Medical Device Inspection System

Abstract

The present disclosure relates to medical device inspection. A method for inspecting a medical device is disclosed. In some embodiments, the medical device inspection is performed by a medical device inspection system described herein. One aspect is a method for inspecting a medical device, the method including identifying the medical device, inspecting the medical device with an inspection scope, storing inspection data, analyzing the inspection data, generating analytical data based on the analysis of the inspection data, and generating one or more outputs based on the analytical data.

Description

[Technical Field]

[0001] (Priority Claim) This application was filed as a PCT international patent application on October 25, 2023, and claims the benefit of and priority to U.S. Patent Application No. 63 / 380,765, filed October 25, 2022, and U.S. Patent Application No. 63 / 500,570, filed May 5, 2023, the disclosures of which are incorporated herein by reference in their entireties. [Background technology]

[0002] Millions of medical devices are used daily in hospitals around the world. With continued advancements in medical and surgical procedures over the years, one long-standing trend has been toward minimally invasive procedures performed through smaller incisions or through natural orifices in the body. Examples of this trend include arthroscopic surgery, transcatheter aortic valve replacement ("TAVR"), natural orifice transluminal endoscopic surgery ("NOTES"), robotic surgery, and the like. Many of these procedures involve the use of long, flexible catheter instruments, long, thin, rigid instruments with lumens, and / or long, flexible endoscopes to visualize the procedure. Additionally, endoscopes are used in a myriad of different diagnostic and therapeutic procedures in many parts of the body.

[0003] One of the challenges with using endoscopes, fiberscopes, catheter-based medical instruments (including surgical instruments), and other long, thin, reusable instruments is how to properly and effectively clean them, especially their internal lumens. Many endoscopes and other instruments are too expensive to be disposable and must be reused. Long, small-diameter, flexible instruments can be extremely difficult to clean internally and difficult to inspect internally. Flexible instruments can not only collect bacteria and other contaminants, but can also crack or otherwise become permanently deformed during use, for example, when the instrument is bent or twisted. These instruments are typically processed in cleaning facilities located within hospitals by workers with little training. To inspect the interior of such instruments, a small, flexible scope is inserted and advanced through the instrument's lumen, sometimes revealing contaminants and damage. However, it can be difficult for the inspector to effectively identify contaminants and internal damage to the device. Therefore, the inspection process can be labor-intensive and sometimes ineffective. It can also be difficult to find a scope small enough to fit through the lumen of some medical devices while still allowing adequate visualization. Additionally, once contamination of an endoscope or catheter lumen (or similar internal portion of a medical device) is identified, it can often be difficult to properly clean and / or decontaminate the lumen. Summary of the Invention [Problem to be solved by the invention]

[0004] In general, the present disclosure is directed to the testing of medical devices. In some embodiments, by way of non-limiting example, the testing of medical devices is performed by a medical device testing system as described herein. [Means for solving the problem]

[0005] One aspect is a method of inspecting a medical device, the method including identifying the medical device, inspecting the medical device with an inspection scope to generate inspection data, analyzing the inspection data using a machine learning model, generating analysis data based on the analysis of the inspection data, and generating one or more outputs based on the analysis data.

[0006] Another aspect is a medical device inspection system comprising an inspection scope including a camera that performs an inspection of the medical device to capture inspection data, and a computing device that includes an inspection analyzer that analyzes the inspection data to identify possible anomalies in the medical device.

[0007] A further aspect is a computer system comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the computer system to receive inspection data documenting an inspection of a medical device having an inspection scope, and process the inspection data to automatically determine one or more conditions of the medical device.

[0008] Yet another aspect is a method for inspecting a medical device, the method including identifying the medical device, retrieving medical device data for the identified medical device, inspecting the medical device using an inspection scope to generate inspection data, analyzing the inspection data, generating analysis data based on the analysis of the inspection data, and generating one or more outputs based on the analysis data.

[0009] Another aspect is a method of inspecting a medical device, the method including positioning an inspection scope relative to the medical device, collecting inspection data including at least one image taken by the inspection scope of the medical device, and generating a user interface including a graphical representation of at least a portion of the medical device and a location indicator representing a corresponding location on the medical device where the image was taken.

[0010] A further aspect is a method of generating a user interface, the method including: using a computing device to acquire inspection data related to inspection of a medical device with an inspection scope, the inspection data including at least one image of an interior of the medical device and a corresponding location where the at least one image was taken; and generating a user interface related to inspection of the medical device, the user interface including a graphical representation of at least a portion of the medical device and a location indicator representing the corresponding location of the medical device where the at least one image was taken.

[0011] Yet another aspect is a computing device comprising at least one processing unit and at least one computer-readable storage device storing data of instructions that, when executed by the at least one processing unit, cause the computing device to: acquire inspection data related to an inspection of a medical device with an inspection scope, the inspection data including at least one image of an interior of the medical device and a corresponding location where the at least one image was taken; and generate a user interface related to the inspection of the medical device, the user interface including a graphical representation of at least a portion of the medical device and a location indicator representing the corresponding location of the medical device where the at least one image was captured.

[0012] Another aspect is a computer-readable storage device that stores instruction data that, when executed by at least one processing unit of at least one computing device, causes the at least one computing device to obtain inspection data related to inspection of a medical device with an inspection scope, the inspection data including at least one image of the interior of the medical device and a corresponding location where the at least one image was taken, and generates a user interface related to the inspection of the medical device, the user interface including a graphical representation of at least a portion of the medical device and a location indicator representing the corresponding location of the medical device where the at least one image was taken. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 illustrates an exemplary medical device testing network according to some embodiments of the present disclosure. [Figure 2] FIG. 2 is a flowchart illustrating an exemplary method for testing a medical device, according to some embodiments of the present disclosure. [Figure 3] FIG. 3 is a schematic block diagram illustrating an exemplary medical device inspection system, according to some embodiments of the present disclosure. [Figure 4] FIG. 4 illustrates an exemplary computing device according to some embodiments of the present disclosure. [Figure 5] FIG. 5 illustrates an example user interface of a medical device testing network, according to some embodiments of the present disclosure. [Figure 6] FIG. 6 illustrates an exemplary user interface as may be displayed during a medical device test. [Figure 7] FIG. 7 is another example of the user interface shown in FIG. 6, illustrating the display of medical device test data after the test has been completed. DETAILED DESCRIPTION OF THE INVENTION

[0014] Various aspects will now be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various aspects does not limit the scope of the claims appended hereto. Additionally, any examples described herein are not intended to be limiting, but merely to describe some of the many possible embodiments for the appended claims.

[0015] FIG. 1 illustrates an exemplary medical device inspection system 10. In this example, medical device inspection system 10 includes computing device 14 (including computing devices 14A-14D), medical device inspection coordinator 16 (including 16A-D), and asset database 18 (including 18A-D). The example of FIG. 1 also illustrates multiple computing environments, including server computing environment 12, inspection station 22, health management system 24, and another (“other”) system 26. A data communications network 20 is also shown. Exemplary inspection station 22 includes inspection scope 30. A medical device M is also shown within inspection station 22.

[0016] The exemplary medical device inspection system 10 operates to perform inspection of a medical device M. A variety of different types of medical devices can be inspected. Some medical devices have an elongated, flexible body and may include one or more internal orifices. Examples include endoscopes, fiberscopes, catheter-based medical instruments (including surgical instruments), and other reusable instruments.

[0017] In some embodiments, the inspection is performed by the medical device inspection system 10 using an inspection scope 30, which is shown in more detail with reference to FIG. 3 . An example of an inspection scope 30 is a borescope. The inspection data is generated using the inspection scope. In some embodiments, the inspection data includes image data, which includes at least one image. An example of an image is a photograph. The inspection scope can capture the image. The image can be a still image, or can include or be generated from video, such as a video feed. Thus, the terms “image” and image data refer to a still image, or a video image, or both. In some embodiments, the video is composed of multiple frames, and each frame is an image. In some embodiments, multiple video frames are used to generate the image.

[0018] In some embodiments, the medical device inspection system 10 may capture video data of a complete inspection of the medical device M and capture images (or identify frames of video) at specific landmarks on the medical device M. Landmarks are points of interest within the medical device. An example of a landmark is a hotspot on the medical device. A hotspot is a point, area, or region on the medical device that is known to be prone to abnormalities. The medical device inspection system 10 operates to check the medical device M for abnormalities, which may include, for example, debris, damage (e.g., gouges, kinks, cracks), discoloration, moisture (e.g., water droplets), contaminants (e.g., biofilm or biological material), etc.

