Systems and methods for automating clinical workflow decisions and generating preferred reading indicators - Patents.com

Abstract

Examples of the present disclosure describe systems and methods for automating clinical workflow decisions. In some aspects, patient data may be collected from multiple data sources, such as imaging data. The patient data may be processed using an artificial intelligence (AI) component. The output of the AI ​​component may be used by a healthcare professional to inform healthcare decisions regarding the patient. The output of the AI ​​component and additional information related to healthcare decisions and healthcare pathways may be provided as input to a decision analysis component. The decision analysis component may process the input and output automated healthcare recommendations that may be used to further inform the healthcare professional's healthcare decisions. In some aspects, the output of the decision analysis component may be used to determine a priority or timeline for performing one or more actions related to the patient's healthcare.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application was filed as a PCT International patent application on July 31, 2020, and claims priority to and the benefit of U.S. Provisional Patent Application No. 62 / 881,156, entitled “SYSTEMS AND METHODS FOR AUTOMATING CLINICAL WORKFLOW DECISIONS,” filed on July 31, 2019, and U.S. Provisional Patent Application No. 62 / 941,601, entitled “SYSTEMS AND METHODS FOR AUTOMATING CLINICAL WORKFLOW DECISIONS AND GENERATING A PRIORITY READ INDICATOR,” filed on November 27, 2019, the disclosures of which are hereby incorporated by reference in their entireties. [Background technology]

[0002] (background) Modern breast care involves the analysis of a variety of complex factors and data points, such as patient history, provider experience, imaging modalities utilized, etc. The analysis allows providers to determine the breast care pathway that will optimize the quality of breast care and patient experience. However, such decisions are subjective and therefore can vary widely between providers. As a consequence, some patients may be provided with a suboptimal breast care pathway, which can result in increased hospital costs and a diminished patient experience.

[0003] The aspects disclosed herein are directed to these and other general considerations, and it should also be understood that although relatively specific problems may be discussed, the examples should not be limited to solving the specific problems identified in the background and elsewhere in this disclosure. Summary of the Invention [Means for solving the problem]

[0004] (overview) Examples of the present disclosure describe systems and methods for automating clinical workflow decisions. In some aspects, patient data may be collected from multiple data sources, such as patient records, healthcare practitioner notes / assessments, imaging data, etc. The patient data may be processed using an artificial intelligence (AI) component. The output of the AI ​​component may be used by healthcare practitioners to inform healthcare decisions regarding one or more patients. The output of the AI ​​component, information related to the healthcare practitioner's healthcare decisions, and / or complementary healthcare-related information may be provided as input to a decision analysis component. The decision analysis component may process the input and output automated healthcare recommendations that may be used to further inform the healthcare practitioner's healthcare decisions. In some aspects, the output of the decision analysis component may be used to determine a priority or timeline for performing one or more actions related to the patient's healthcare. For example, the output of the decision analysis component may indicate a priority or importance level for evaluating the patient's imaging data.

[0005] An aspect of the present disclosure provides a system comprising at least one processor and a memory coupled to the at least one processor, the memory comprising computer-executable instructions that when executed by the at least one processor perform a method including collecting patient data from one or more data sources, providing the patient data to a first artificial intelligence (AI) algorithm to analyze characteristics of the patient data, receiving a first output from the first AI algorithm, providing the first output to a second AI algorithm to determine a clinical workflow decision related to patient care, receiving a second output from the second AI algorithm, the second output comprising automated patient care recommendations, and providing the automated patient care recommendations to a healthcare professional.

[0006] An aspect of the disclosure further provides a method that includes collecting patient data from one or more data sources, providing the patient data to a first artificial intelligence (AI) component to analyze characteristics of the patient data, receiving a first output from the first AI component, providing the first output to a second AI component to determine a clinical workflow decision regarding patient care, and receiving a second output from the second AI component, the second output comprising automated patient care recommendations, and providing the automated patient care recommendations to a healthcare professional.

[0007] An aspect of the present disclosure further provides a system comprising at least one processor and a memory coupled to the at least one processor, the memory comprising computer-executable instructions that when executed by the at least one processor perform a method, the method including collecting image data from one or more data sources, evaluating the image data and identifying one or more features, calculating a confidence score based on the one or more features, comparing the confidence score to a threshold, and assigning a high evaluation priority to the image data when the confidence score exceeds the threshold.

