Radiology operations command center local technician - super user matching

Abstract

A method (100) of connecting a local medical imaging device operator (LO) with a remote medical imaging expert (RE) during an imaging examination performed using a medical imaging device (2) includes determining characteristics of available remote medical imaging experts that are available to assist the local operator; matching one or more of the available remote medical imaging experts with characteristics of the imaging examination based on the determined characteristics of the remote medical imaging experts; and providing a user interface (UI) (28, 28') to at least one display device (24, 24') operable by the local operator and the remote medical imaging expert, the UI displaying a list (44) of the matched available remote medical imaging experts, and the local medical imaging device operator being able to select a matched available medical imaging expert from the displayed list via the user interface.

Description

Technical Field

[0001] The following generally covers imaging technology, remote imaging assistance technology, remote imaging inspection and monitoring technology, and related technologies. Background Technology

[0002] Currently, the demand for diagnostic imaging is high. With the world's population aging, the need for rapid, safe, and high-quality imaging is only likely to continue to grow, placing further pressure on imaging centers and their staff. To perform imaging examinations on patients quickly and safely, and to maintain high throughput and quality standards, imaging providers must establish effective workflows that protect them from disruption.

[0003] Some common workflow disruptions include a lack of proper guidance for newly appointed technicians, technicians' lack of experience with imaging machines from various vendors with different control interfaces and features, and a lack of understanding of imaging protocols. These are just some examples of common events that, if encountered during a scan, can adversely affect the current imaging check and delay subsequent checks, potentially disrupting the entire workday. With proper preparation, many of these disruptions can be mitigated or completely avoided by providing on-the-spot guidance to technicians.

[0004] The following discloses some improvements to overcome these and other problems. Summary of the Invention

[0005] In one aspect, a non-transient computer-readable medium stores instructions executable by at least one electronic processor to perform a method of connecting a local medical imaging device operator to a remote medical imaging expert during an imaging examination performed using a medical imaging device. The method includes: determining characteristics of available remote medical imaging experts that can be used to assist the local operator; matching one or more of the available remote medical imaging experts with characteristics of the imaging examination based on the determined characteristics of the remote medical imaging experts; and providing a user interface to at least one display device operable by the local operator and the remote medical imaging expert, the UI displaying a list of matched available remote medical imaging experts, and the local medical imaging device operator being able to select a matched available medical imaging expert from the displayed list via the user interface.

[0006] In another aspect, an apparatus for connecting a local medical imaging equipment operator during an imaging examination performed using a medical imaging equipment includes: a screen-sharing device for sharing the screen of the controller of the medical imaging equipment. A telephone or video communication link is operatively connected to an electronic network for providing telephone or video communication with a telemedicine imaging expert in a group of telemedicine imaging experts. A database stores defined characteristics of the telemedicine imaging experts in the group, including at least one of the following: experience level regarding the modality of the imaging examination; experience level regarding the anatomical structures of the patient to be imaged during the imaging examination; experience level regarding working with the local operator; and experience level regarding the types of problems that occur during the imaging examination. At least one electronic processor is programmed to: retrieve characteristics of one or more telemedicine imaging experts from the database, the one or more telemedicine imaging experts being available to assist the local operator in the imaging examination; rank the available telemedicine imaging experts based on matching the characteristics of the available telemedicine imaging experts with the characteristics of the imaging examination; and provide a UI that displays a list of ranked available telemedicine imaging experts, enabling the local operator to select one of the listed available telemedicine imaging experts and establish an assistance session between the local operator and the selected telemedicine imaging expert via the screen sharing device and the telephone or video communication link.

[0007] In another aspect, a method for connecting a telemedicine imaging expert to a local operator to provide assistance during an imaging examination includes: retrieving from a database the characteristics of one or more telemedicine imaging experts that can be used to assist the local operator during the imaging examination; ranking the available telemedicine imaging experts using scores indicating the characteristics of the available telemedicine imaging experts relative to the characteristics of the imaging examination; providing a UI that displays a list of the ranked available telemedicine imaging experts; receiving user input via at least one user input device indicating a selection of one of the listed telemedicine imaging experts; collecting data related to the effectiveness of the assistance provided by the selected telemedicine imaging expert and the local operator; determining one or more quality metrics associated with the collected data; and updating the information stored in the database for the selected telemedicine imaging expert using the calculated one or more quality metrics.

[0008] One advantage is that it provides remote specialists or radiologists assisting technicians in performing medical imaging examinations with contextual awareness of one or more local imaging examinations, which helps to provide effective assistance to one or more local operators at different facilities.

[0009] Another advantage is that it provides remote experts or radiologists to assist technicians in performing medical imaging examinations, where the remote experts or radiologists are well matched with the imaging modalities and imaging examinations.

[0010] Another advantage is the pre-selection of remote experts or radiologists to assist technicians in ongoing medical imaging examinations, where the remote expert or radiologist is well-matched to the imaging modality and imaging examination, and where the pre-selected remote expert or radiologist is ready to respond quickly to any automatically detected errors during the ongoing imaging examination.

[0011] Another advantage is that it provides remote experts with information about the steps that have been performed in the workflow, in order to assist in subsequent steps in the workflow.

[0012] Another advantage is that it matches the most suitable remote expert to provide assistance to one or more local operators based on the remote expert's experience level and the characteristics of the imaging examination performed by one or more local operators.