[0019] Medical device inspection system 10 can be implemented in a variety of possible architectures. FIG. 1 illustrates one exemplary architecture including several different computing environments that interact to collectively perform medical device inspections. Various other implementations are possible. For example, in another embodiment, medical device inspection system 10 is implemented solely as inspection station 22. In another possible embodiment, medical device inspection system 10 is implemented with server computing environment 12 and inspection station 22 working together. In another possible embodiment, server computing environment 12 and / or inspection station 22 may further interact with health management system 24 and / or other systems 26. In yet another example, medical device inspection system 10 may be implemented entirely within health management system 24 or other systems 26. In some embodiments, inspection station 22 is part of health management system 24 or other systems 26. Similarly, in some embodiments, server computing environment 12 may be part of health management system 24 or other systems 26. In still other embodiments, certain portions or aspects of the processes of medical device inspection system 10 may be distributed or otherwise divided and performed by different portions of medical device inspection system 10. As one simple example, physical inspection of medical device M with inspection scope 30 may be performed at inspection station 22, while subsequent data processing, storage, and analysis steps may be performed by server computing environment 12. Other embodiments distribute or deploy components or functionality of medical device inspection system 10 across various systems in other combinations or configurations.

[0020] In the depicted example, medical device inspection system 10 includes a server computing environment 12. The exemplary server computing environment 12 includes at least one computing device 14A (e.g., a server computing device), a medical device inspection coordinator 16A, and an asset database 18A.

[0021] The various environments of the medical device testing system 10 (including the server computing environment 12, the testing stations 22, the health management system 24, and other systems 26) can communicate with each other over a network 20. The network can include one or more data communication networks, such as one or more local area networks and the Internet. Communication can occur via wired or wireless data communication technologies.

[0022] Server computing environment 12 includes at least one computing device 14A (e.g., a server computing device). Computing device 14A is configured to operate (or interact with) medical device inspection coordinator 16A and / or asset database 18A. In some embodiments, computing device 14A is configured as shown and described in further detail with reference to FIG. 4.

[0023] Additionally, in some embodiments, computing device 14 includes one or more artificial intelligence (“AI”) accelerators, such as one or more machine learning (ML) accelerators. An example of an AI accelerator or ML accelerator is a graphics processing unit (GPU). In some embodiments, computing device 14A includes one or more GPUs. In some embodiments, the one or more GPUs are optimized for deep learning. An example of a suitable GPU is a Compute United Device Architecture (CUDA)-enabled (CUDA-enabled) GPU, available from NVIDIA of Santa Clara, CA. CUDA-enabled GPUs include a parallel computing platform and application programming interface (API) model. CUDA-enabled GPUs include hundreds to thousands of smaller cores that enable multitasking, making them well-suited for the image processing, deep learning, and AI tasks described herein. GPUs can be used to significantly reduce the time required to perform the deep learning and AI processing described herein due to the highly parallel nature of neural network computations. The CUDA-enabled GPU can run neural networks (or other machine learning networks), which in some embodiments can be used to process inspection data (including inspection images) from inspection station 22 (or health management system 24 or other system 26) to determine whether an abnormality may exist in medical device M.

[0024] While FIG. 1 shows only one computing device 14A, some embodiments include multiple computing devices. In these embodiments, each of the multiple computing devices may be identical or similar and provide similar functionality (e.g., to provide greater capacity and redundancy or to provide service from multiple geographic locations). Alternatively, in these embodiments, some of the computing devices may provide dedicated services (e.g., image processing services). Various combinations of these are also possible. Furthermore, in some embodiments, some of the components disclosed herein may be hosted and executed on external systems (e.g., including third-party systems or internal systems belonging to different groups / systems of a healthcare enterprise). In some embodiments, computing device 14A and / or asset database 18A may be or include cloud services, including cloud processing and data storage. Example embodiments of these and other solutions disclosed herein may also be cloud-based and / or internet-based.

[0025] Although this example shows a server computing environment 12, other embodiments may operate locally on one or more computing devices as described herein without involving a server computing environment 12.

[0026] Medical device inspection system 10 includes medical device inspection coordinator 16. Medical device inspection coordinator 16 may be a single unit (e.g., 16A, 16B, 16C, or 16D) or multiple units (e.g., any combination of 16A, 16B, 16C, and 16D). Furthermore, the various medical device inspection coordinators 16A, 16B, 16C, and 16D may be the same or different. In certain configurations, the various medical device inspection coordinators perform certain of the operations of the overall medical device inspection coordinator 16, such as by dividing or distributing the processing (operations) among various computing environments. The reference numeral 16 is used herein to refer to any of various possible implementations of a medical device inspection coordinator, whether an individual unit or multiple units.

[0027] The exemplary medical device inspection coordinator 16 operates to perform some of the processes of the medical device inspection system, some of which are described in further detail with reference to FIG.

[0028] The exemplary server computing environment 12 shown in FIG. 1 includes a medical device inspection coordinator 16A, which may run on server computing device 14A or a separate computing device. In some embodiments, medical device inspection coordinator 16A controls and administers the medical device inspection process. In some embodiments, medical device inspection coordinator 16 automatically executes the medical device inspection. In other embodiments, medical device inspection coordinator 16 provides guidance or information to assist an operator in conducting the medical device inspection. In some embodiments, medical device inspection coordinator 16 operates a model that identifies possible anomalies in medical device M by processing video and / or image data of medical device M captured by inspection scope 30. In some embodiments, artificial intelligence techniques are used by medical device inspection coordinator 16. In some embodiments, the model is trained using medical device inspection data (e.g., image data from medical device inspections and / or other inspection data).

[0029] In some embodiments, the medical device inspection coordinator 16 executes in the server computing environment 12 in conjunction with the medical device inspection process. The inspection process may be performed in an inspection station 22 to inspect the medical device M. For example, the inspection process may inspect the medical device M for abnormalities. Anomalies may include, for example, debris, damage (e.g., gouges, kinks, cracks), discoloration, moisture (e.g., water droplets), contaminants (e.g., biofilm or biological material), etc. If any abnormalities are identified, further inspection or processing of the medical device may be warranted, and in some embodiments, such additional inspection or processing may be recommended or directed by the medical device inspection coordinator 16. Additional processing may include, for example, repeating a decontamination process or obtaining a replacement part. In other embodiments, many other additional processing steps are possible. An example of an inspection station 22 is illustrated and described in further detail with reference to FIG. 3.

[0030] In some embodiments, medical device inspection coordinator 16 provides a user interface that includes images and data from medical device inspection system 10. Examples of possible user interfaces are shown and described in further detail with reference to Figures 5-7.

[0031] Some embodiments include an asset database 18. In some embodiments, asset database 18 stores data regarding inspection data, such as images, detected anomalies, operator annotations, device ID, location information, and time information. In some embodiments, images are stored with associated information stored as metadata. In some embodiments, the data is stored in a relational database. The database can be hosted off-site, on-site, or in the cloud (connected via the internet). In some embodiments, the asset database stores information that is manually entered (e.g., by medical device inspection coordinator 16) and / or automatically determined. As described herein, asset database 18 may be a single database or a collection of multiple databases, including any combination of asset databases 18A, 18B, 18C, or 18D shown in FIG. 1, or other databases, including cloud databases or cloud services, etc.

[0032] Some embodiments include a health management system 24. In some embodiments, the health management system 24 is a computer system of a healthcare enterprise (e.g., a provider, clinic, hospital, etc.). In some embodiments, healthcare personnel access the server computing environment 12 to determine the status of medical equipment assigned for various procedures. In some embodiments, the health management system 24 can provide information on the current status of medical equipment and can integrate the server computing environment 12 into various healthcare workflows. In some embodiments, the health management system 24 includes a database (e.g., asset database 18C) containing information about the availability and / or status of various medical equipment used by the healthcare enterprise, and may include historical or reference images and / or data. In some embodiments, various computing devices 14 can interact with the health management system 24 via an API. Similarly, in some embodiments, the health management system 24 can interact with the server computing environment, the examination stations 22, and / or other systems 26 via an API.

[0033] Some embodiments of the medical device inspection system 10 include other systems 26. The other systems 26 may include one or more other systems from other parties, such as other departments within a company or a third party. Examples of other systems 26 that may interact with the server computing environment 12, the inspection station 22, the health management system 24, or even other systems 26 include one or more database systems, a manufacturer system, a third-party repair system, a Food and Drug Administration (FDA) system, a global unique device identification database, a manager system, an external hospital system, an electronic medical record system, a leak testing system, a dryer or other testing or sensing system, a third-party repair service (e.g., to allow a third party to check in on the status of a customer's medical device), or a third-party rental service. For example, a manufacturer system may include a computer system associated with a manufacturer of a medical device. While the illustrated example includes only one other system 26, some embodiments include multiple parties, each with their own system for interacting with the server computing environment 12. In some embodiments, the other systems 26 can add data to the asset database 18. For example, manufacturers of various medical devices sold by the manufacturer can add data to the asset database. For example, a manufacturer may add entries individually or in bulk for medical devices sold to the health care system, including adding a unique identifier for each device, information about known hotspots, product specifications, etc. In some embodiments, the information provided by the manufacturer is further used to train a machine learning model for detecting conditions of the medical device M. In some embodiments, the manufacturer system includes a database containing data about medical devices sold by the manufacturer.In some of these embodiments, the server computing environment 12 (including the computing device 14A), or either the inspection station 22, the health management system 24, or yet another system 26, may access the asset database 18D of the other system 26 via an application programming interface (API) to access data regarding medical devices and / or other features provided by the other system 26. Some additional non-limiting examples of the other system 26 include a leak testing system, a dryer, or other testing or sensing system.