[0008] This Summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description below. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Additional aspects, features and / or advantages of the examples will be set forth in part in the description that follows, and in part will be obvious from the description, or may be learned by practice of the disclosure. The present invention provides, for example, the following: (Item 1) 1. A system comprising: At least one processor; a memory coupled to the at least one processor; Equipped with The memory comprises computer-executable instructions that, when executed by the at least one processor, perform a method; The method comprises: collecting patient data from one or more data sources; providing the patient data to a first artificial intelligence (AI) algorithm to analyze characteristics of the patient data; receiving a first output from the first AI algorithm; providing the first output to a second AI algorithm to determine a clinical workflow decision regarding patient care; receiving a second output from the second AI algorithm, the second output comprising an automated patient care recommendation; and providing said automated patient care recommendations to a healthcare professional; and Including, the system. (Item 2) 2. The system of claim 1, wherein the patient data comprises at least one of a digital image of the breast, patient personal information, or patient history information. (Item 3) 2. The system of claim 1, wherein the one or more data sources comprise at least one of patient visit information, a patient electronic medical record (EMR), a hospital information system (HIS) record, or a medical imaging system. (Item 4) 2. The system of claim 1, wherein the first AI algorithm provides an assessment of breast density. (Item 5) 2. The system of claim 1, wherein the first output comprises at least one of a breast density category score, a computer-aided detection marker result, a radiometric characteristic, or a breast cancer risk assessment result. (Item 6) The method comprises: providing at least a portion of the first output to the medical personnel; receiving an initial patient care recommendation from the health care provider; supplementing the initial patient care recommendations with the automated patient care recommendations; Item 2. The system of item 1, further comprising: (Item 7) The method comprises: acquiring one or more digital mammography images from the patient during a first visit; providing the one or more digital mammography images to the first AI algorithm to generate the first output; providing the first output to the medical personnel; and Further comprising: 2. The system of claim 1, wherein the first output enables the medical professional to perform one or more diagnostic procedures on the patient during the first visit. (Item 8) 2. The system of claim 1, wherein supplemental information is further provided to the second AI algorithm to determine clinical workflow decisions regarding patient care, the supplemental information comprising at least one of the patient data, a diagnostic report, and practice guidelines from a clinical professional society. (Item 9) 2. The system of claim 1, wherein the automated patient care recommendations comprise diagnostic information and recommendations related to at least one of a biopsy procedure, chemotherapy, surgical intervention, radiation therapy, or a mammography imaging procedure. (Item 10) 1. A method for automating clinical workflow decisions, the method comprising: collecting patient data from one or more data sources; providing the patient data to a first artificial intelligence (AI) component for analyzing characteristics of the patient data; receiving a first output from the first AI component; providing the first output to a second AI component to determine a clinical workflow decision regarding patient care; receiving a second output from the second AI component, the second output comprising an automated patient care recommendation; and providing said automated patient care recommendations to a healthcare professional; and A method comprising: (Item 11) 11. The method of claim 10, wherein the patient data comprises at least one of a digital mammography image, patient personal information, or patient history information. (Item 12) 11. The method of claim 10, wherein the first AI component provides an assessment of the density of the patient's breast. (Item 13) 11. The method of claim 10, wherein the first output comprises at least one of a breast composition categorical score, a computer-aided detection marker result, a radiometric characteristic, or a breast cancer risk assessment result. (Item 14) 11. The method of claim 10, wherein the second AI component implements at least one of an artificial neural network, a support vector machine (SVM), a linear reinforcement model, or a random decision forest. (Item 15) The method comprises: providing at least a portion of the first output to the medical personnel; receiving an initial patient care recommendation from the health care provider; supplementing the initial patient care recommendations with the automated patient care recommendations; 11. The method of claim 10, further comprising: (Item 16) The method comprises: acquiring one or more digital mammography images from the patient during a first visit; providing the one or more digital mammography images to the first AI algorithm to generate the first output; providing the first output to the medical personnel; and Further comprising: 11. The method of claim 10, wherein the first output enables the medical professional to perform one or more diagnostic procedures on the patient during the first visit. (Item 17) 11. The method of claim 10, wherein the automated patient care recommendations comprise diagnostic information and recommendations related to at least one of a biopsy procedure, chemotherapy, surgical intervention, radiation therapy, or a mammography imaging procedure. (Item 18) 1. A system comprising: At least one processor; a memory coupled to the at least one processor; Equipped with The memory comprises computer-executable instructions that, when executed by the at least one processor, perform a method; The method comprises: collecting image data from one or more data sources; evaluating the image data and identifying one or more features; calculating a confidence score based on the one or more features; comparing the confidence score to a threshold; assigning a high evaluation priority to the image data when the confidence score exceeds the threshold; Including, the system. (Item 19) Calculating the confidence score is comparing the image data to a stored data set and identifying similarities between the image data and the stored data set, the stored data set including labeled image data; calculating the confidence score based on at least one of an amount of similarity identified between the image data and the stored data set or a degree of match between the identified similarities; Item 19. The system according to item 18, comprising: (Item 20) 20. The system of claim 18, wherein the high evaluation priority causes the image data to be evaluated before a previously received set of image data is evaluated, the previously received set of image data having a lower evaluation priority than the image data. [Brief description of the drawings]

[0009] BRIEF DESCRIPTION OF THE DRAWINGS Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0010] [Figure 1] FIG. 1 illustrates an overview of an exemplary system for automating clinical workflow decisions described herein.

[0011] [Diagram 2] FIG. 2 is a diagram of an exemplary process flow for automating clinical workflow decisions as described herein.

[0012] [Diagram 3] FIG. 3 illustrates an overview of an exemplary decision processing system for automating clinical workflow decisions as described herein.

[0013] [Figure 4] FIG. 4 illustrates an exemplary method for automating clinical workflow decisions described herein.

[0014] [Diagram 5] FIG. 5 illustrates an exemplary method for determining image capture priority as described herein.

[0015] [Figure 6A]FIG. 6A illustrates an exemplary user interface associated with the automated clinical workflow decisions described herein.

[0016] [Figure 6B] FIG. 6B illustrates an analysis dialog interface associated with the example user interface of FIG. 6A.

[0017] [Figure 7] FIG. 7 illustrates an example of a suitable operating environment in which one or more of the present embodiments may be implemented. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] (Detailed Description) Medical imaging has become a widely used tool for identifying and diagnosing abnormalities such as cancer or other disease conditions in the human body. Medical imaging processes such as mammography and tomosynthesis are particularly useful tools for imaging the breast to screen for or diagnose cancer or other pathologies associated with the breast. A tomosynthesis system is a mammography system that allows high-resolution imaging of the breast based on limited-angle tomosynthesis. Tomosynthesis typically generates multiple x-ray images (each of the individual layers or slices of the breast through their entire thickness). In contrast to conventional two-dimensional (2D) mammography systems, tomosynthesis systems acquire a series of x-ray projection images, each acquired at a different angular displacement as the x-ray source moves along a path such as an arc above the breast. In contrast to conventional computed tomography (CT), tomosynthesis is typically based on projection images acquired at limited angular displacements of the x-ray source around the breast. Tomosynthesis reduces or eliminates the problems caused by tissue overlap and structural noise present in 2D mammography imaging.

[0019] In modern breast care centers, images generated using medical imaging are evaluated by various medical professionals to determine the best breast care path for patients. However, this evaluation can be daunting considering the complexity of imaging data and systems, patient information and records, hospital information systems, medical professionals' knowledge and experience, clinical practice guidelines, AI diagnostic systems and outputs, etc. As a result, the evaluation can generate medical judgments that differ widely between medical professionals. The differences in medical judgments can cause medical professionals to provide suboptimal medical paths to some patients. These suboptimal medical paths can significantly diminish the patient experience.

[0020] Furthermore, medical imaging evaluation typically involves a batch reading process for which image data on a large number (e.g., 100 or more) of screening subjects are collected. Typically, after the screening subjects leave the imaging facility, the collected image data is evaluated ("read") in batches at the convenience of the mammography radiologist. When actionable (or potentially actionable) content is identified in the images evaluated during the batch reading process, each screening subject is "recalled" (e.g., recalled to the imaging facility) for follow-up imaging and / or biopsy. Due to scheduling and other conflicts, the time delay between screening (image acquisition) and recall can be days or weeks. This delay can have undesirable consequences, for example in the case of aggressive cancer. The delay can cause undue stress and anxiety even for screening subjects who are ultimately determined to have no abnormalities.

[0021] To address such challenges with suboptimal medical decisions, the present disclosure describes systems and methods for automating clinical workflow decisions and supporting medical practitioners' decisions. In some aspects, patient data regarding one or more patients (or "screening subjects") may be collected from multiple data sources accessible to a medical practitioner, clinical facility, or services belonging thereto. As used herein, patient data may refer to information related to a patient's name / identifier, patient's personal information, medical images, vital signs and other diagnostic information, visit history, prior treatment, previously diagnosed illnesses / disorders / diseases, prescribed medications, and the like. Examples of data sources include, but are not limited to, patient visit information, patient's electronic medical record (EMR), hospital information system (HIS), and medical imaging system. In some examples, the patient data collection process may be performed manually, automatically, or a combination thereof.