[0013] Another advantage is that it provides remote operators or radiologists with status information about medical imaging examinations using a standard display format that is independent of the controller display of the medical imaging equipment performing the examination.

[0014] The given embodiments may provide zero, one, two, or all of the foregoing advantages, and / or may provide other advantages that will become apparent to those skilled in the art upon reading and understanding this disclosure. Attached Figure Description

[0015] This disclosure can take the form of various components and their arrangements, as well as various steps and their arrangements. The accompanying drawings are for illustrative purposes only and should not be construed as limiting this disclosure.

[0016] Figure 1 An illustrative device for providing remote assistance according to the present disclosure is shown schematically.

[0017] Figure 2 Schematic illustration of the work by Figure 1 The module implemented by the device.

[0018] Figure 3 It shows the result of Figure 1 An example of the output generated by the device.

[0019] Figure 4 It shows the result of Figure 1 An example flowchart of the operation performed appropriately by the device. Detailed Implementation

[0020] The following relates to a Radiology Operations Control Center (ROCC) system and method for providing remote expert or “super-technician” assistance to local technicians performing imaging examinations. Rapid identification of qualified super-technicians for assisting a given imaging examination is valuable. Delays in providing super-technicians can adversely affect imaging laboratory workflows. Furthermore, in some disclosed embodiments, it is contemplated that super-technician assistance be provided in response to certain automatically detected errors or problems during an imaging examination, again requiring super-technicians to be immediately available upon detection of such errors.

[0021] In some embodiments disclosed herein, the hyper-technical technician matching system matches the best available hyper-technical technician with a local technician and / or the current imaging examination. For this purpose, the system tracks available hyper-technical technicians. A database stores information about each hyper-technical technician regarding their expertise in various imaging modalities, imaging anatomy, etc. For matching with a specific local technician, the database stores similar information about the local technician. For matching with a specific imaging procedure, information about the imaging procedure is stored. The database may have an auxiliary information mining system for obtaining information about the current imaging examination directly from the imaging scanner controller or from a radiology information system (RIS) or other examination scheduling system.

[0022] Because the database can store a wide range of features to characterize high-technicians, local technicians, and / or imaging examinations, feature selection or reduction processes can be optionally run for a given match. For example, this can be implemented as principal component analysis (PCA) to generate highly discriminative features.

[0023] Based on a (optionally reduced) feature set, available super-technicians are matched with local technicians and / or imaging examinations. In one matching approach, clustering algorithms or other machine learning (ML) components group available super-technicians based on experience across various modalities (e.g., skill levels 1-5 for a given modality and anatomy) and rank the available super-technicians based on these groups. In another approach, a (non-machine learning) scoring system is employed to score the degree of match between each available super-technician and local technician and / or examination, and the super-technicians are ranked by their scores.

[0024] A user interface (UI) is provided, through which the highest-matching super-technicians are presented to the local technician. In one approach, a list of the top N highest-matching super-technicians is provided in a selection dialog box. The local technician selects a super-technician from the top N selection dialog boxes of the top N closest matches, and more detailed information about the selected super-technician is displayed in a UI presentation window. If the local technician is satisfied with the selected super-technician, they select a "Connect" button, etc., to initiate a ROCC session with the selected super-technician. In a variation of this method, the UI may be presented to a third party, such as a ROCC administrator who selects a super-technician and initiates a ROCC session.

[0025] In another variant embodiment, the UI is instead presented to the most qualified senior technician. This approach may be appropriate in the case of a ROCC session triggered by an automatically detected error condition during an ongoing imaging inspection. In this case, the UI pops up to the highest-ranking senior technician, where the information displayed is from the imaging inspection in which the error occurred (and possibly also information about the local technician who performed the inspection). This is used by the senior technician who can then accept an invitation to initiate an ROCC session by activating a "Connect" button, etc. Alternatively, the senior technician can decline the invitation using an appropriate UI dialog selector, in which case the invitation is presented to the next most qualified senior technician.

[0026] Optionally, the system may collect quality metrics, such as data on whether a given matched highly skilled technician is able to effectively assist in imaging examinations. This collected data can be used by a human maintainer who can fine-tune database features, one or more machine learning components, etc., to optimize the system's performance. In a variant approach, the system may employ adaptive learning, such as pattern matching, to adaptively adjust one or more machine learning components to maximize metrics such as the effectiveness of assistance provided by the matched highly skilled technician.

[0027] refer to Figure 1 This illustrates a device for providing assistance from a remote medical imaging specialist (RE) (or ultra-high-technician) to a local technician operator (LO). For example... Figure 1As shown, the local operator (LO) operating the medical imaging device (also known as an image acquisition device, imaging device, etc.) 2 is located in the medical imaging device compartment 3, and the remote operator (RE) is located in a remote service location or center 4. It should be noted that the "remote operator" RE may not necessarily operate the medical imaging device 2 directly, but rather provide assistance to the local operator (LO) in the form of advice, guidance, instructions, etc. The remote location 4 can be a remote service center, a radiologist's office, a radiology department, etc. The remote location 4 can be in the same building as the medical imaging device compartment 3 (this could be, for example, in the case of a "remote operator" RE for a radiologist responsible for reviewing perimeter images), but more typically, the remote service center 4 and the medical imaging device compartment 3 are in different buildings and can actually be located in different cities, different countries, and / or different continents. Typically, the remote location 4 is located away from the imaging device compartment 3 in the sense that the remote operator (RE) cannot directly visually observe the imaging device 2 in the imaging device compartment 3 (and therefore optionally provide video feeds, as further described herein).