[0034] In some embodiments, individual medical devices are automatically cataloged and added to the asset database 18 when they are inspected / scanned at the inspection station 22 or when new medical devices are initially deployed within the health management system 24 or medical device inspection system 10.

[0035] In the illustrated example, each of the inspection stations 22, health management systems 24, and other systems 26 may optionally include all or components of the medical device inspection coordinator 16 (i.e., 16A, 16B, 16C, and 16D) and / or asset databases 18 (18A, 18B, 18C, and 18D). In some configurations, the medical device inspection coordinator may be entirely hosted in one or more of the inspection stations 22, health management systems 24, and other systems 26. In some embodiments, these different combinations may interact with each other to provide the functionality discussed herein. In some embodiments, asset data may be stored across one or more of the asset databases 18 of the inspection stations 22, health management systems 24, and other systems 26, and the server computing environment 12 may interact with one or more of the asset databases 18A, 18B, 18C, or 18D to compile the necessary information. In some embodiments, each of the inspection stations 22, health management systems 24, and other systems 26 has limited access to the asset database 18A, for example, limited to retrieving data that the respective station 22, system 24, or system 26 has permission to access. Many other configurations are possible and within the scope of this disclosure.

[0036] Asset databases 18A, 18B, 18C, and 18D may contain data not included in other asset databases. In some embodiments, asset databases 18A, 18B, 18C, and 18D may share common data, or may be completely unique and separate data, or a combination of both. Each system (e.g., inspection station 22, health management system 24, and other systems 26) may have its own database that it accesses, and such database may be separate from any other database. In other embodiments, system databases 18 (including 18A, 18B, 18C, and 18D) may be linked so as to share data with one or more of the other asset databases 18 (including 18A, 18B, 18C, and 18D).

[0037] In some embodiments, medical device inspection coordinator 16 and / or medical device inspection station 22 may be integrated into one or more of health management systems 24 or other systems 26. Other combinations and integrations are possible to form still other embodiments and possible implementations within the scope of the present disclosure.

[0038] 2 is a flow chart illustrating an exemplary method 100 for inspecting a medical device M. This example of method 100 includes steps 102, 104, 106, 108, 110, 112, 114, and 116. In some embodiments, method 100 is performed by medical device inspection system 10 shown in FIG. 1. In some embodiments, method 100 is performed by medical device inspection coordinator 16 shown in FIG. 1.

[0039] Method 100 operates to perform an inspection of a medical device M. The medical device may be one of a variety of possible types of medical devices. One example of a medical device M is shown and described in further detail with reference to FIGS. 1 and 3. Some medical devices have an elongated, flexible body and may include one or more internal orifices. Examples include endoscopes, fiberscopes, catheter-based medical / surgical instruments, and other reusable instruments.

[0040] Step 102 is performed to identify the medical device to be inspected. In some embodiments, step 102 involves prompting a user to provide identifying information. Examples of identifying information include a manufacturer name and model number. Another example is a serial number (alone or in combination with the manufacturer's name and serial number). Other identifiers, such as an asset number, lot number, or various other possible identifiers, may also be used. In some embodiments, the medical device M is manually identified by an operator. For example, by the operator manually inspecting the medical device and entering the identifying information into the computing device 14. In other embodiments, the medical device may be identified by scanning a barcode with a barcode scanner or camera, capturing a photograph of part or all of the medical device (inside, outside, or both), performing a catalog search via a user interface, etc. One example of a catalog search is a manufacturer search, in which the manufacturer of the medical device M is first entered or selected from a list. A database query can then be performed to provide a list of medical devices or types of medical devices. The operator can then navigate the available options and select a particular medical device. Other types of search queries can be performed in a similar manner to search for and identify medical devices M.

[0041] In some embodiments, the medical device M is automatically identified. For example, visual identification, such as image recognition, may be used to automatically recognize and detect the medical device M based on the inspection data received in step 106 or by other image data. In some embodiments, the visual identification may utilize machine learning algorithms. Other examples of automatic identification include automatically scanning a computer-readable code (i.e., using a camera or other computer-readable code scanner), sensing a radio frequency identification (RFID) tag (i.e., using an RFID tag reader), etc.

[0042] In some embodiments, step 102 is performed by scanning the identifier, such as using an identification device, such as a camera (e.g., an inspection scope), a barcode scanner, an RFID reader, or the like. In such examples, the identifier may be presented on the medical device in text form or may be encoded in a machine-readable format, such as a barcode or QR code. The scanner may be a handheld scanner that may be operated by a user. In some embodiments, a fixed-position scanner may also be used, which is integrated into the inspection system or mounted on a support structure. The fixed-position scanner may be configured to view the medical device during a portion of the inspection process, such that the medical device identifier can be automatically scanned without requiring additional steps or user interaction.

[0043] In another possible example, medical equipment can be identified using or in conjunction with an asset tracking system. For example, the asset tracking system can indicate which medical equipment is present in a particular room, and the operator can select the medical equipment from a list of these medical equipment.

[0044] In some embodiments, the testing system's computer system uses APIs to communicate with various systems (hospital systems, equipment tracking systems, repair contracts, managers, etc.).

[0045] Step 104 is performed to retrieve medical device data including information about the medical device M. In some embodiments, once the medical device is identified, medical device data about the medical device M can be retrieved from a medical device database, such as asset database 18. The medical device data may include, for example, inspection data, reference data, and historical data (including previous analysis data). The medical device data may include product characteristics, inspection or cleaning instructions, inspection or cleaning protocols (e.g., Instructions for Use (IFU)), historical data (including historical data from prior processing by the inspection system, including images from prior inspections, video status, location, repair history, device usage history, cleaning history, and test data associated with the device), reference images (e.g., images of the entire medical device, external images, sample images showing what the medical device (or portion thereof) should look like under normal processing conditions (i.e., no anomalies), or sample images showing possible anomalies), identification of landmarks for the medical device M, etc. The landmarks may include hot spots, such as locations where anomalies are more likely to exist. For example, hot spots may include points prone to wear or breakage, or points prone to the accumulation of debris or other material.

[0046] The medical device data may include inspection support information, which is information that may be presented to a user or used by a medical device inspection coordinator to assist and guide the inspection of the medical device M. In some embodiments, the inspection support information includes any one or more of: (a) one or more historical images of the medical device, (b) one or more historical analysis data from previous inspections, (c) one or more landmarks of the medical device, (d) at least some instructions for use (IFU) for the medical device, (e) one or more reference images, and (f) any combination of (a) through (e).

[0047] The medical device data retrieved in step 104 may be used by medical device inspection system 10 in other steps, including at least steps 102, 106, 110, 112, and 116.

[0048] In some embodiments, at least some of the medical device data is presented on a user interface. Examples of user interfaces are shown in FIGS. 3 and 5-7. In some embodiments, the user interface includes at least one of: (1) areas of the device to be inspected; (2) known hotspots for the type of device; (3) device history; (4) reference images; (5) historical test data associated with the device (e.g., images, repair history); and / or (6) instructions for use (IFU) for using the device. In some embodiments, the information includes a tutorial on how to inspect the medical device. For example, the tutorial may instruct the user on where to inspect for hotspots and issues typically found in hotspots (e.g., by showing example images). In some embodiments, the tutorial may include resources such as instructional videos, tests, discoveries, etc. Further examples of user interfaces are described in more detail with reference to FIGS. 3 and 3-7.

[0049] Step 106 is performed to inspect (e.g., scan) the medical device using an inspection scope (e.g., inspection scope 30 shown and described with reference to FIGS. 1 and 3). Examples of inspection scopes are disclosed in various patent applications by Clarus Medical, LLC, including U.S. Patent Application Publication No. 2019 / 0224357, filed January 22, 2019, U.S. Patent Application Publication No. 2019 / 0282327, filed February 19, 2019, U.S. Patent Application Publication No. 2022 / 0080469, filed September 10, 2021, and U.S. Patent Application Publication No. 2022 / 0240767, filed February 3, 2022, the disclosures of which are incorporated herein by reference in their entireties.