[0022] After collecting the patient data, the patient data may be presented to the AI ​​processing component. The AI ​​processing component may utilize one or more rule sets, algorithms, or models. As used herein, a model may refer to a predictive or statistical utility or program that may be used to determine a probability distribution of one or more character sequences, classes, objects, outcome sets, or events, and / or predict a response value from one or more predictors. The model may be based on or incorporate one or more rule sets, machine learning, neural networks, and the like. In an example, the AI ​​processing component may process the patient data and provide one or more outputs. Exemplary outputs include, but are not limited to, a breast composition / density category score, computer-aided detection markers (e.g., for calcifications and tumors detected in the breast), computerized radiometric features, results of a breast cancer risk assessment, and the like. As used herein, a breast composition / density category score may indicate the percentage of the breast that is composed of fibroglandular tissue. Generally, dense breasts contain more epithelial cells, stromal cells, and collagen, which are important factors in the transformation of normal cells into cancer cells. As used herein, computer-assisted detection markers may refer to digital geometric shapes (e.g., triangles, circles, squares, etc.) that are added to (or overlaid on) an image. Detection markers may indicate areas of the breast where a lesion or object of diagnostic interest has been detected using computer-assisted detection software and / or machine learning algorithms. As used herein, radiometric features may refer to characteristics that describe the information content in an image. Such characteristics may include image attributes / values ​​related to breast density, breast shape, breast volume, image resolution, etc.

[0023] In certain aspects, the output and / or patient data may be provided to one or more recipients, or one or more receiving devices. Examples of receiving devices include, but are not limited to, image review workstations, medical imaging systems, and technician workstations. Medical personnel (and / or people associated with them) may use the receiving devices to evaluate the output and / or patient data to inform one or more medical decisions or paths. As one specific example, a set of x-ray images of the patient's breasts and the output of the AI ​​processing component may be provided to an image review workstation. A physician may evaluate the data provided to the image review workstation and determine an initial or primary breast care path for the patient. As used herein, a breast care path (or medical path) may refer to a plan or strategy for guiding decisions and timing regarding diagnostic, interventional, therapeutic, and / or complementary actions at one or more stages of a disease or condition. In general, a breast care path may represent a strategy (e.g., a care path) for managing a patient population with a particular problem or condition, or a strategy (e.g., a care plan) for managing an individual patient with a particular problem or condition. As another example, the output of the AI ​​processing component may be provided to an imaging system or acquisition room, where a technician may evaluate the data provided to the imaging system / acquisition room to enable the technician to perform a diagnostic procedure while the patient is at the facility.

[0024] In certain aspects, various inputs may be provided to a decision analysis component configured to output a recommended medical path. The decision analysis component may utilize one or more rule sets, algorithms, or models described above with respect to the AI ​​processing component. Exemplary inputs to the decision analysis component include, but are not limited to, patient data, output of the AI ​​processing component, a medical practitioner's initial / primary medical judgment and diagnostic assessment, and medical practice guidelines from clinical professional associations. The decision analysis component may process the various inputs and provide one or more outputs. Exemplary outputs include, but are not limited to, automated patient medical recommendations, assessments of the medical practitioner's judgment, recommended treatments and procedures, instructions for performing therapies / procedures, diagnostic and intervention reports, automated appointment scheduling, and assessment priorities or timelines. In certain examples, the output of the decision analysis component may be provided (or otherwise made accessible) to one or more medical practitioners. The output may be used to further inform the medical decisions of the medical practitioners.

[0025] In some aspects, the output of the decision analysis component may comprise (or otherwise indicate) a priority read indicator. The priority read indicator may indicate an evaluation ("read") priority for one or more medical images. In one example, the priority read indicator may be determined by identifying aspects of the medical image (such as features of a potentially actionable lesion), determining a confidence level for the identified aspects, and comparing the determined confidence level to a threshold. Those medical images that meet and / or exceed the threshold may be assigned a "priority" status or value. Alternatively, the "priority" status / value may be assigned to the patient corresponding to the medical image. The priority status / value may be used to place an evaluation importance or timeline for the reading of the medical image or further patient evaluation. For example, a medical image with a "high" priority status may be placed in a read queue above medical images with a normal priority status or a lower priority status. As a result of the "high" priority status of a medical image, medical personnel may be notified of the medical image immediately (or quickly) and may evaluate the medical image while the screening subject is still at the screening facility. As another example, a patient with a "high" priority status may immediately undergo further evaluation. For example, additional medical images of the patient may be collected, a specialist may immediately see (or be assigned to) the patient, or an appointment / procedure may be scheduled. Thus, the priority read indicator improves detection of abnormalities and reduces the number of patient recalls.

[0026] Thus, the present disclosure provides multiple technical advantages, including, but not limited to, generating automated (or semi-automated) clinical workflows, automating breast care analysis and risk assessment, generating automated treatment and treatment orders, generating automated diagnostic and intervention reports, allowing "same-visit" diagnostic procedures to be performed while the patient is still at the facility, normalizing medical decision-making, optimizing medical recommendations, prioritizing the evaluation of medical images, and enhancing patient experience by reducing patient visits, patient anxiety, hospital costs, and prolonged care.

[0027] FIG. 1 illustrates an overview of an exemplary system for automating clinical workflow decisions described herein. The presented exemplary system 100 is a combination of interdependent components that interact and form an integrated system for automating clinical workflow decisions. The components of the system may be hardware components (e.g., used to execute / run an operating system (OS)) or software components (e.g., applications, application programming interfaces (APIs), modules, virtual machines, runtime libraries, etc.) implemented on and / or executed by the hardware components of the system. In one example, the exemplary system 100 may provide an environment in which the software components run, follow a set of constraints for operation, and utilize the resources or facilities of the system 100. For example, the software may run on a processing device such as a personal computer (PC), a mobile device (e.g., a smart device, a mobile phone, a tablet, a laptop, a personal digital assistant (PDA), etc.), and / or any other electronic device. For an example of an operating environment of a processing device, see the exemplary operating environment illustrated in FIG. 7. In another example, the components of the system disclosed herein may be distributed across multiple devices. For example, input may be entered on a client device and the information may be processed or accessed using other devices in the network, such as one or more server devices.

[0028] As an example, system 100 may include computing devices 102, 104, and 106, processing system 108, decision system 110, and network 112. Those skilled in the art will appreciate that a system such as system 100 may be scaled differently and may include more or fewer components than those depicted in FIG. 1. For example, in some examples, the functionality and components of processing system 108 and decision system 110 may be integrated into a single processing system. Alternatively, the functionality and components of processing system 108 and / or decision system 110 may be distributed across multiple systems and devices.