[0028] Image acquisition device 2 can be a magnetic resonance (MR) image acquisition device, a computed tomography (CT) image acquisition device, a positron emission tomography (PET) image acquisition device, a single-photon emission computed tomography (SPECT) image acquisition device, an X-ray image acquisition device, an ultrasound (US) image acquisition device, or another modality of medical imaging device. Imaging device 2 can also be a hybrid imaging device, such as a PET / CT or SPECT / CT imaging system. Although in Figure 1 A single image acquisition device 2 is illustrated in the diagram, but more typically, a medical imaging laboratory will have multiple image acquisition devices, which may have the same and / or different imaging modalities. For example, if a hospital performs many CT imaging examinations and relatively few MRI examinations and even fewer PET examinations, the hospital's imaging laboratory (sometimes called a "radiology laboratory" or some other similar nomenclature) may have three CT scanners, two MRI scanners, and only a single PET scanner. This is merely an example. Furthermore, a remote service center 4 can provide services to multiple hospitals. The local operator controls the medical imaging device 2 via the imaging device controller 10. The remote operator is located at a remote workstation 12 (or more generally, an electronic controller 12).

[0029] As used herein, the term "medical imaging equipment compartment" (and variations thereof) refers to the room containing the medical imaging equipment 2 and any adjacent control room containing the medical imaging equipment controller 10 for controlling the medical imaging equipment. For example, referring to an MRI apparatus, the medical imaging equipment compartment 3 may include a radio frequency (RF) shielded room containing the MRI equipment 2 and an adjacent control room housing the medical imaging equipment controller 10, as understood in the field of MRI equipment and procedures. On the other hand, for other imaging modalities such as CT, the imaging equipment controller 10 may be located in the same room as the imaging equipment 2, such that there is no adjacent control room, and the medical compartment 3 is simply the room containing the medical imaging equipment 2. Furthermore, although... Figure 1 A single medical imaging device compartment 3 is shown, but it should be understood that a remote service center 4 (and more specifically, a remote workstation 12) communicates with multiple medical compartments via a communication link 14, which typically includes the Internet enhanced by a local area network at the remote operator (RE) and local operator (LO) ends for electronic data communication.

[0030] like Figure 1 As schematically illustrated, in some embodiments, a camera 16 (e.g., a video camera) is arranged to capture a video stream 17 of a portion of a medical imaging device compartment 3, which includes at least the area of ​​the imaging device 2 where the local operator LO interacts with the patient, and optionally may also include an imaging device controller 10. The video stream 17 is transmitted, for example, as a streaming video feed received via a secure internet link to a remote workstation 12 via a communication link 14.

[0031] In other embodiments, as illustrated in the embodiment, the live video feed 17 is provided by a video cable splitter 15 (e.g., a DVI splitter, an HDMI splitter, etc.). In other embodiments, the live video feed 17 may be provided by a video cable connecting the auxiliary video output (e.g., aux vid out) port of the imaging device controller 10 to a remote workstation 12 operated by a remote expert (RE).

[0032] Additionally or alternatively, the screen mirroring data stream 18 is generated by the screen sharing device 13 and transmitted from the imaging device controller 10 to the remote workstation 12. The communication link 14 also provides a natural language communication path 19 for verbal and / or text communication between the local and remote operators. For example, the natural language communication link 19 could be a Voice over Internet Protocol (VoIP) telephone connection, an online video chat link, a computerized instant messaging service, etc. Alternatively, the natural language communication path 19 could be provided by a dedicated communication link separate from the communication link 14 that provides data communications 17, 18; for example, the natural language communication path 19 could be provided via a landline telephone.

[0033] Figure 1 The diagram also illustrates a remote workstation 12, such as an electronic processing device, workstation computer, or more generally a computer, operatively connected to receive and present video 17 from the medical imaging device compartment 3 of camera 16 and to present screen mirroring data stream 18 as a mirrored screen within the remote service center 4. Alternatively or additionally, the remote workstation 12 may be implemented as a server computer or, for example, multiple server computers interconnected to form a server cluster, cloud computing resources, etc. The workstation 12 includes typical components such as an electronic processor 20 (e.g., a microprocessor), at least one user input device 22 (e.g., a mouse, keyboard, trackball, etc.), and at least one display device 24 (e.g., an LCD display, a plasma display, a cathode ray tube display, etc.). In some embodiments, the display device 24 may be a separate component from the workstation 12. The display device 24 may also include two or more display devices, for example, one display presenting video 17 and another display presenting a shared screen of the imaging device controller 10 generated from the screen mirroring data stream 18. Alternatively, the video and the shared screen may be presented in corresponding windows on a single display. Electronic processor 20 is operatively connected to one or more non-transient storage media 26. By way of non-limiting illustrative example, non-transient storage media 26 may include one or more of the following: disks, RAID or other magnetic storage media; solid-state drives, flash drives, electrically erasable read-only memory (EEROM) or other electronic storage; optical disks or other optical storage devices; various combinations thereof; etc.; and may be, for example, network storage devices, internal hard disk drives of workstation 12, various combinations thereof, etc. It should be understood that any reference herein to one or more non-transient media 26 is to be interpreted broadly as encompassing a single medium or multiple media of the same or different types. Similarly, electronic processor 20 may be implemented as a single electronic processor, or two or more electronic processors. Non-transient storage media 26 stores instructions executable by at least one electronic processor 20. Instructions include instructions for generating a graphical user interface (GUI) 28 for display on a remote operator display device 24.