[0050] One example of an inspection scope is a borescope. Inspection scopes often include an elongated body, such as in the form of a long, thin tube. The elongated body may include one or more optical fibers, one or more electrical wires, one or more stiffeners, one or more digital cameras, one or more light sources, or other elements. The optical fibers can be used, for example, to carry light from one or more light sources to the tip of the inspection scope, to carry light from the light sources to emit light from the side of the elongated body (radially), and / or to return light from the tip to the digital camera. In some embodiments, the optical fibers are arranged in an optical fiber bundle. The light source may include other light sources, such as a visible light source and / or an ultraviolet (UV) light source (capable of emitting UV light, such as UV-C). In some embodiments, the digital camera is located at or near the tip of the inspection scope. In another possible configuration, the digital camera is located at the proximal end of the inspection scope (opposite the distal tip), such as inside a handle or other housing. A fiber (such as a fiber bundle) can transmit light from the distal tip to a digital camera. Other configurations are possible.

[0051] A digital camera includes one or more optical sensors that detect light and generate an electrical signal, e.g., ultimately generating a digital image or digital video. Some examples of digital cameras include charge-coupled devices (CCDs) and complementary metal-oxide semiconductors (CMOSs). There are many different variations of these types of digital cameras, which may also be used in other embodiments. A digital camera includes one or more optical sensors, also called image sensors.

[0052] Some embodiments include a fiber optic camera having a bundle of optical fibers that transmit images from a distal end (such as via a lens located at the distal end) to an eyepiece and / or other proximal end where a camera is attached.

[0053] Electrical wires may also be disposed within the elongate body in some embodiments, such as to deliver power to one or more electronic elements, such as a digital camera or a light source, and / or to carry electrical signals to and from such elements and back to the electronics at the proximal end.

[0054] During step 106, the camera is operated to capture an image of the medical device M, which may be an individual image or a video. A video may be made up of multiple images.

[0055] The inspection scope 30 moves relative to the medical device M to capture images of the medical device M at different positions, for example, along the entire length or a portion of the length of the medical device M, or along a particular region of the medical device. In some embodiments, the inspection scope 30 moves while the medical device M remains stationary. In other embodiments, the medical device M moves while the inspection scope 30 remains stationary.

[0056] Some embodiments include a mechanical advancement system (e.g., 134 shown in FIG. 3 ). In some embodiments, the mechanical advancement system is motorized. The advancement system moves one or both of the inspection scope 30 or the medical device M. For example, the advancement system can move the inspection scope 30 along the inside of a stationary medical device M. In another embodiment, the advancement system can move the medical device M while the inspection scope 30 remains stationary. Examples of mechanical advancement systems include feeders, robotic arms, and other automated systems. In some embodiments, the mechanical advancement system uses gravity and friction to advance the inspection scope 30 through the medical device M, or vice versa. Examples of mechanical advancement systems are shown and described in further detail in applicant's co-pending application, U.S. Patent Application Publication No. 2019 / 0224357, filed January 22, 2019.

[0057] Some embodiments do not have a mechanical advancement system. For example, an operator can advance the inspection scope 30 through the medical device M by manually moving either the inspection scope or the medical device relative to the other. In some embodiments, the inspection scope 30 is inserted and advanced in an advancing direction through the medical device M during the inspection process. In another embodiment, the inspection scope 30 is first inserted or advanced through the medical device M and then withdrawn from the medical device while the inspection is being performed. In yet another possible embodiment, inspection can occur during both the insertion and withdrawal of the inspection scope 30.

[0058] In some embodiments, inspection station 22 also includes a position tracker (e.g., position tracker 138 shown and described with reference to FIG. 3). Position tracker 138 may be part of inspection scope 30 or may operate as part of inspection station 22. In yet other examples, the position tracker may be part of medical device inspection coordinator 16. In some embodiments, two or more of inspection station 22, medical device inspection coordinator 16 (and / or another system), and inspection scope 30 cooperate to collectively perform the operations of the position tracker. The position tracker identifies the position of inspection scope 30 relative to the medical device.

[0059] In various possible embodiments, position can be determined quantitatively or qualitatively, or both. For example, a quantitative position can be a measurement. One example of a measurement is a distance from the opening of the medical device M (or from another reference point). For example, the position tracker can use the opening of the medical device M as an origin position and then measure the movement of the tip of the inspection scope 30 into the interior of the medical device M relative to the origin position (e.g., 1 cm, 2 cm, 3 cm, 4 cm, etc.). Examples of quantitative position can be defined with respect to a particular portion or location within the interior of the medical device M, such as at or near a particular hotspot, or at or near a particular portion, edge, or other location. Such qualitative positions can also be identified using quantitative measurements or can be identified using other techniques, such as image recognition. The position tracker can use both quantitative and qualitative positions in some embodiments.

[0060] In some embodiments, the position tracker operates to identify the position when the image was taken, so that the exact location of the medical device at which the image was taken is known. The position tracker may also be used to measure the velocity of relative movement between the inspection scope 30 and the medical device. Velocity may also be calculated based on the detected positions and the duration of time elapsed between those positions.

[0061] In some of these examples, the position tracker 138 may use image recognition to recognize and identify positions within the device, or the position may be calculated based on the speed of sensed movement combined with the time of tracked movement. In some embodiments, the position is determined and / or input manually by an operator, such as using manual measurements, measurement lines or indicators on the medical device or inspection scope 30, or known landmarks on the medical device M. In other embodiments, the position tracker 138 may automatically determine, track, and record the position of the inspection scope 30 relative to the medical device M. In some embodiments, the inspection scope 30 may include a measurement device for determining position (e.g., a borescope may have tick marks or other measurement indicators that can be counted or otherwise read, such as using a camera or other optical detector, to determine the current position measurement). In some embodiments, video data is analyzed by the position tracker 138 to determine the position. In some embodiments, physical landmarks are identified to determine the position. Other automatic and manual methods for determining the position may be used, as described herein.

[0062] In some embodiments, the position tracker also determines and tracks the orientation of the inspection scope 30 relative to the medical device M. For example, the position tracker can identify a rotational orientation. The orientation can identify, for example, the top and / or bottom of the medical device, or a rotational position as measured as degrees from a reference orientation. The orientation can then be used, for example, to identify both the location and orientation of landmarks or anomalies on the medical device.

[0063] Some embodiments do not include a position tracker. However, in some embodiments, position information can still be obtained. For example, when an anomaly is detected, the position can be measured by observing or marking the current depth of the inspection scope 30, withdrawing the inspection scope 30, measuring the length of the inspection scope 30 from the observed or marked point to the tip of the inspection scope 30, etc. In some embodiments, the measured position is entered by the operator into the computing device 14, which provides the position to the medical device inspection coordinator 16.

[0064] In some embodiments, a user interface is presented to the user as the user inspects a medical device using the inspection scope 30. The user interface may indicate that the current location of the inspection scope 30 is a landmark, such as a hotspot. In some embodiments, real-time alerts are presented to the user on the user interface. For example, the user may be alerted to the detected quality or condition of an area of ​​the device as the scope progresses through the device. In some embodiments, the user may be alerted that the scope is approaching a landmark, such as a hotspot. In some embodiments, the user may be notified in real time that an anomaly has been detected or may exist. In some examples, real-time recommendations may be presented to the user. For example, a recommendation may be presented that the user speed up or slow down the movement of the scope. In another example, a recommendation may be presented to apply a dosage of UV or other treatment while the inspection is in progress.

[0065] Step 108 is performed to store the inspection data. In one example, the inspection data includes images (optionally including video) captured by the inspection scope 30. Another example of inspection data is a timestamp identifying the date and / or time the image was captured. Another example of inspection data is location data from the location tracker 138 identifying the location where the image was captured. The data may be saved to a storage device. In some embodiments, the data is stored in one or more databases, which may consist of or include one or more third-party databases. One example of one or more databases is the asset database 18 (see FIG. 1).

[0066] Inspection data may also include process data. Process data is data that documents the steps of the inspection system during an inspection. Any of a variety of data may be collected. For example, position and time data may be collected. The position and time data may be associated with captured images or video clips or recordings. Speed ​​data may be collected to identify the relative speed of movement of the inspection scope 30 with respect to the medical device. The images may be evaluated to determine image quality, and data recording the image quality may be stored.

[0067] The processing data may also include information about one or more operators (e.g., technicians) involved in one or more steps of the inspection process, such as the date and time the step occurred, and the identity of the operator, such as a name or identification number. Similarly, in some examples, information about the physical location of a cleaning or inspection station (e.g., a workstation) or building may be collected and stored. In these examples, such information may be manually entered by an operator or determined by various scanning (e.g., barcode, RFID, etc.) or location determination processes.

[0068] In some embodiments, the processed data includes information about the inspection system and / or inspection scope 30 being used to perform the inspection and scan. The information may include the manufacturer's name, model number, and / or identification number. The information may also include inspection system characteristics (e.g., length, diameter, etc.) and capabilities (e.g., cleaning function (e.g., brush), disinfection function (e.g., UV light)), and whether such capabilities were utilized. If so, what, when, where, how much, and / or for how long. For example, data can be collected regarding what wavelength of UV light was used (e.g., UV-C), what intensity, where it was used, and the length of exposure time or other dosimetry.