[0029] The computing devices 102, 104, and 106 may be configured to receive patient data relating to a medical patient, such as the patient 114. Examples of the computing devices 102, 104, and 106 include medical imaging systems / devices (e.g., X-ray, ultrasound, and / or magnetic resonance imaging (MRI) devices), medical workstations (e.g., EMR devices, image review workstations, etc.), mobile medical devices, patient computing devices (e.g., wearable devices, mobile phones, etc.), and similar processing systems and devices. The computing devices 102, 104, and 106 may be located within a medical facility or facility, on the patient, on a medical professional, etc. In an example, the patient data may be provided to the computing devices 102, 104, and 106 using a manual or automated process. For example, a medical professional may manually input the patient data into the computing device. Alternatively, the patient's device may automatically upload the patient data to the medical device based on one or more criteria.

[0030] The processing system 108 may be configured to process patient data. In some aspects, the processing system 108 may have access via the network 112 to one or more sources of patient data, such as the computing devices 102, 104, and 106. At least a portion of the patient data may be provided as input to the processing system 108. The processing system 108 may process the input using one or more AI processing techniques. Based on the processed input, the processing system 108 may generate one or more outputs, such as an assessment of breast composition, detection markers, radiometric characteristics, etc. The outputs may be provided (or made accessible) to other components of the system 100, such as the computing devices 102, 104, and 106. In some examples, the outputs may be evaluated by one or more medical personnel to determine a medical path for the patient. For example, a physician may use the computing device 106 to evaluate the x-ray images collected from the imaging system and the results of the detection markers collected from the processing system 108. Based on the evaluation, the physician may determine a medical decision / plan for the patient.

[0031] The decision system 110 may be configured to provide a recommended medical path. In some aspects, the decision system 110 may have access to one or more sources of patient data, output from the processing system 108, diagnostic assessments and notes, medical practice guidelines, etc. At least a portion of this data may be provided as input to the decision system 110. The decision system 110 may process the input using one or more AI processing techniques or models. For example, the decision system 110 may implement an artificial neural network, a support vector machine (SVM), a linear reinforcement model, a random decision forest, or similar machine learning techniques. In at least one example, the AI ​​processing techniques performed by the decision system 110 may be the same as (or similar to) those performed by the processing system 108. In such an example, the functionality of the decision system 110 and the processing system 108 may be combined into a single processing system or component. Based on the processed input, the decision system 110 may generate one or more outputs, such as an automated diagnosis, a patient care recommendation, an assessment of the healthcare professional's judgment, step-by-step treatment instructions, etc. In some aspects, the output(s) may be used to further inform medical decisions of medical personnel. For example, a physician may compare the medical decisions of the decision system 110 with the physician's own medical decisions to determine the best medical path for the patient.

[0032] 2 is a diagram of an exemplary process flow for automating clinical workflow decisions described herein. The exemplary system 200 presented includes a patient information record 202, an X-ray imaging system 204, an image review station 206, an AI processing component 208, a decision supporter 210, a practice guideline 212, a diagnostic report 214, a biopsy recommendation 216, a radiation recommendation 218, a surgery recommendation 220, a chemotherapy recommendation 222, a preferred reading indicator 223, and additional imaging system(s) 224. Those skilled in the art will understand that a system such as system 200 may be scaled differently and may include more or fewer components than those described in FIG. 2.

[0033] As illustrated in FIG. 2, patient data may be collected from a patient. In some aspects, the patient data may be collected from a patient during a visit to a medical facility. In another example, the patient data may be provided to a medical facility while the patient is not visiting the medical facility. For example, the patient data may be remotely uploaded from a patient device to one or more HIS devices. In the process flow 200, a patient information record 202 may store patient information such as name or identifier, contact information, personal information, diagnostic history, vital signs information, prescribed medications, etc. An X-ray imaging system 204 may generate and / or store X-ray images of, for example, the patient's breast. An additional imaging system(s) 224 may generate and / or store ultrasound images of, for example, the patient's breast and / or MRI images of the breast.

[0034] In one aspect, information recorded in the patient information record 202 and images generated using the X-ray imaging system 204 and the additional imaging system(s) 224 (collectively referred to as "patient data") may be provided to the AI ​​processing component 208. In one example, the AI ​​processing component 208 may be configured to assess one or more characteristics of the patient's breast based on breast image data received as input. The assessment may include analyzing the texture / tissue of the imaged breast and identifying one or more patterns in the breast image. Based on the provided patient data, the AI ​​processing component 208 may generate breast assessment data, such as a breast composition / density categorical score, computer-aided detection markers (e.g., for calcifications and tumors detected in the breast), computerized radiometric characteristics, and results of a breast cancer risk assessment. The breast assessment data may be provided to the X-ray imaging system 204 and / or the additional imaging system(s) 224. A technician may evaluate the breast assessment data provided to the x-ray imaging system 204 and / or the additional imaging system(s) 224 and determine, for example, whether additional imaging should be performed for the patient. The breast assessment data and / or patient data may also be provided to the image review station 206. A physician may evaluate the information provided to the image review station 206 as well as the practice guidelines 212 and generate diagnostic information and / or medical decisions regarding the patient (collectively referred to as a "diagnostic report").

[0035] In some aspects, breast assessment data, patient data and / or diagnostic reports may be provided to a decision supporter 210. Based on the provided information and / or practice guidelines 212, the decision supporter 210 may automatically generate decision information such as patient care recommendations, assessment of a medical professional's decision, recommended imaging procedures, recommended treatments and procedures, instructions for performing the treatments / procedures, priorities and / or timelines for treatments / procedures, and diagnostic reports 214. Examples of recommended treatments and procedures include biopsy recommendations 216, radiation recommendations 218, surgery recommendations 220, and chemotherapy recommendations 222. Examples of treatment and procedure priorities / timelines include a priority reading indicator 223. The priority reading indicator 223 may include or represent a status, value, or date / time for evaluating the medical images. In some aspects, the decision information may be made accessible to one or more medical professionals (or from their associated computing devices). For example, the process flow 200 illustrates decision information provided to a physician who created a diagnostic report. As another more specific example, the process flow 200 illustrates a priority read indicator 223 provided to the technician, the x-ray imaging system 204 and the image review station 206 .

[0036] 3 illustrates an overview of an exemplary decision processing system 300 for automating clinical workflow decisions described herein. The automated clinical workflow techniques implemented by the input decision system 300 may include the automated clinical workflow techniques and data described in the system of FIG. 1. In some examples, one or more components (or their functionality) of the input decision system 300 may be distributed across multiple devices and / or systems. In other examples, a single device (with at least one processor and / or memory) may comprise the components of the input decision system 300.

