters to at least one threshold change; 3. The system of claim 2, further programmed and / or configured to determine the pain level of the patient by:

4. 4. The system of claim 3, wherein the sensor data includes at least one of the following parameters associated with the patient: heart rate, oxygen saturation, skin resistivity, skin color, exercise level, temperature proximate to the injection site, or any combination thereof.

5. 3. The system of claim 2, wherein the at least one sensor includes at least one of a pulse oximeter, a skin resistance sensor, a skin color sensor, an accelerometer, a temperature sensor, or any combination thereof.

6. Further comprising a sensor device, the at least one sensor is included in the sensor device, the sensor device including a glove-shaped housing configured to be worn on the patient's hand, the housing including the at least one sensor and a wireless communication device, the wireless communication device configured to wirelessly transmit the sensor data to an external device.

3. The system of claim 2.

7. Further comprising a sensor device, The at least one sensor is included in the sensor device, the sensor device comprising: an elongated housing extending between a first end and a second end; a pulse oximeter connected to the elongated housing via a wire, the elongated housing configured to encircle at least one of a patient's hand and wrist, the elongated housing including a wireless communication device and at least one of a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof, the pulse oximeter, the skin resistance sensor, the accelerometer, the temperature sensor, or any combination thereof configured to determine the sensor data, and the wireless communication device configured to wirelessly transmit the sensor data to an external device.

3. The system of claim 2.

8. A method executed by at least one processor, comprising: obtaining patient data associated with the patient; determining an initial risk prediction for the patient associated with a fluid infusion administered to the patient based on the patient data, the initial risk prediction comprising a probability that the patient will experience at least one adverse event in response to the fluid infusion; providing the initial risk prediction to a user device before the fluid injection is administered to the patient; determining, using at least one sensor, sensor data associated with the patient after the fluid infusion has begun; determining a current risk prediction for the patient associated with the fluid infusion based on the sensor data determined after the fluid infusion has been initiated, the current risk prediction comprising a probability that the patient will experience the at least one adverse event in response to the fluid infusion; providing the current risk prediction to the user device; determining a patient's pain level based on the sensor data determined after the fluid infusion has begun; comparing the patient's pain level to at least one threshold level; in response to determining that the patient's pain level meets the at least one threshold level; providing an alert to the user device; and automatically controlling at least one of (i) a fluid injection system to stop the fluid injection, and (ii) an imaging system to adjust the timing of imaging operations. with at least one of A method comprising:

9. The step of determining the patient's pain level comprises: determining a change in one or more parameters of the sensor data over a period of time; comparing the change in the one or more parameters to at least one threshold change; 9. The method of claim 8, comprising:

10. 10. The method of claim 9, wherein the sensor data includes at least one of the following parameters associated with the patient: heart rate, oxygen saturation, skin resistivity, exercise level, temperature proximate to an injection site, or any combination thereof.

11. 9. The method of claim 8, wherein the at least one sensor includes at least one of a pulse oximeter, a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof.

12. The at least one sensor is included in a sensor device, the sensor device including a glove-shaped housing configured to be worn on the patient's hand, the housing including the at least one sensor and a wireless communication device, and the method further comprising: wirelessly transmitting the sensor data to an external device using the wireless communication device; 9. The method of claim 8, further comprising:

13. the at least one sensor is included in a sensor device, the sensor device including an elongated housing extending between a first end and a second end, and a pulse oximeter connected to the elongated housing via a wire, the elongated housing configured to encircle at least one of a patient's hand and wrist, the elongated housing including a wireless communication device and at least one of a skin resistance sensor, an accelerometer, a temperature sensor, or any combination thereof; The method comprises: determining the sensor data using the pulse oximeter and the at least one of the skin resistance sensor, the accelerometer, the temperature sensor, or any combination thereof; wirelessly transmitting the sensor data to an external device using the wireless communication device; 9. The method of claim 8, further comprising:

14. A computer program comprising instructions for causing said at least one processor to perform a method according to any one of claims 8 to 13.

Images