[0034] The medical imaging device controller 10 in the medical imaging device compartment 3 also includes components similar to the remote workstation 12 located in the remote service center 4. Unless otherwise stated herein, the features of the medical imaging device controller 10 (which includes the local workstation 12') located in the medical imaging device compartment 3 (similar to the features of the remote workstation 12 located in the remote service center 4) have common reference numerals followed by an "apostrophe" symbol, and the description of the components of the medical imaging device controller 10 will not be repeated. Specifically, the medical imaging device controller 10 is configured to display a GUI 28' on a display device or controller display 24', which presents information related to the control of the medical imaging device 2, such as configuration displays for adjusting configuration settings, alarms 30 that are perceptible at a remote location when status information regarding the medical imaging examination meets the alarm criteria of the imaging device 2, imaging acquisition monitoring information, and the presentation of acquired medical images, etc. It should be understood that a screen mirror data stream 18 carries the content presented on the display device 24' of the medical imaging device controller 10. Communication link 14 allows screen sharing between display device 24 in remote service center 4 and display device 24' in medical imaging equipment compartment 3. GUI 28' includes one or more dialog screens, including, for example, an examination / scan selection dialog screen, a scan settings dialog screen, an acquisition monitoring dialog screen, etc. GUI 28' can be included in video feed 17 or mirror data stream 17' and displayed on remote workstation monitor 24 at remote location 4.

[0035] Figure 1 A remote workstation 12 is also shown that communicates with a database 31 storing patient information (e.g., an electronic health record (EHR) database, an electronic medical record (EMR) database, a radiology information system (RIS) database, etc.).

[0036] Figure 1The illustration depicts a local operator (LO) and a remote specialist (RE) (i.e., an expert, such as a highly skilled technician). However, in a Radiology Operations Command Center (ROCC) as envisioned herein, the ROCC provides highly skilled technicians who can assist local operator LOs in various hospitals, radiology laboratories, etc. The ROCC may be housed in a single physical location or may be geographically distributed. For example, in one anticipated implementation, remote operator ROs are recruited from the United States and / or internationally to provide highly skilled technicians with extensive expertise across various imaging modalities and imaging processes targeting various imaging anatomy structures. Given this multiple local operator LO and multiple remote operator ROs, the disclosed communication link 14 includes server computers 14s (or server clusters, cloud computing resources including servers, etc.) programmed to establish connections between selected local operator LO / remote specialist RE pairs. For example, if the server computers 14s are internet-based, specific selected local operator LO / remote specialist RE pairs can be connected using various components 16, 10, 12 with Internet Protocol (IP) addresses, natural language communication paths 19 with telephones or video terminals, etc. Server computer 14s is operatively connected to one or more non-transient storage media 26s. By way of non-limiting illustrative example, non-transient storage media 26s may include one or more of the following: disks, RAID or other magnetic storage media; solid-state drives, flash drives, electrically erasable read-only memory (EEROM) or other electronic storage; optical discs or other optical storage devices; various combinations thereof; etc.; and may be, for example, network storage devices, internal hard drives of server computer 14s, various combinations thereof, etc. It should be understood that any reference herein to one or more non-transient media 26s should be broadly interpreted to cover a single medium or multiple media of the same or different types. Similarly, server computer 14s may be implemented as a single electronic processor or two or more electronic processors. Non-transient storage media 26s store instructions executable by server computer 14s. Additionally, non-transient computer-readable media 26s (or another database) store data related to a set of remote experts (REs) and / or a set of local operators (LOs). Remote expert data can include, for example, skillset data, work experience data, data related to the ability to work in a multi-vendor modality, and data related to the experience of the local operator (LO).

[0037] Furthermore, as disclosed herein, server 14s executes expert matching method or process 100, which matches available qualified remote experts (REs) with a given local operator (LO).

[0038] Now for reference Figure 2 And continue to refer to Figure 1In one embodiment of the expert matching method or process 100, the server 14s is programmed with several components to assist remote expert REs. The principal component analysis model 32 is configured to analyze remote expert data stored in a database 41 (which may be a non-transient computer-readable medium 26s) and identify characteristics used to perform medical imaging examinations, such as the modality of the medical imaging device 2, the supplier of the medical imaging device, the type of protocol to be used in the medical imaging examination, the anatomical structures to be imaged, the condition of the patient to be imaged, the prior experience of the local operator LO in handling relevant cases, the availability of remote experts, etc. Specifically, the availability of each remote expert RE is retrieved. In some examples, the principal component analysis model 32 may be a machine learning (ML) model.