[0037] With reference to FIG. 3, the input decision system 300 may include a data collection engine 302, a decision engine 304, and an output creation engine 306. The data collection engine 302 may be configured to access a set of data. In some aspects, the data collection engine 302 may have access to information related to one or more patients. The information may include patient data (e.g., patient identification, patient medical images, patient diagnostic information, etc.), breast composition assessments, detection markers, radiometric characteristics, diagnostic assessments and notes, medical practice guidelines, etc. In some aspects, at least a portion of the information may be test data or training data. The test / training data may include labeled data and images used to train one or more AI models or algorithms.

[0038] The decision engine 304 may be configured to process the received information. In some aspects, the received information may be provided to the decision engine 304. The decision engine 304 may apply one or more AI processing algorithms or models to the received information. For example, the decision engine 304 may apply an AI-based fusion algorithm to the received information. The AI ​​processing algorithm / model may evaluate the received information and determine an association between the received information and the training data used to train the AI ​​processing algorithm / model. Based on the evaluation, the decision engine 304 may identify or determine a best medical path or recommendation for one or more patients associated with the patient data. In some aspects, the decision engine 304 may further identify and provide image reading priority. For example, the decision engine 304 may assign a "priority" status to an image in the received information.

[0039] The output creation engine 306 may be configured to create one or more outputs regarding the received information. In some aspects, the output creation engine 306 may use the identification or decision of the decision engine 304 to create one or more outputs. As an example, the output creation engine 306 may recommend the use of one or more additional imaging modalities, such as contrast-enhanced MRI, advanced ultrasound imaging (e.g., shear wave imaging, contrast imaging, 3D imaging, etc.), and positron emission tomography (PET) imaging. As another example, the output creation engine 306 may generate a comprehensive report including diagnostic information and recommendations regarding biopsy procedures, chemotherapy, surgical intervention, or radiation therapy. The recommendations may include detailed treatment instructions and associations between data points and medical images. As a specific example, for a biopsy procedure, the output creation engine 306 may provide step-by-step biopsy instructions associated with the biopsy images, as well as previous diagnostic images from X-ray, ultrasound, and MRI imaging systems.

[0040] Having described various systems that may be employed by aspects disclosed herein, the disclosure now describes one or more methods that may be implemented by various aspects of the disclosure. In some aspects, the methods 400 and 500 may be performed by an exemplary system, such as the system 100 of FIG. 1 or the decision processing system 300 of FIG. 3. In some examples, the methods 400 and 500 may be performed on a device with at least one processor configured to store and execute operations, programs, or instructions. However, the methods 400 and 500 are not limited to such examples. In other examples, the methods 400 and 500 may be implemented on an application or service for automating clinical workflow decisions. In at least one example, the methods 400 and 500 may be performed (e.g., computer-implemented operations) by one or more components of a distributed network, such as a web service / distributed network service (e.g., cloud service).

[0041] FIG. 4 illustrates an exemplary method 400 for automating clinical workflow decisions described herein. The exemplary method 400 begins with operation 402, in which patient data is collected from one or more data sources. In one aspect, a data collection component, such as the data collection engine 202, may collect patient data from one or more data sources. Exemplary data sources include patient visit information, the patient's EMR, the medical facility's HIS records, and a medical imaging system. For example, during a patient's visit to a medical facility, a patient information record stored (or accessible) by the medical facility may be used to collect or access personal information about the patient, such as the patient's age, diagnosis history, lifestyle information, etc. During the patient's visit, an X-ray imaging system may be used to generate one or more 2D and / or 3D X-ray images of the patient's breast(s). The X-ray images may be combined with (or otherwise associated with) the personal information and / or stored in one or more medical records or systems of the medical facility.

[0042] In certain aspects, the data collection process may be initiated manually and / or automatically. For example, a medical professional may manually initiate the data collection process by requesting patient information from a patient and entering the requested patient information into a patient information record. Alternatively, the data collection process may be initiated automatically upon satisfaction of one or more criteria. Exemplary criteria may include a patient check-in event, entering diagnostic information or a patient's medical path into an HIS, or evaluating a digital mammography image via an image review workstation. For example, in response to detecting a patient check-in event at a medical facility, an electronic system / service of the medical facility may automatically collect patient information from one or more of the patient's medical records. The collected data may be aggregated into an active working file for the patient's current visit.

[0043] In operation 404, the patient data is provided to a processing component. In some aspects, one or more portions of the patient data may be provided to a processing component, such as the AI ​​processing component 208. The processing component may comprise or have access to one or more rule sets, algorithms, or predictive models. The processing component may use a set of AI algorithms to process the information and create a set of outputs. For example, continuing from the above example, the combined data (e.g., patient personal information and x-ray images) may be provided as input to an AI system accessible from a medical facility. The AI ​​system may be implemented on a single device (such as a single workstation at the medical facility) or may be provided to multiple devices across multiple devices in a distributed computing environment as a distributed service / system. The AI ​​system may be configured to perform breast assessments using machine learning algorithms that analyze attributes of each patient's breasts (such as patterns, textures, etc.). The AI ​​system may be implemented on a single device (such as a single workstation at the medical facility) or may be provided to multiple devices across multiple devices in a distributed computing environment as a distributed service / system. By applying machine learning algorithms to the combined data, the AI ​​system may identify one or more aspects of the x-ray image that indicate the imaged breast is heterogeneously dense. This density classification may be based, for example, on the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density (MD) Assessment Categories. The AI ​​system may further add detection markers to the x-ray image to indicate one or more calcifications or tumors detected within the x-ray image.

[0044] In operation 406, an output is received from the processing component. In certain aspects, the processing component may generate one or more outputs from the patient data. Exemplary outputs include a breast composition category score, a breast density assessment, computer-assisted detection markers, computerized radiometric features, results of a breast cancer risk assessment, and the like. At least a portion of the output may be provided to one or more medical practitioners and / or one or more medical systems / devices. For example, continuing from the above example, the AI ​​system may output a density classification of the imaged breast (e.g., heterogeneously dense) and / or corresponding X-ray image data (e.g., original X-ray image, X-ray image update with detection markers, and / or calcifications or tumors, etc.). The output of the AI ​​system may be provided to the patient's radiologist and / or one or more computing devices (e.g., workstations, mobile devices, etc.) of the medical imaging technician. Based on the radiologist's evaluation of the output of the AI ​​system, the radiologist may recommend performing ultrasound imaging of the patient's breast. In response to the recommendations, the medical imaging technician may perform the recommended diagnostic procedure (e.g., magnification / contrast diagnostic imaging) and / or complementary screening procedure (e.g., ultrasound imaging) while the patient is still at the facility (e.g., during the patient's current visit). Performing those procedures while the patient is still at the facility may avoid additional visits to the clinic and may reduce clinical costs associated with rescheduling the appointment.