[0069] Storage of the test data may be temporary or permanent. For example, in some embodiments, the test data is stored for processing by step 110, and unwanted data may be deleted later. In other embodiments, the test data is stored regardless of other steps.

[0070] The inspection data can be stored in a variety of ways, such as in one or more files, a database, etc. In some embodiments, the images are associated with corresponding data, such as location and time data. The data can be stored in a database and associated with the image in the database. In another possible embodiment, the data can be stored in the image metadata (e.g., the time and location fields of the image), as inspection metadata, or as a filename. For example, the filename can be a combination of one or more of a medical device identifier, date, time, location, and / or other data. In some embodiments, the inspection data can be stored in another system and / or database, such as an asset tracker.

[0071] Step 110 is performed to analyze the inspection data. In some embodiments, step 110 is performed by a computing device 14 (e.g., computing device 14B shown and described with reference to FIGS. 1 and 3). The computing device may be part of the inspection scope 30 (e.g., physically connected or incorporated within the same housing) or may be a separate computing device. Communication may be direct wired, wireless, or via a data communications network such as a local area network, the Internet, an off-site network, or the like.

[0072] In some embodiments, step 110 is performed by an inspection analyzer (e.g., inspection analyzer 128 shown and described with reference to FIG. 3, which may include an anomaly detector 130). In some embodiments, inspection analyzer 128 is or includes one or more software applications. In some embodiments, inspection analyzer 128 runs on a computing device. In some embodiments, inspection analyzer 128 is or includes a neural network, such as a convolutional neural network (CNN), which may run on one or more computing devices and may include one or more remote computing devices. An example of a neural network is a deep neural network. In some embodiments, inspection analyzer 128 includes anomaly detector 130 (see FIG. 3). Anomaly detector 130 may be or include one or more machine learning algorithms (e.g., artificial intelligence) including one or more machine learning models trained to detect or predict whether an anomaly is present. In some embodiments, anomaly detector 130 performs image analysis. In some embodiments, anomaly detector 130 performs object recognition. In some embodiments, the anomaly detector 130 is or includes an image classifier. The anomaly detector 130 can be or include one or more machine learning algorithms, which can be supervised or unsupervised machine learning algorithms.

[0073] In some embodiments, the anomaly detector 130 is trained on a set of training data. The training data may include positive training examples, negative training examples, or positive and negative training examples. Examples may include images of medical devices without anomalies and images of medical devices with anomalies. The training examples may be labeled (or "tagged") with specific data, such as whether an anomaly is present or not and / or the type or class of anomaly. For example, various anomalies are possible, including debris, damage, discoloration, liquid droplets (moisture), etc.

[0074] In some embodiments, anomaly detector 130 can detect or predict whether a certain anomaly (or one of multiple anomalies) is present. The analysis can be performed based on a single image, the entire exam, or a portion of the exam. The analysis can also be performed on one area or multiple areas of the medical device. In some of these embodiments, anomaly detector 130 is trained using annotated example exam data and / or using manual input received during the course of the exam. Other examples are disclosed herein.

[0075] Step 112 is performed to generate analytical data based on the analysis performed in step 110 .

[0076] The analytical data may include, for example, whether an anomaly was detected or a prediction (e.g., a confidence score) of whether an anomaly exists. The analytical data may include the status of the medical device M. Examples of a status may be clean, dirty, or damaged. Other conditions may include the location of the device, whether the medical device M is available or unavailable, whether the medical device M is removed for use, and / or whether the medical device M is removed for service. In other embodiments, other conditions are possible.

[0077] A confidence score can be generated. In one possible example, the confidence score is a score within a given range (e.g., −1 to 1, 0 to 1, 0 to 10, 0 to 100, etc.). In another possible example, the confidence score is a percentage between 0% and 100%, with 0% indicating the minimum confidence level and 100% indicating the maximum confidence level.

[0078] Such data may be generated and stored for the medical device M as a whole, or for specific locations or regions of the medical device M, and in some examples, for aggregated locations. For example, the analysis data may indicate whether an anomaly has been detected and what type of anomaly, or whether the medical device is likely to have an anomaly or type of anomaly. As another example, the analysis data may include such data for multiple different locations or regions of the medical device. In other words, it may separately show the results of the analysis for multiple different portions of the medical device. In some embodiments, the analysis data may be visually displayed through a graphical user interface to show the relevant location or region of the medical device and a description of the condition of that region, such as whether an anomaly was identified, the type of anomaly, the confidence level, the severity of the anomaly, etc. The visual indication of the anomaly may be done through a graphical display, such as by displaying a shape (e.g., a circle, a square, an arrow, etc.) on or near the point or region in the image, color-coding the point or region in the image, or presenting a variety of other possible graphical displays. In some embodiments, the display device may compare the medical device to a reference image that shows what a normal medical device should look like. The display of the reference image may be a display of the complete medical device, or a relevant point or area of ​​the medical device.

[0079] In some embodiments, the severity of the anomaly is determined. Severity can be measured and reported in various ways. In some embodiments, severity is determined by a test analyzer. In one example, severity is determined by a machine learning algorithm trained to identify anomalies and their severity. In some embodiments, severity is measured and reported using a rating scale (e.g., 0 to 3 or 0 to 10). In another embodiment, severity is reported by a classification (e.g., none, low, medium, high). In some embodiments, the classification includes a color code (e.g., green for low, yellow for medium, red for high). The color code can be used to visually depict the severity of the identified anomaly in a user interface. The severity report can be provided to an operator to determine whether to take action, whether to suggest action, and / or which action to take or suggest. In some embodiments, different severities are associated with different instructions that can be provided to a user or that can initiate different workflows. Non-limiting examples of some possible instructions and workflows include: no further treatment required, re-clean, refer to device manufacturer, send for repair, replace device, use on patient, do not use on patient, quarantine until further notice, and determining that the medical device has reached the end of its life.

[0080] The medical device inspection system 10 can also monitor and store information about the inspection scope 30. For example, the status of the inspection scope 30 can be determined. In some embodiments, the inspection scope 30 is considered clean until it is exposed to a medical device that is determined to be dirty. The status of the inspection scope 30 is then set to dirty. The dirty status can be saved and can trigger other actions, such as other workflows. If the inspection scope 30 is determined to be damaged, its status can be set to damaged. In other embodiments, other states are possible.

[0081] Step 114 is performed to store the analytical data. In some embodiments, all analytical data is stored, while in other embodiments, analytical data is stored only if the data meets a certain threshold, such as when a prediction exceeds a certain probability. In some embodiments, the analytical data is stored in a database, such as asset database 118.

[0082] In some embodiments, the stored analytical data is searchable. For example, a search can be performed to determine what historical tests have been performed on the device itself or on a collected collection of devices. An example of a collection of devices is a collection of devices owned by a user or group. For example, a hospital can check the status of their entire fleet of devices.

[0083] In some embodiments, the user interface allows a user, such as a data manager or an individual with testing expertise, to review, approve, and / or modify the analytical data before storing the data. For example, a user can accept analytical data when it appears to be accurate and reject analytical data when it appears to be inaccurate. In some embodiments, the feedback, analytical data, and testing data are returned to the artificial intelligence system for further training of the model used to generate the analytical data.

[0084] Step 116 is performed to generate one or more outputs. The outputs may include presenting information to an operator, initiating an action, or both. The actions may include triggering one or more workflows.

[0085] In some embodiments, the output includes a report or data provided to an integrated database and / or software system, such as an asset tracker. In some embodiments, the report is in a defined format, such as Adobe® PDF format, Microsoft® Word® format (i.e., .doc or .docx), a structured data format such as JAVA® Script Object Notation (JSON), or XML format. In another example, the report may be provided via a user interface display or website interface (i.e., HTML format). Many other examples are possible and within the scope of this disclosure. In some embodiments, the data is input to another system (e.g., a tracking system). Other outputs may include notifications sent to different actors that may need to take action based on the status of the medical device. For example, a manufacturer may be notified of certain anomalies, such as defects, to initiate replacement of the defective device. In some embodiments, the report is provided to multiple systems, including manufacturers, repairers, medical administrators, infection preventionists, etc. The report may be automatically stored in one or more databases, including asset database 18 shown and described with reference to FIG. 1. Other systems and / or databases that may receive and / or send some or all of the output include manufacturer databases, healthcare enterprise databases, other tracking databases (e.g., inventory systems, repair systems, electronic health management systems, scheduling systems, procedure scheduling systems, etc.) In some embodiments, the output may include a summary of all of the findings.

[0086] In some embodiments, the output includes a display of an image from the inspection, hi some embodiments, the image may be presented with additional information, such as processed or analyzed data, or other display based thereon.