[0039] Principal component analysis model 32 was configured to classify remote specialists (REs) into skill levels on a scale of 1–5. A “Level 1 Specialist” may have approximately 0–2 years of experience, be proficient in patient care and safety, and will require support and guidance (e.g., from more experienced high-level technicians) in refining their image acquisition foundation. A “Level 2 Specialist” may have approximately 2–3 years of experience, be proficient in patient care and safety, be able to produce high-quality images, and will need to consolidate their knowledge to gain confidence in making independent decisions and being exposed to new, more challenging cases. A “Level 3 Specialist” has at least 3–5 years of experience and is highly capable of acquiring standard images. A “Level 4 Specialist” has 5 or more years of experience, extensive knowledge of advanced imaging, can provide advice on checklists, can anticipate most imaging protocols, and should be able to maintain and update relationships with other technicians according to preferences. A “Level 5 Specialist” has at least 10 years of experience, strong knowledge of advanced medical imaging, can set up checklists, can anticipate all imaging protocols, should be able to support the retention of all knowledge to assist the local operator (LO), and should be able to maintain up-to-date relationships with technicians according to preferences.

[0040] Data characterizing remote expert REs can be provided as input to principal component analysis model 32. To do this, remote expert REs can, for example, complete a questionnaire upon joining the ROCC to provide information about their experience with different modalities, imaging anatomy, etc. In one embodiment, principal component analysis model 32 includes, for example, five features for use in the model, wherein the features are calculated based on the remote expert's relevant work experience and the corresponding medical imaging examination complexity condition. For example, a set of features can be obtained from the work experience of the ultra-highly skilled technician (denoted as ST-WE) combined with the complexity of the medical imaging examination to produce binary features. If the first quartile, median, and third quartile of ST-WE, and their correlation with examination complexity (REC) or the support required to perform the scan, are 10, 20, and 30, the following four features can be defined: "Is the ST-WE_REC value less than 10?", "Is the ST-WE_REC value greater than or equal to 10?", "Is the ST-WE_REC value greater than or equal to 20?", and "Is the ST-WE_REC value greater than or equal to 30?". Other binarized features, such as ST-WE_ScannerType and ST-WE_Vendor, can be obtained similarly. Other constraints on the principal component analysis model 32 may include constraints on accuracy, such as the area under the curve (AUC) value.

[0041] ML module 34 is configured to categorize all remote expert REs in the group of remote experts into various categories based on factors such as their experience working with different imaging modalities, patient condition, complexity of the medical imaging examination, modality, modality provider, and local operator (LO) experience. One or more ML models 35 can be generated and used to predict a set of variables related to the medical imaging examination. Model 35, together with principal component analysis model 32 and remote expert RE data stored in non-transient computer-readable medium 26, is used to find the “best-fit” remote expert to assist the local operator (LO) in the medical imaging examination. For example, model 35 can be used to categorize remote expert REs by importance into the following categories: exact match, relevant experience, exact examination experience but no modality experience, modality experience but no examination experience, and no modality or examination experience.

[0042] The quality metric check module 36 is configured to simulate remote expert (RE)-local operator (LO) pairing for future pairing predictions and also monitors quality metric values ​​37, such as AUC values ​​from principal component analysis models 32 and 35.

[0043] Decision module 38 is configured to determine whether the results from ML module 34 and quality metric check module 36 meet a predetermined satisfactory threshold. If the threshold is not met, the remote experts (REs) can change the input constraints on principal component analysis model 32 and obtain a new set of remote experts based on the new input constraints and a new set of quality metric values ​​37.

[0044] If the threshold is met, the allocation module 40 is configured to identify the best remote expert (RE) (or a top N list of the N highest-ranked expert REs) for medical imaging examinations (e.g., selecting an accurate sequence for imaging and successfully obtaining high-quality images) and match the best remote expert with the local operator (LO). If a match is made, the resulting model 35 and quality metric 37 can be stored in the database 41 (or alternatively, in a non-transient computer-readable medium 26) for future matching.

[0045] The GUI output module 42 is configured to output list 44' (via GUI 28) on the display device 24 of the remote workstation 12, showing a list 44 with the best available remote experts (REs) and one or more corresponding quality metrics (e.g., confidence level, value, AUC value, etc.). List 44 may include only a set number of best available remote experts (REs), such as three best available experts and their corresponding quality metrics 37.

[0046] Figure 3 An example from List 44 is shown. For example... Figure 3 As shown, list 44 includes three best available remote expert REs, and corresponding quality metrics 37 including confidence values ​​and AUC values. When one of the listed remote expert REs is selected via at least one user input device 22, 22', a drop-down menu 46 listing a set of empirical metrics 48 for the remote expert RE can be displayed. Figure 3 As shown, the set of experience metrics 48 may include brain imaging experience metrics, spinal imaging experience metrics, liver imaging experience metrics, cardiac imaging experience metrics, knee imaging experience metrics, and whole-body imaging experience metrics. These metrics 48 are compared with the corresponding experience metrics 50 shown in the drop-down menu 52 of the local operator (LO). This allows the selection of the best available remote expert (RE) to assist the local operator (LO). Additionally, list 44 may include a communication button 54 selectable by either the remote expert (RE) or the local operator (LO) to establish a natural language communication path 19 via the communication link 14 between the two parties.