[0045] In operation 408, the output of the processing component is provided to a decision component. In some aspects, the output of the processing component, the medical professional's recommendations, the X-ray image data, the complementary data from the diagnostic / screening procedure, and other information related to the patient may be provided to a decision component, such as the decision engine 304. The decision component may comprise or have access to one or more rule sets, algorithms, or predictive models. The decision component may use one or more AI algorithms to process the information and create a set of outputs. For example, continuing from the above example, the patient data, the AI ​​system output, the X-ray image data, the ultrasound image data (recommended by the radiologist), the radiologist's recommendation data, as well as practice guidelines from one or more clinical professional organizations (e.g., the American College of Radiology (ACR), the National Comprehensive Cancer Network (NCCN), etc.) may be provided as inputs to the AI-based fusion algorithm. The AI-based fusion algorithm may be configured to provide a best medical path or recommendation for one or more patients. Based on the provided inputs, the AI-based fusion algorithm may determine that a surgical intervention is the best care plan for the patient.

[0046] In operation 410, an output is received from the judgment component. In certain aspects, the judgment component may create one or more outputs from the received inputs. Exemplary outputs include automated patient care recommendations, assessment of the healthcare professional's judgment, recommended treatments and procedures, instructions for performing the treatment / procedure, diagnostic and intervention reports, and automated appointment scheduling. For example, continuing from the above example, based on the inputs provided to the AI-based fusion algorithm, the AI-based fusion algorithm may output a comprehensive report including diagnostic information about the patient and recommendations regarding surgical interventions for the patient. The recommendations regarding surgical interventions may be accompanied by specific guidelines for performing the recommended surgical procedures. The instructions may include surgical images, step-by-step surgical instructions, computer-assisted detection markers, recommended medications, recovery procedures, and the like.

[0047] In operation 412, the automated patient care recommendation is provided to a medical practitioner. In some aspects, the output (or a portion thereof) from the judgment component may be provided to one or more targets. Exemplary targets include a medical practitioner device, a clinical facility device, a patient device, a data archive, one or more processing systems, etc. The target may assess the automated patient care recommendation and inform or evaluate its own patient care recommendation. For example, continuing from the above example, the overall report and recommendation for surgical intervention may be provided to one or more computing devices of the patient's radiologist. The overall report may indicate that 93% of radiologists have recommended surgical intervention for patients with patient data similar to that of the patient and AI system output similar to that of the patient. Based on the overall report and recommendation, the radiologist may make or approve a recommendation for surgical intervention. In at least one example, the radiologist may modify a previous medical recommendation made by the radiologist to be consistent with the recommendation provided by the judgment component.

[0048] FIG. 5 illustrates an example method 500 for determining image reading priorities as described herein. In one aspect, the example method 500 may be implemented (in whole or in part) on an X-ray imaging system or device, such as the X-ray imaging system 204. The example method 500 begins with operation 502, where image data is collected from one or more data sources. In one aspect, a data collection component, such as the data collection engine 302, may collect image data from one or more data sources. Example data sources include the patient's EMR, the clinical facility's HIS records, and medical imaging systems. For example, during a visit to an imaging facility, the X-ray imaging system 204 may be used to generate one or more 2D and / or tomosynthesis X-ray images of the patient's breast(s). The X-ray images may be combined (or otherwise associated) with the patient's personal information and / or stored in one or more medical records or systems of the medical facility.

[0049] In operation 504, features of the image data may be identified. In certain aspects, the image data (or portions thereof) may be provided to an input processing component, such as the AI ​​processing component 208 and / or the decision supporter 210. In at least one example, the input processing component may be incorporated into an X-ray imaging system or device in which the exemplary method 500 is performed. The image data may be provided to the input processing component as the image data is being collected (e.g., in real time), immediately after the image data is collected, or at any other time after the image data is collected. The input processing component may comprise or have access to one or more rule sets, algorithms, or predictive models. The input processing component may evaluate the image data and identify one or more features of the image data. Image features may include, but are not limited to, edges or boundaries of shapes, interest points, and blobs. Identifying the features may include the use of feature detection and / or feature extraction techniques. Feature values ​​may be calculated for and / or assigned to each feature using one or more characterization techniques, such as ML processing, normalization operations, binning operations, and / or vectorization operations. The value of a feature may be a numerical representation of the feature, a value paired with the feature in the merged data, an indication of one or more disease states related to the feature, etc.

[0050] In operation 506, a confidence score may be calculated for the image features. In one aspect, the input processing component (or a component associated therewith) may use feature values ​​calculated for the identified image features to generate a confidence score. The confidence score may represent the probability that a particular feature matches a predefined feature or feature category / classification. Generating the confidence score may include comparing the image data features and / or feature values ​​to a set of labeled features and / or feature values, known ones, or predefined ones. For example, for a received image, four interest points may be identified and the four interest points may be assigned a respective feature value set. Each of the respective feature value sets may be compared to stored feature data from known images. The stored feature data may include various feature values ​​and may be labeled to classify the feature or image. For example, feature data sets may be listed for various breast abnormalities and / or mammogram findings. The confidence score may be generated based on a match or similarity between the feature values ​​for the received image and the stored feature values. In some aspects, the confidence score may be a numeric value, a non-numeric (or partially numeric) value, or a label. For example, the confidence value may be represented by a numeric value on a scale from 1 to 10, with "1" representing a low confidence match and "10" representing a high confidence match. In such an example, a higher confidence value may indicate a greater number (or percentage) of matches or similarities between the feature values ​​for the received image and the stored feature values.

[0051] In the determining operation 508, the confidence score may be compared to a threshold. In one aspect, an input processing component (or a component associated therewith) may compare the confidence score to a configurable confidence threshold. The confidence threshold may represent a level of confidence that must be met or exceeded before the image (or image data) is assigned a preferred read status. The confidence threshold may be selected based on a desired balance between screening positive cases (e.g., cases in which cancer is confirmed) and screening negative cases (e.g., cases in which no cancer is found). For example, in a specific example, the selected confidence threshold may result in the identification of a set of 1000 cases in which 70% of the cases are screening positive cases, 20% of the cases show a non-cancer abnormality, and 10% of the cases are screening negative cases. Each of the screening positive cases may be assigned a preferred read status. By increasing the selected confidence threshold, a reduced set of cases may be selected. For example, a set of 750 cases may be identified where 80% of the cases are screen positives, 15% of the cases indicate a non-cancer anomaly, and 5% of the cases are screen negatives. Alternatively, an expanded set of cases may be selected by decreasing the selected threshold. For example, a set of 1,250 cases may be identified where 60% of the cases are screen positives, 25% of the cases indicate a non-cancer anomaly, and 15% of the cases are screen negatives.