[0087] In some embodiments, the output includes a display of one or more detected anomalies (or other analytical data) with or without the display of a corresponding image.

[0088] In some embodiments, the output may include a display of a reference image associated with the medical device, which may allow an operator and / or exam analyzer to compare and contrast images from the exam with the reference image.

[0089] Output may also include storage of images from the exam, or transmission of the images (and any associated data) to another device or system. In some embodiments, the images and / or data are stored and delivered in a format compatible with tracking software, healthcare system electronic record systems, or other systems.

[0090] The displayed processed data may include the position of the inspection scope 30 relative to the medical instrument, the velocity of the inspection scope 30 relative to the medical instrument, and an indication of the quality of the image being captured.

[0091] In some embodiments, the image may not be displayed until a possible abnormality is detected.

[0092] Based on the analysis performed by the test analyzer, various possible steps can be initiated. One example is that images or video clips (e.g., over a period of time or along an area of ​​the medical device) may be captured and stored. For example, when a potential anomaly is detected, images and other data may be stored in association with the potential anomaly. Another possible example is alerting an operator by sending a message (e.g., email, text message, app notification), displaying an alert, generating an audible alert, etc. Persons or systems other than the operator may also or alternatively be notified. For example, a message may be sent to a medical facility, a repair company or repair professional, a medical device manufacturer, or other administrator or other person. The message may be a text-based message and may include an image or any other information or data described herein. As another example, data may be stored or transmitted documenting the potential anomaly. As another example, a workflow may be initiated to initiate a cleaning or repair process or to initiate an order for a replacement medical device.

[0093] 2 may be performed in a different order than that shown. Additionally, more or fewer steps may be performed, and not all steps may be required.

[0094] Any one or more of steps 102 through 114 (including any combination thereof or all of steps 102 through 114) can be performed once for a single test, or can be repeated throughout the course of a test. If repeated while a test is being performed, the operator can be notified as soon as a possible anomaly is detected, or other action can be taken without having to first complete the entire test. When a possible anomaly is detected, a user interface display can be generated to alert the operator, for example. Other audible or visual alerts are also possible. The operator can then review the possible anomaly. Other workflows or actions can also be performed, as discussed herein. In some embodiments, the steps are performed continuously in real time during testing of the medical device. In some embodiments, at least some of the steps are performed simultaneously.

[0095] In some embodiments, the medical device inspection system 10 may further include software that guides an operator through one or more steps of the inspection process.

[0096] For example, the medical device inspection system 10 may identify whether the medical device M is suitable for use with the inspection scope 30. In some embodiments, the medical device inspection system 10 identifies a particular type of inspection scope 30 to be used with a particular type of medical device M.

[0097] In some embodiments, the system maintains a database of medical devices. For example, any medical device with a lumen can be cataloged in the database. The database can also store information about whether the medical device M is of a type that can or should be inspected with the inspection scope 30. The type of inspection scope 30 can also be identified for each medical device. If the medical device M is not already in the database, the software can include an option to add a new medical device to the database. In some embodiments, the database stores medical device specification data, such as drawings, photographs, and / or reference images, or other details or information about the medical device. In some embodiments, the system can interact (e.g., via an API or other interaction method) with other systems that maintain records of medical devices.

[0098] Similarly, information about the various inspection scopes 30 may also be stored in the database.

[0099] Some embodiments include a mechanism for a medical device manufacturer, other company, or user to add a catalog or other set of one or more medical devices to a database (such as asset database 118) and corresponding medical device data. The same or similar mechanism can be used to add examination scopes and corresponding examination scope data.

[0100] In some embodiments, the system stores instructions for use (IFUs) for the medical device. The system can retrieve the instructions for use for the particular medical device being inspected, for example, based on make, model, serial number, and / or automatic identification, as described herein.

[0101] In some embodiments, the medical device inspection system 10 uses information from the asset database 118 or automated determinations to identify specific points or areas of the medical device M for inspection. An example of a specific point or area is a landmark, such as a hotspot. In some embodiments, the entire medical device M may be inspected, while in other embodiments, the inspection system 10 may be directed to a specific area, such as a hotspot, of the medical device M.

[0102] In some embodiments, the system guides the operator on where to look while performing the inspection. For example, it may identify a particular point or area of ​​the medical device M to be inspected. Such identification may be performed and instructed graphically through a user interface, through other explanations or instructions, or by live monitoring of the position of or images from the inspection scope 30. As described above, the position of the inspection scope 30 relative to the medical device M may be determined using mechanical means, image recognition, manual input, etc.

[0103] Fully automated inspections (including moving the inspection scope 30 relative to the medical device M) are also possible using the mechanical advancement system 134 described herein. As mentioned above, the mechanical advancement system 134 can be robotic, mechanical, automatic, or manual. In some embodiments, the inspection scope 30 is moved within and relative to the medical device M. In some embodiments, complete images are captured during the automated inspection. In another possible embodiment, images are captured only for specific landmarks, such as hot spots for the device. In some examples, the inspection system automatically captures images when the inspection scope 30 is at or within a range of landmark locations. The images are then stored, analyzed, and output, if any, is generated.

[0104] The output can be provided in real time or after the test is completed.

[0105] In some embodiments, the output can provide real-time feedback to the operator while the inspection is being performed. As one example, the inspection system 10 can monitor the speed at which the inspection scope 30 is moving relative to the medical device and can provide feedback as to whether the speed is too fast or too slow. For example, the system 10 can identify a preferred range of processing speeds, including a minimum desired speed and a maximum desired speed, and can provide an indication or warning as to whether the speed is within the preferred range of processing speeds or outside of the preferred range of processing speeds. The display can further indicate whether the speed is too fast or too slow, or can provide instructions to the operator, such as "speed up" or "speed down." In some embodiments, a preferred range of speeds is selected or indicated to the user (as described above) for performing the appropriate inspection (whether automated or manual). In another embodiment, a preferred range of speeds is selected or indicated to the user for providing the appropriate dosage of ultraviolet (UV) light to decontaminate internal components or surfaces. Some embodiments have multiple preferred speed ranges for more than one of these purposes and depending on the current process mode of the inspection system. In some embodiments, the preferred rate is determined as a function of the known power of the UV to provide an appropriate dosage.

[0106] 3 is a schematic block diagram illustrating an exemplary medical device inspection station 22. The exemplary inspection system includes a computing device 14B and an inspection assembly 131. The exemplary computing device 14B includes a medical device inspection coordinator 16B. In this example, the medical device inspection coordinator 16B includes an inspection analyzer 128 with an anomaly detector 130. The exemplary computing device also includes a display device 122 that displays a user interface 124. The exemplary inspection assembly 131 includes an inspection scope 30 that includes a support structure 133, an advancement system 134, a position tracker 138, and a camera 140. The inspection assembly 131 is shown supporting a medical device M thereon. Inspection data 132 is generated by the inspection system and can be communicated between the inspection assembly 131 and the computing device 14B.

[0107] In some embodiments, inspection station 22 is used as part of method 100 as shown and described with reference to Figure 2. Examples of user interfaces 124 are shown and described with reference to Figures 5-7. In some embodiments, computing device 14B includes some or all of the components shown and described with reference to Figure 4. In one embodiment, computing device 14B communicates over a network with other computing devices, such as computing device 14A of server computing environment 12 as shown and described with reference to Figure 1.

[0108] In some embodiments, the inspection assembly 131 includes a support structure 133 for supporting the inspection scope 30 and the medical device M. The support structure 133 can take a variety of possible forms and typically includes at least a frame or other housing that supports and optionally guides the movement of the various components of the inspection station 22 relative to one another. In some embodiments, the support structure 133 is a vertical support structure that can vertically support one or more of the medical device M or the inspection scope 30, or portions thereof. An advantage of a vertical support structure configuration is that it may reduce table or floor space, for example. In other embodiments, the support structure 133 includes a horizontal support structure for horizontal support.

[0109] In some embodiments, inspection scope 30 includes a camera 140 for visually inspecting medical device M. In some embodiments, inspection scope 30 transfers inspection data 132 to computing device 14B. Example components shown in FIG. 3 are also described with reference to method 100 shown and described in FIG. 2.

[0110] The advancement system 134 is configured to move the inspection scope 30 relative to the medical device M. In some embodiments, the advancement system 134 is motorized to move the inspection scope 30 or the medical device M.

[0111] As mentioned above, in some embodiments, the advancement system 134 is configured to operate automatically. For example, the advancement system 134 may include a robotic arm or an automated feeder that advances the inspection scope 30 through the medical device M. Other motorized, mechanical, or manual methods may be used in different embodiments and are disclosed herein. In some examples, the advancement system 134 advances the inspection scope 30, capturing inspection data, which is processed by an AI model to provide real-time feedback for automatically controlling the advancement system 134. For example, the AI ​​system may analyze the captured image data to determine how the inspection scope 30 should be advanced through the medical device M. In some embodiments, the AI ​​model may output findings that are verified / approved by a user before being reported and / or stored in a database.