[0047] When a remote expert (RE) is selected to provide assistance to a given local operator (LO), communication link 14 connects the local operator (LO) to the selected remote expert (RE). The remote workstation 12 of the selected remote expert (RE) and / or the medical imaging device controller 10 operated by the local operator (LO) are configured to execute a method or process 200 for providing assistance from the remote expert (RE) to the local operator (LO). For brevity, method 200 will be described as being executed by the remote workstation 12. Non-transient storage medium 26 stores instructions that can be read and executed by at least one electronic processor 20 (of the workstation 12 shown) and / or one or more electronic processors of one or more servers on a local area network or the Internet to perform the disclosed operations (including the execution of method or process 200).

[0048] A suitable implementation of the auxiliary method or process 200 is as follows. Method 200 is performed during at least a portion of a medical imaging examination performed using the medical imaging device 2, and the local expert RE is a local expert selected via matching method 100. As used herein, the term "duration of medical imaging examination" (or a variation thereof) refers to a period of time during which the medical imaging examination includes (i) the actual image acquisition time, (ii) the post-imaging processing time, and (iii) the time until patient release. To perform method 200, workstation 12 in remote location 4 is programmed to receive at least one of the following: (i) video 17 from camera 16 of the medical imaging device 2 located in the medical imaging device compartment 3; and / or (ii) screen sharing 18 from screen sharing device 19; and / or (iii) video 17 tapped by video cable splitter 15. Video feed 17 and / or screen sharing 18 may be displayed at the remote workstation monitor 24, typically in a separate window of GUI 28. Screen captures can be performed on video feed 17 and / or screen sharing 18 to determine information relevant to the medical imaging examination (e.g., modality, vendor, anatomical structures to be imaged, cause of the problem to be addressed, etc.). Specifically, the GUI 28 presented on the display 24 of the remote workstation 12 preferably includes a window presenting video 17, a window presenting a mirrored screen of the medical imaging device controller 10 constructed from the screen mirroring data stream 18, and status information about the medical imaging examination maintained at least in part using screen capture information. This allows the remote operator (RE) (via screen sharing) to be aware of the contents of the display of the medical imaging device controller 10, and also (via feed 17) to be aware of the physical condition, such as the patient's position within the medical imaging device controller 10, and additionally to be aware of the status of the imaging examination summarized by the status information. During the imaging procedure, a natural language communication path 19 is suitably used to allow the local operator (LO) and the remote operator (RE) to discuss the procedure, and particularly allows the remote operator to provide advice to the local operator.

[0049] refer to Figure 4 And continue to refer to Figure 1-3 An illustrative embodiment of expert matching method 100 is schematically shown as a flowchart. At operation 102, information about a medical imaging examination to be performed by the local operator (LO) is collected. In one approach, the information is transmitted to server 14s via an imaging lab scheduling system that schedules the imaging examination. In another contemplated approach, the local operator (LO) completes an electronic expert assistance request form (e.g., via a webpage) pushed to the local operator (LO) by server 14s, and the form requests relevant information about the imaging examination (e.g., modality, vendor, anatomical structure to be imaged, cause of the problem to be solved, etc.). In yet another contemplated approach, video feed 17 and / or screen sharing 18 are captured at server 14s and screen-captured to determine information related to the medical imaging examination (e.g., modality, vendor, anatomical structure to be imaged, cause of the problem to be solved, etc.). In some examples, the information collected during operation 102 may include the availability of different remote experts (REs).

[0050] At operation 104, the characteristics of available remote medical imaging specialists (REs) that can be used to assist the local operator (LO) are determined. To do this, remote specialist data stored in non-transient computer-readable medium 26 can be retrieved and input into principal component analysis model 32. A feature selection process such as principal component analysis (PCA) can be performed on the retrieved data and screen capture information from video 17 and screen sharing 18 to generate principal component analysis model 32 and determine the characteristics of available remote specialist REs. The characteristics of available remote specialist REs may include at least one of the following: experience level regarding the imaging modality of the imaging examination; experience level regarding the anatomical structures of the patient to be imaged during the imaging examination; experience level regarding working with the local operator (LO); experience level regarding the type of problem occurring during the imaging examination, etc. In some examples, the availability data collected at operation 102 can be combined with the scheduling information of the imaging examination to determine not only which remote specialist REs are available, but also the duration of their availability. This allows for a comparison of the expected duration of the imaging examination with the availability duration of the remote specialists to avoid situations where remote specialists become unavailable before the imaging examination (including subsequent processing time until patient release) is completed. This reduces the likelihood that patients will not need to return for a follow-up appointment. A list of "coming soon" remote specialists (REs) can also be included.

[0051] At operation 106, the available remote expert REs are ranked. To do this, an ML operation is applied to principal component analysis model 32 to rank the available remote expert REs. This is performed using ML module 34 and the generated model 35. Furthermore, a quality metric value 37 is generated using quality metric checking module 38, which is also used to rank the available remote expert REs. Quality metric value 37 serves as a score for ranking the remote expert REs. Decision module 38 then ranks the available remote expert REs based on quality metric 37 and the results of model 35 generated by ML module 34.

[0052] At operation 108, one or more of the available remote expert REs are matched with the local operator LO performing the medical imaging examination. This is performed using the allocation module 40. The best remote expert RE is used for the medical imaging examination (e.g., to select the accurate imaging sequence and successfully obtain high-quality images), and the best remote expert is matched with the local operator LO.