[0052] In some examples, the confidence threshold may be determined and configured manually. For example, a user may select or modify the confidence threshold using a user interface of the input processing component. The selection of the confidence threshold may be based on various factors. For example, for at least a portion of the X-ray imaging systems associated with a particular clinical facility, the confidence threshold may be selected based on whether a sufficient number of radiologists are associated with the clinical facility or how quickly radiologists are available to review cases with priority read status. In other examples, the confidence threshold may be determined automatically and / or dynamically by the input processing component. For example, feedback or output from one or more entities or components of the system 200 related to proposed medical paths, image read priorities, etc. may be accessible from the input processing component. The feedback / output may include accuracy ratings or comments from the technician, physician, or radiologist. The feedback / output may additionally include treatments, reports, patient notes, etc. Based on the feedback / output, the input processing component may modify the threshold and may increase or decrease the number of screening positive and / or negative cases identified.

[0053] In one aspect, if it is determined that the confidence score is below the confidence threshold, the flow proceeds to operation 510. In operation 510, the received image data may be assigned a standard level of priority (e.g., a standard priority level, a low priority level, or a non-priority level). A standard level of priority may indicate that the received image data should be evaluated according to the usual availability and / or workload of the associated medical personnel. For example, when the image data is assigned a standard level of priority, the image data may be added to an image reading queue. The position of the image data in the queue (e.g., the order in which the image data was added to the queue) may dictate the order of evaluation of the image data. As a specific example, in a first-in-first-out (FIFO) queue, any standard priority data items added to the queue prior to the received image data will be evaluated before the received image data. In such an example, the image data may not be evaluated even though the screening subject is still at the screening facility.

[0054] However, if it is determined that the confidence score meets or exceeds the confidence threshold, flow proceeds to operation 512. In operation 512, the received image data may be assigned a high level of priority. Assigning a high level of priority may include adding one or more indicators to the image data and / or metadata, such as, for example, a Digital Imaging and Communications in Medicine (DICOM) header for the image data. Exemplary indicators may include a label (e.g., "High Priority", "Priority", etc.), a numerical value, an emphasis, an arrow or pointer, a font or style modification, a date / time value, etc. In certain aspects, a high level of priority may indicate that the received image data should receive prioritized evaluation. As an example, when the image data is assigned a high level of priority, the image data may be added to an image reading queue. Based on the high level of priority, the image data may be evaluated before other data items in the queue having a lower priority level and / or a later time / date that entered the queue. As another example, a priority indicator for the image data assigned a high level of priority may be presented to one or more medical personnel. For example, upon assignment of a high level of priority to the image data, the priority indicator and / or the image data may be presented to the technician using a user interface of the x-ray imaging system or device. In at least one example, the priority indicator and / or the image may be presented to the technician as the technician is collecting the image data (e.g., in real-time). As yet another example, when the image data is assigned a high level of priority, the image data (or an indication thereof) may be sent to one or more destinations. For example, the radiologist may receive a message (e.g., an email, text, voice call, etc.) associated with the assignment of the priority of the image data. The message may include information such as the patient's current condition or location, a reading priority for the image data, current and / or past medical records for the patient, etc.As a particular example, the image data including the priority read indicator may be sent to a radiologist's image review workstation with an indication that the patient is currently in the clinic and awaiting reading of the image data. Alternatively, the image data may be sent to a software application or service used to manage the radiologist's workflow. The software application / service may be configured to create and / or assign a worklist of cases requiring immediate evaluation. In such an example, the high priority read indication may allow follow-up imaging and other actions to be performed while the screened subject is still in the screening clinic.

[0055] 6A illustrates an exemplary user interface 600 associated with the automated clinical workflow decisions described herein. In one example, the user interface 600 represents software used by a technician on a mammography acquisition workstation. The software may be used to collect images from an x-ray imaging system, such as x-ray imaging system 204, during a breast screening exam and / or to review images collected during a breast screening exam. The user interface 600 includes a button 602 that, when selected, activates an "Analysis" dialog.

[0056] FIG. 6B illustrates an analysis dialog 610 that is displayed when the button 602 of FIG. 6A is selected. The analysis dialog 610 includes a button 612, an analysis results section 614, and a read priority indication 616. In one aspect, when the button 612 is selected, image evaluation software is launched and one or more acquired images are analyzed using the techniques described in FIG. 3 and FIG. 4. As a result of the analysis, the analysis results section 614 is at least partially populated with data, such as a read priority indication 616. In FIG. 6B, the read priority indication 616 indicates that the read priority for the analyzed image(s) is "high." Based on the "high" read priority, the technologist may request that the screened subject remain at the facility while the radiologist reviews the acquired image(s). This immediate review by the radiologist (e.g., while the screened subject is at the facility) may reduce or eliminate the need to recall the screened subject for a follow-up appointment.

[0057] FIG. 7 illustrates an exemplary suitable operating environment for the technique for automating clinical workflow decisions described in FIG. 1. In its most basic configuration, the operating environment 700 typically includes at least one processing unit 702 and memory 704. Depending on the exact configuration and type of computing device, the memory 704 (which stores instructions for implementing the techniques disclosed herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in FIG. 7 by dashed line 706. Additionally, the environment 700 may also include storage devices (removable 708 and / or non-removable 710), including but not limited to magnetic or optical disks or tapes. Similarly, the environment 700 may also have input device(s) 714, such as a keyboard, mouse, pen, voice input, and / or output device(s) 716, such as a display, speakers, printer, etc. One or more communication connections 712, such as a LAN, WAN, point-to-point, etc., are also included in the environment. In an embodiment, a connection may be operable for point-to-point communication, connection-oriented communication, connectionless communication, and the like, of the devices.

[0058] The operating environment 700 typically includes at least some form of computer-readable media. Computer-readable media may be any available media that can be accessed by a processing unit 702 or other device with the operating environment. By way of example, and not limitation, computer-readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other non-transitory medium that can be used to store the desired information. Computer storage media does not include communication media.

[0059] Communication media embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transmission mechanism, and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, microwave and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0060] The operating environment 700 may be a single computer operating in a networked environment with logical connections to one or more remote computers. The remote computers may be personal computers, servers, routers, network PCs, peer devices or other nodes of a common network, and typically include many or all of the elements listed above, as well as others not so mentioned. The logical connections may include any manner supported by the available communications media. Such networked environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.

[0061] The embodiments described herein may be employed using software, hardware, or a combination of software and hardware to implement and perform the systems and methods disclosed herein. Although specific devices have been recited throughout this disclosure as performing certain functions, those skilled in the art will understand that those devices are provided for illustrative purposes and that other devices may be employed to perform the functionality disclosed herein without departing from the scope of the present disclosure.