[0112] In some embodiments, a user manually advances the inspection scope 30 through the medical device M. Examples of inspection scopes 30 are disclosed herein. For example, the inspection scope 30 can be a borescope, such as a fiberscope. In some embodiments, the inspection scope 30 includes one or more fiber optic elements (which may include one or more optical fibers, such as a fiber bundle) that carry light from a light source to the tip of the inspection scope 30. In other embodiments, the light source (e.g., a light emitting diode (LED)) is located at or near the tip. Additionally, in some embodiments, the fiber optic element transmits light from the tip back to a camera 140 (or other optical sensor) located away from the tip.

[0113] The camera 140 operates to capture images of the medical device M. The images may be individual images or videos. Videos may be composed of multiple images. The image and video data are included in the examination data 132, which is transferred (wired or wirelessly) to the computing device 14B. The examination data may also include a timestamp identifying the date and / or time the image was taken. In some embodiments, the examination data 132 also includes processing data. Examples of examination data are disclosed herein.

[0114] Medical device M may be one of a variety of different types of medical devices and may include an elongated, flexible body having one or more internal orifices, examples of which include endoscopes, fiberscopes, catheter-based medical / surgical instruments, and other long, thin, reusable instruments.

[0115] Some embodiments include a position tracker 138. The position tracker 138 is configured to detect and monitor the position of the inspection scope 30 relative to the medical device M during a medical device inspection. Examples of position trackers are described herein.

[0116] Computing device 14B operates medical device test coordinator 16B. In one example, medical device test coordinator 16B includes test analyzer 128 and anomaly detector 130, examples of which are disclosed herein.

[0117] The computing device includes a display device 122 for presenting a user interface 124. In some embodiments, output from medical device inspection coordinator 16B is presented on display device 122. User interface 124 can display images from the inspection along with additional information, such as operational or analytical data, or other presentations based thereon. Examples of user interface 124 are shown and described with reference to FIGS. 5 through 7. Other examples are described herein. In some embodiments, the user interface allows a user to view a series of images in the order of the inspection (e.g., from the distal end or the proximal end).

[0118] In some embodiments, the inspection station 22, including the medical device inspection coordinator 16B and the inspection assembly 131, operates to perform an inspection of one or more landmarks, such as specific points of interest on the medical device. The performance of the inspection may be automated, or in other embodiments, the inspection station 22 may provide instructions or otherwise guide the operator to inspect such landmarks. One example of a landmark is a hotspot. A hotspot is a point or area on a medical device that is prone to anomalies. In some embodiments, the hotspot is predetermined. The hotspot can be based on physical characteristics or visually identifiable characteristics, such as a joint, transition, or intersection between two parts or materials, a recess or depression, an opening, or a surface texture. In some embodiments, the hotspot is identified from a study or literature analysis that indicates the most likely spots for anomalies. In some embodiments, the hotspot is identified by data updated from the inspection software. For example, the hotspot can be provided by another party (or other system 26), such as the FDA, a manufacturer, a third-party repair specialist, or an equipment cleaning specialist. In some embodiments, the hotspot is updated in real time.

[0119] In some embodiments, one or more landmarks can be identified. Landmarks may be predefined and stored in a database, such as in association with a type of medical device. For example, landmarks can be linked to a particular medical device (as serialized), can be linked to a make / model year, a category of device, etc. Landmarks can also be manually defined by an operator. For example, an operator can identify a particular point on a medical device as a landmark or hotspot.

[0120] Various user interface configurations (e.g., user interface 124 shown and described with reference to FIG. 3 ) can be used to receive landmark identification from the operator, such as by receiving input on a photograph of the medical device M, by receiving input on a diagram of the medical device M, by providing location information (e.g., a length of 10 cm from the front end of the medical device, a range of 5 cm to 15 cm from the front end of the medical device), etc. In some embodiments, landmarks are identified by image recognition or at specific locations identified by the end user. For example, channel junctions, elevator mechanisms, distal tips may be recognized and identified from the captured image.

[0121] In yet another embodiment, landmarks can be determined automatically, such as by computer analysis of historical data to determine common areas where anomalies have previously been identified for this type or model of medical device. Computer analysis can also be performed on the fly, such as using artificial intelligence to automatically predict and identify landmarks for medical device M, such as current image data, knowledge of the medical device's construction, and / or historical data for this or other similar medical devices.

[0122] In some embodiments, once the medical device M is identified, the inspection station 22 is configured to present the operator with a landmark tutorial for the selected medical device M. The tutorial may include a training presentation traversing one or more landmarks, one or more schematic diagrams of the medical device M with identified landmarks, an example inspection scope image showing the operator what the inspection will look like, or a variety of other possible training presentations or visual representations.

[0123] In some embodiments, the inspection station 22 is configured to store, present, or otherwise provide or make available historical records, for example, relating to a particular medical device M, make / model, category of device, and / or age of device. For example, historical photos of the medical device M or medical device inspection can be shown to the operator or incorporated into the report. This can help the operator learn about any known or previous anomalies that have been identified and can provide a reference image the operator can use to compare previous conditions to current conditions. The historical records can include whether the device is new, the age of the device, the number of times the medical device has been used, recent or past damage, recent or past repairs, or other information.

[0124] In some embodiments, the information may also include patient data, such as information about patients for whom the medical device M was previously used (e.g., the patient's name or patient identification number), which procedures were performed, medical findings or diagnoses (such as to document that the medical device may have been exposed to certain biohazards, chemicals, radiation, etc.), or other patient-related data (with or without patient identification information). The information may also include healthcare provider information, such as information about the healthcare professional who last used the medical device M. The information may also include past (historical) patient or healthcare provider information.

[0125] In some embodiments, medical device inspection coordinator 16 operates to perform some or all of the steps 100 shown in Figure 2. For example, medical device inspection coordinator 16 may identify the medical device (step 102) and retrieve medical device data (step 104) from asset database 18 or from other sources, such as health management system 24 or other systems 26.

[0126] The medical device inspection coordinator 16 then operates to coordinate the medical device inspection (step 106). For example, the medical device inspection coordinator 16 can automatically control the inspection assembly 131 (including the advancement system and inspection scope) to perform the medical device inspection, such as using the control signal 136. The medical device inspection coordinator 16 can use the retrieved information to identify landmarks within the medical device for inspection and control the advancement system 134 so that the camera 140 captures images of these areas. Other options are possible, as discussed herein, such as a complete inspection of the medical device M. The resulting inspection data 132, including the images, can then be stored by the medical device inspection coordinator 16, such as in the asset database 118.

[0127] In another example, medical device test coordinator 16 assists an operator in performing a medical device test. In this example, a user interface may be presented to guide the operator through the test. Certain steps may still be automatically controlled by medical device test coordinator 16, even when an operator is involved. Various information, guidance / instructions, reference images, etc., may be presented during the test to assist the operator, as discussed in further detail herein.

[0128] Analysis of the test data is then performed, in some embodiments, utilizing test analyzer 128 and anomaly detector 130. Test analyzer 128 can process the test data and generate analysis results. In some embodiments, test analyzer 128 operates to identify landmarks within the medical device. In some embodiments, test analyzer 128 utilizes position data generated by position tracker 138. In some embodiments, the test analyzer performs object recognition, for example, utilizing one or more machine learning models, to identify landmarks on the medical device.

[0129] To aid in the analysis, the test analyzer 128, in some embodiments, utilizes an anomaly detector 130. The anomaly detector 130 operates to evaluate the medical device to assess the presence or absence of anomalies. In some embodiments, the anomaly detector 130 may utilize human input, such as by displaying corresponding images of particular landmarks along with a reference image and prompting a user to provide input as to whether an anomaly is present at the landmark. In another example, the anomaly detector 130 utilizes a machine learning model to automatically analyze one or more images of the medical device to determine or predict whether an anomaly may be present.

[0130] In some embodiments, anomaly detector 130 is or includes a neural network, such as a convolutional neural network (CNN), which in some embodiments operates to process image data from medical device inspections, for example, to extract features from the images.

[0131] In some embodiments, the anomaly detector 130 includes an input layer that accepts medical device images from a medical device inspection. In some embodiments, the images are preprocessed. Preprocessing may include, for example, one or more of resizing, normalization, dilation, grayscale conversion, or noise reduction. Such preprocessing can improve the quality of subsequent machine learning operations by providing consistent inputs to the model.

[0132] In some embodiments, anomaly detector 130 includes multiple convolutional layers. The layers are configured to detect, for example, patterns, textures, and features in an image. Multiple layers can be combined with pooling layers to improve the ability of anomaly detector 130 to understand different aspects of an image.