[0053] At operation 110, a ranked list 44 of available remote experts (REs) is displayed via GUI 28 on display device 24 of remote workstation 12 and / or via GUI 28' on display device 24 of medical imaging equipment controller 10. (Return to Reference) Figure 2 List 44 may include quality metric 37 as a score used to rank available remote expert REs. Quality metric 37 may be displayed alongside the corresponding remote expert RE, and remote experts may be ranked based on the highest quality metric. List 44 may also include "coming soon" remote expert REs based on scheduling availability data.

[0054] In operation 110, the local operator (LO) selects one of the remote experts (REs) listed in list 44. To do this, the local operator (LO) uses at least one user input device 22' and selects one of the listed remote expert REs. In some examples, the local operator (LO) can select a remote expert RE to open a drop-down menu 46 to display a set of experience metrics 48 for the selected remote expert. The local operator (LO) can also select a communication button 54, which can be selected by either the remote expert RE or the local operator (LO), to establish a natural language communication path 19 via a communication link 14 between the two parties, allowing the screens of the medical imaging device controller 10 and the remote workstation 12 to be shared. In some examples, the selected remote expert RE can reject the natural language communication path 19 (e.g., if the remote expert RE is busy, or feels that the problem to be addressed in the medical imaging examination is not within his or her skills). In this case, a communication link can be established between another remote expert among the listed remote expert REs (e.g., the next highest-ranked remote expert). The process can continue until one of the remote experts re-accepts the natural language communication path 19.

[0055] At optional operation 112, database 41 can be updated based on the interaction between the selected remote expert (RE) and the local operator (LO). In one example, if the selected remote expert (RE) refuses to assist the local operator (LO), database 41 can be updated to no longer recommend the remote expert for those types of problems, for that particular local operator (LO), etc. Additionally, data related to the effectiveness of the assistance provided by the selected remote expert (RE) and the local operator (LO) can be collected. This can be done via feedback forms completed by the local operator (LO) and / or the remote expert (RE). In one example, one or more quality metrics 37 for the selected remote expert (RE) can be recalculated and stored in database 41 (along with updates related to adding the remote expert's experience through handling matters with the local operator (LO)). In another example, an adaptive learning process is performed on the collected data to maximize the quality metrics 37 related to the effectiveness of the assistance, which can also be stored in database 41.

[0056] This disclosure has been described with reference to preferred embodiments. Modifications and variations may be made by others as they read and understand the foregoing detailed description. It is intended that the exemplary embodiments be construed as including all such modifications and variations, provided they fall within the scope of the appended claims or their equivalents.

Claims

1. A non-transient computer-readable medium (26s) storing instructions executable by at least one electronic processor (14s) to perform a method of connecting a local medical imaging device operator (LO) with a remote medical imaging specialist (RE) during an imaging examination performed using a medical imaging device (2), the method comprising: Identify the characteristics of available remote medical imaging experts who can assist the operator of the local medical imaging equipment; Based on the determined characteristics of the telemedicine imaging experts, one or more of the available telemedicine imaging experts are matched with the characteristics of the imaging examination. and A user interface is provided to at least one display device operable by both the local medical imaging device operator and the remote medical imaging expert. This user interface displays a list (44) of matching available remote medical imaging experts, and the local medical imaging device operator is able to select a matching available medical imaging expert from the displayed list via the user interface. The characteristics of the remote medical imaging experts (REs) identified as being suitable for assisting the local medical imaging equipment operator (LO) include: A feature selection process is performed on the characteristics of the remote medical imaging expert and the characteristics of the imaging examination.

2. The non-transient computer-readable medium (26s) according to claim 1, wherein, The method further includes: The available remote medical imaging experts (REs) are ranked based on their scores for the characteristics of the available remote medical imaging experts and the characteristics of the imaging examination.

3. The non-transient computer-readable medium (26s) according to claim 2, wherein, The user interface that provides the list (44) of available remote medical imaging specialists (REs) for the matching includes: The list is provided as a ranked list of available remote medical imaging experts based on the scores.

4. The non-transient computer-readable medium (26s) according to claim 2, wherein, The user interface that provides the list (44) of available remote medical imaging specialists (REs) for the matching includes: The list of available remote medical imaging experts, including the highest-ranked ones, is provided based on the scores.

5. The non-transient computer-readable medium (26s) according to any one of claims 3 and 4, wherein, The user interface that provides the list (44) of available remote medical imaging specialists (REs) for the matching includes: The scores of the available remote medical imaging experts are listed on the user interface.

6. The non-transient computer-readable medium (26s) according to claim 3 or 4, wherein, Determining the characteristics of available remote medical imaging experts (REs) includes determining the scheduling availability of the remote medical imaging experts; and providing the user interface for displaying the list (44) includes providing the list with the scheduling availability of the remote medical imaging experts.

7. The non-transient computer-readable medium (26s) according to any one of claims 2-4, wherein, Ranking the available remote medical imaging experts (REs) includes: Machine learning (ML) operations are applied to the characteristics of the available telemedicine imaging experts to rank them.

8. The non-transient computer-readable medium (26s) according to any one of claims 1-4, wherein, The feature selection process is a principal component analysis (PCA) process.

9. The non-transient computer-readable medium (26s) according to any one of claims 1-4, wherein, The features of the available remote medical imaging expert (RE) include at least one of the following: Regarding the empirical level of the imaging modality of the imaging examination; The level of experience regarding the anatomical structures of the patient to be imaged during the imaging examination; The level of experience working with the local medical imaging equipment operator (LO); as well as Experience level regarding the types of problems that occur during the imaging examination.