[0062] This disclosure describes several embodiments of the technology with reference to the accompanying drawings, in which only some of the possible embodiments are shown. However, other aspects may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the possible embodiments to those skilled in the art.

[0063] Although specific embodiments are described herein, the scope of the technology is not limited to those specific embodiments. Those skilled in the art will recognize other embodiments or improvements that are within the scope and spirit of the technology. Thus, specific structures, acts or mediums are disclosed as exemplary embodiments only. The scope of the technology is defined by the following claims and any equivalents therein.

Claims

1. 1. A system comprising: At least one processor; a memory coupled to the at least one processor; Equipped with The memory comprises computer-executable instructions that, when executed by the at least one processor, perform a method; The method comprises: collecting patient data for a patient from one or more data sources, the patient data including digital images of the breast from a medical imaging system; providing the patient data to a first artificial intelligence (AI) algorithm to analyze characteristics of the patient data; receiving a first output from the first AI algorithm, the first output comprising breast assessment data; providing the first output comprising breast assessment data to the x-ray imaging system (204), the additional imaging system(s) (224), and / or one or more computing devices, thereby instructing the x-ray imaging system (204), the additional imaging system(s) (224), and / or one or more computing devices to present the first output to a user; receiving input from the user whether additional imaging should be performed based on the presented first output; when the input indicates that the additional imaging should be performed, causing the X-ray imaging system (204) and / or additional imaging system(s) (224) to perform the additional imaging for the patient, such as a recommended diagnostic procedure including magnification / contrast diagnostic imaging and / or a supplemental screening procedure including ultrasound imaging; providing the first output and the additional imaging to a second AI algorithm to determine a clinical workflow decision regarding patient care; receiving a second output from the second AI algorithm, the second output comprising an automated patient care recommendation; and providing the automated patient care recommendations to a health care professional, the automated patient care recommendations including diagnostic information; Including, the system.

2. The system of claim 1, wherein receiving the first output from the first AI algorithm includes receiving results of computer-assisted detection markers to be added to the digital image of the breast, and the method further includes providing results of the computer-assisted detection markers to be added to the digital image of the breast to the X-ray imaging system (204) and / or additional imaging system(s) (224).

3. The system of claim 2, wherein additional digital images of the breast are acquired by the X-ray imaging system (204) and / or additional imaging system(s) (224) based on the results of the computer-assisted detection markers being added to the digital images of the breast.

4. the patient data comprises at least one of patient personal information or patient history information; or The one or more data sources comprise at least one of patient visit information, patient electronic medical records (EMR), and hospital information system (HIS) records; or The first AI algorithm provides at least one of a breast density assessment, a breast composition category score, a radiometric signature, or a breast cancer risk assessment result; or The first output comprises at least one of a breast composition category score, a computer-aided detection marker result, a radiometric characteristic, or a breast cancer risk assessment result; or Supplemental information is further provided to the second AI algorithm to determine clinical workflow decisions regarding patient care, the supplemental information comprising at least one of the patient data, a diagnostic report, and practice guidelines from a clinical professional society; or The system of claim 1 , wherein the automated patient care recommendations comprise recommendations related to at least one of a biopsy procedure, chemotherapy, surgical intervention, radiation therapy, or a mammography imaging procedure.

5. The method comprises: providing at least a portion of the first output to the medical personnel; receiving an initial patient care recommendation from the health care provider; supplementing the initial patient care recommendations with the automated patient care recommendations; The system of claim 1 further comprising:

6. The method comprises: acquiring one or more digital mammography images from the patient during a first visit; providing the one or more digital mammography images to the first AI algorithm to generate the first output; providing the first output to the medical personnel; and Further comprising: The system of claim 1 , wherein the first output enables the medical personnel to perform one or more diagnostic procedures on the patient during the first visit.

7. A method for automating determination of a clinical workflow for execution by a processor in a system, the method comprising: the processor collecting patient data for the patient from one or more data sources, the patient data including digital images of the breast from a medical imaging system; providing the patient data to a first artificial intelligence (AI) component for analyzing characteristics of the patient data; the processor receiving a first output from the first AI component, the first output comprising breast assessment data; and the processor instructing the x-ray imaging system (204), the additional imaging system(s) (224), and / or one or more computing devices to present the first output to a user by providing the first output comprising breast assessment data to the x-ray imaging system (204), the additional imaging system(s) (224), and / or one or more computing devices; receiving an input from the user regarding whether additional imaging should be performed based on the presented first output; when the input indicates that the additional imaging should be performed, causing the x-ray imaging system (204) and / or additional imaging system(s) (224) to perform the additional imaging for the patient, such as a recommended diagnostic procedure (e.g., magnification / contrast diagnostic imaging) and / or a supplemental screening procedure (e.g., ultrasound imaging); the processor providing the first output and the additional imaging to a second AI component for determining clinical workflow decisions regarding patient care; the processor receiving a second output from the second AI component, the second output comprising an automated patient care recommendation; and the processor providing the automated patient care recommendations to a medical professional, the automated patient care recommendations including diagnostic information; A method comprising:

8. The method of claim 7, wherein the processor receiving the first output from the first AI algorithm includes the processor receiving results of computer-aided detection markers being added to the digital image of the breast, and the method further includes the processor providing results of the computer-aided detection markers being added to the digital image of the breast to the X-ray imaging system (204) and / or additional imaging system(s) (224).

9. 9. The method of claim 8, wherein additional digital images of the breast are acquired by the X-ray imaging system (204) and / or additional imaging system(s) (224) based on the results of the computer-assisted detection markers being added to the digital images of the breast.

10. the patient data comprises at least one of a digital mammography image, patient demographic information, or patient history information; or the first AI component providing an assessment of the density of the patient's breast; or The first output comprises at least one of a breast composition category score, a computer-aided detection marker result, a radiometric characteristic, or a breast cancer risk assessment result; or the second AI component implements at least one of an artificial neural network, a support vector machine (SVM), a linear reinforcement model, or a random decision forest; or The method of claim 7 , wherein the automated patient care recommendations comprise recommendations related to at least one of a biopsy procedure, chemotherapy, surgical intervention, radiation therapy, or a mammography imaging procedure.

11. The method comprises: the processor providing at least a portion of the first output to the medical practitioner; receiving an initial patient care recommendation from the medical provider; the processor supplementing the initial patient care recommendations with the automated patient care recommendations. The method of claim 7 further comprising:

12. The method comprises: the processor acquiring one or more digital mammography images from the patient during a first visit; the processor providing the one or more digital mammography images to the first AI algorithm to generate the first output; the processor providing the first output to the medical personnel; Further comprising:

8. The method of claim 7, wherein the first output enables the medical practitioner to perform one or more diagnostic procedures on the patient during the first visit.

Images