10. The non-transient computer-readable medium (26s) according to any one of claims 1-4, wherein, The user interface that provides a list (44) of available remote medical imaging specialists (REs) for the matching includes: When a user input indicating the selection of one of the listed telemedicine imaging experts is received via at least one user input device, a drop-down menu is provided to display information about the selected telemedicine imaging expert.

11. The non-transient computer-readable medium (26s) according to any one of claims 1-4, wherein, The user interface that provides a list (44) of available remote medical imaging specialists (REs) for the matching includes: Upon receiving user input via at least one user input device indicating the selection of one of the listed remote medical imaging experts, a two-way telephone or video communication link is established between the selected remote medical imaging expert and the local medical imaging equipment operator (LO), and the display of the medical imaging equipment (2) is shared at the workstation (12) of the selected remote medical imaging expert.

12. The non-transient computer-readable medium (26s) according to claim 11, wherein, The user interface that provides a list (44) of available remote medical imaging specialists (REs) for the matching includes: Upon receiving an indication of rejection of the communication link from the selected telemedicine imaging expert, a communication link is established between another telemedicine imaging expert among the listed telemedicine imaging experts and the local medical imaging equipment operator (LO).

13. The non-transient computer-readable medium (26s) according to any one of claims 1-4, wherein, The characteristics of the remote medical imaging expert (RE) are stored in a database (41), and the method further includes: Collect data related to the effectiveness of assistance provided by the selected remote medical imaging specialist and the local medical imaging equipment operator (LO); Calculate one or more quality metrics related to the collected data; and The information for the selected remote medical imaging expert stored in the database is updated using one or more calculated quality metrics.

14. The non-transient computer-readable medium (26s) according to claim 13, wherein, The method further includes: Collect data relating to the effectiveness of assistance provided by the selected remote medical imaging specialist (RE) and the local medical imaging equipment operator (LO); An adaptive learning process is performed on the collected data to maximize the quality metric related to the effectiveness of the aid; and The information for the selected telemedicine imaging expert stored in the database (41) is updated using the maximized quality metric.

15. An apparatus for connecting a local medical imaging equipment operator (LO) during an imaging examination performed using a medical imaging equipment (2), the apparatus comprising: A screen sharing device (13) for sharing the screen of the controller of the medical imaging device; A telephone or video communication link, which is operationally connected to an electronic network to provide telephone or video communication with a group of telemedicine imaging experts (REs). Database (41) that stores defined characteristics of the telemedicine imaging experts in the group of telemedicine imaging experts, the characteristics including at least one of the following: experience level with respect to the modality of the imaging examination; Experience level regarding the anatomical structures of the patient to be imaged during the imaging examination; experience level working with the operator of the local medical imaging equipment; And the level of experience regarding the types of problems that occur during the imaging examination; as well as At least one electronic processor (14s) is programmed to: A feature selection process is performed on the characteristics of the remote medical imaging expert and the characteristics of the imaging examination to retrieve the characteristics of one or more remote medical imaging experts from the database, the one or more remote medical imaging experts being used to assist the local medical imaging equipment operator in the imaging examination. The available remote medical imaging experts are ranked based on matching the characteristics of the available remote medical imaging experts with the characteristics of the imaging examination. and A user interface is provided that displays a ranked list of available remote medical imaging experts (44), enabling the local medical imaging equipment operator to select one of the listed available remote medical imaging experts and establish an auxiliary session between the local medical imaging equipment operator and the selected remote medical imaging expert via the screen sharing device and the telephone or video communication link.

16. The apparatus according to claim 15, wherein, The at least one electronic processor (14s) is also programmed to: Upon receiving user input via at least one user input device indicating the selection of one of the listed remote medical imaging experts (REs), a communication link is established between the selected remote medical imaging expert and the local medical imaging equipment operator (LO).

17. The apparatus according to any one of claims 15 and 16, wherein, The at least one electronic processor (14s) is also programmed to: Machine learning (ML) operations are applied to the characteristics of the available remote medical imaging experts (REs) to rank the available remote medical imaging experts.

18. The apparatus according to claim 15 or 16, wherein, The at least one electronic processor (14s) is also programmed to: When a user input indicating the selection of one of the listed telemedicine imaging experts is received via at least one user input device, a drop-down menu is provided to display information about the selected telemedicine imaging expert.

19. A method of connecting a remote medical imaging expert (RE) to a local medical imaging equipment operator (LO) to provide assistance during an imaging examination, the method comprising: A feature selection process is performed on the characteristics of the remote medical imaging expert and the characteristics of the imaging examination to retrieve from the database (41) one or more remote medical imaging experts whose characteristics can be used to assist the operator of the local medical imaging device in the imaging examination. The available remote medical imaging experts are ranked using the scores of the characteristics indicated by the characteristics of the imaging examination. Provides a user interface that displays a ranked list of available remote medical imaging specialists (44); Receive user input via at least one user input device indicating the selection of one of the listed telemedicine imaging experts; Collect data related to the effectiveness of assistance provided by the selected remote medical imaging expert and the local medical imaging equipment operator; Determine one or more quality metrics related to the collected data; and The information for the selected remote medical imaging expert stored in the database is updated using one or more calculated quality metrics.

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