[0133] In some embodiments, the anomaly detector 130 includes one or more fully connected (FC) layers. An FC layer is an example of a dense layer. One or more dense layers can be used to help the anomaly detector 130 make a classification decision. For example, one or more FC layers can be used to combine extracted features and perform a final classification.

[0134] In some embodiments, the anomaly detector 130 includes an output layer that provides an output of the machine learning model. For example, the output layer can output a determination of whether the medical device is abnormal. An example output is "normal" or "abnormal." As another example, the output can include a probability (i.e., that the medical device is normal or abnormal) in the form of a percentage or number, for example, between 0 and 1. In some embodiments, the output is binary (i.e., a binary classification by a binary classifier), while in other embodiments, the output can have multiple outputs (i.e., a multi-class classification by a multi-class classifier).

[0135] In some embodiments, the anomaly detector 130 is trained using a labeled training set. In one example, training involves a training algorithm (such as a gradient descent algorithm) to adjust the weights of the neural network to minimize the difference between its predictions and the actual labels.

[0136] Additionally, in some embodiments, anomaly detector 130 utilizes one or more reference images to enable the medical device inspection data image to be compared to the reference images. In some embodiments, anomaly detector 130 utilizes image differencing, in which a reference image (of a normal device without anomalies) is subtracted from the inspection image to highlight the differences. In some embodiments, anomaly detector 130 utilizes thresholding to convert the difference image to binary (black and white) to highlight significant differences. In some embodiments, the binary image can then be used as an additional input to a neural network to help the anomaly detector better focus on the differences.

[0137] In some embodiments, the anomaly detector 130 may include continuous learning, such as a feedback loop and retraining. The feedback loop allows additional images (including anomalies or both normal and abnormal images) that are collected to be added to the training set. The model may then be retrained on the updated data set to improve its accuracy.

[0138] In some embodiments, the output of anomaly detector 130 is presented to an operator for review. The operator can make a final determination of whether an anomaly exists. In some embodiments, the operator provides user input to update the anomaly determination. In some embodiments, user input overrides (or confirms) the automatic anomaly determination by anomaly detector 130. Additionally, in some embodiments, user input can be provided to manually identify medical device anomalies not detected by anomaly detector 130, which are then recorded.

[0139] FIG. 4 illustrates an exemplary architecture of a computing device 14 that may include any of the computing devices disclosed herein (e.g., any one of computing devices 14A, 14B, 14C, or 14D) and may be used to implement aspects of the present disclosure. Computing device 14 may be local to or remote from inspection scope 30 and one or more other computing devices. Computing device 14 may be a personal computer or a server computing device. Computing device 14 illustrated in FIG. 4 may be used to execute the operating system, application programs, and software modules (including software engines) described herein.

[0140] Computing device 14, in some embodiments, includes at least one processing unit 180, such as a central processing unit (CPU). Various processing units are available from various manufacturers, e.g., Intel or Advanced Micro Devices. In this example, computing device 14 also includes a system memory 182 and a system bus 184 that couples various system components, including system memory 182, to processing unit 180. System bus 184 may be one of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.

[0141] Examples of suitable computing devices for computing device 14 include a server computer, a desktop computer, a laptop computer, a tablet computer, a mobile computing device (such as a smartphone, an iPod® or iPad® mobile digital device, or other mobile device), or other device configured to process digital instructions.

[0142] System memory 182 includes read-only memory 186 and random access memory 188. A basic input / output system 190, containing the basic routines that help transfer information within computing device 14, such as during start-up, is typically stored in read-only memory 186.

[0143] Computing device 14 also includes a secondary storage device 192, such as a hard disk drive in some embodiments, for storing digital data. Secondary storage device 192 is connected to system bus 184 by a secondary storage interface 194. Secondary storage device 192 and its associated computer-readable media provide non-volatile storage of computer-readable instructions (including application programs and program modules), data structures, and other data for computing device 14.

[0144] In some embodiments, a hard disk drive is provided as secondary storage, although other types of computer-readable storage media are used in other environments. Examples of these other types of computer-readable storage media include magnetic cassettes, flash memory cards, digital video disks, compact disks (CDs) read-only memory, digital versatile disks (VMDs) read-only memory, random access memory, or read-only memory. Some embodiments include non-transitory media. Furthermore, such computer-readable storage media may include local storage or cloud-based storage.

[0145] A number of program modules may be stored in secondary storage 192 or memory 182, including an operating system 196, one or more application programs 198, other program modules 200 (such as the software engines described herein), and program data 202. Computing device 14 may utilize any suitable operating system, such as Microsoft Windows®, Google Chrome®, Apple OS, and other operating systems suitable for the computing device.

[0146] In some embodiments, a user provides input to the computing device 14 through one or more input devices 204. Examples of input devices 204 include a keyboard 206, a mouse 208, a microphone 210, and a touch sensor 212 (such as a touchpad or touch-sensitive display). Other embodiments include other input devices 204. The input devices are often connected to the processing unit 180 through an input / output interface 214 coupled to the system bus 184. These input devices 204 can be connected by any number of input / output interfaces, such as a parallel port, a serial port, a game port, a universal serial bus, or the like. Wireless communication between the input devices and the interface 214 is also possible, and in some possible embodiments includes infrared, BLUETOOTH® wireless technology, 802.11a / b / g / n, cellular, or other radio frequency communication systems.

[0147] In this embodiment, a display device 122, such as a monitor, LCD display, projector, or touch-sensitive display, is also connected to system bus 184 via an interface, such as video adapter 218. In addition to display device 122, computing device 14 may include various other peripheral devices (not shown), such as speakers or a printer.

[0148] When used in a local area networking environment or a wide area networking environment (such as the Internet), computing device 14 is typically connected to the network via a network interface 220, such as an Ethernet interface. Other possible embodiments use other communication devices. For example, some embodiments of computing device 14 include a modem for communicating across the network.

[0149] Computing device 14 typically includes at least some form of computer-readable media. Computer-readable media includes any available media that can be accessed by computing device 14. By way of example, computer-readable media includes computer-readable storage media and computer-readable communication media.

[0150] Computer-readable storage media include volatile and nonvolatile, removable and non-removable media implemented in any device configured to store information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technology, compact disc read-only memory, digital versatile disks or other optical storage devices, magnetic cassettes, magnetic tape, magnetic disk memory or other magnetic memory, or any other medium that can be used to store the desired information and that can be accessed by computing device 14. Computer-readable storage media does not include computer-readable communication media.

[0151] Computer-readable communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" refers to 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, computer-readable communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency, infrared and other wireless media. Combinations of any of the above are also included within the scope of computer-readable media.

[0152] The computing device shown in FIG. 4 is also an example of a programmable electronic device that may include one or more such computing devices, and when multiple computing devices are included, such computing devices may be coupled together with a suitable data communications network to collectively perform various functions, methods, or processes disclosed herein.

[0153] 5 illustrates an exemplary user interface 330 for medical device test coordinator 16 (shown in FIGS. 1 and 3). In this example, the user interface includes a test image display 332, a reference image display 334, a device ID 336, a list of saved images 338, operator annotations or notes 340, a capture button 342, and a settings button 344. This display is for illustrative purposes only; other user interfaces may have more or fewer components than those shown here.

[0154] In some embodiments, user interface 330 is a user interface with which an operator can interact while using medical device inspection system 10, such as medical device inspection scope 30. Medical device inspection coordinator 16B provides interface 330 with images and data from the medical device inspection system.

[0155] For example, the inspection image display 332 displays the most recent image received from the medical device inspection system while the medical device is being inspected, which may be a still image or a frame from a video feed.

[0156] In some embodiments, the interface 330 also presents one or more reference images to the operator. The reference images can be retrieved from an asset database for the particular medical device. The reference images can show, for example, what the original scope looked like when clean and fully functional. In this way, the operator can compare the inspection image display 332 to the reference image display 334 to check for anomalies or other differences between the inspection image display 332 and the reference image display 334. In another possible example, the reference images can show examples of anomalies so the operator can be on the lookout for such features. In another possible example, the reference images can include historical images from the same medical device currently being processed. This can be useful for comparing the inspection image display 332 with previous settings of images taken to see if anything has changed. Similarly, images over a period of time can be viewed to see the progression of anomalies, such as wear, damage, film or contaminant buildup, rust components, etc.

[0157] The interface 330 may display information about the medical device currently being processed (such as the device identifier 336) and / or the medical device inspection system currently being used.

[0158] The interface 330 may include a list 338 of images that have already been saved during the current inspection process, which can be reviewed by the operator if desired.

[0159] The interface 330 may also be configured to receive operator annotations. The operator may provide input identifying any state changes in the medical device, any noted anomalies, completion of workflow processing steps, or may make any general notes or observations. In some embodiments, the annotations are used to further train the machine learning model or to train a new or updated model.

[0160] A capture button 342 is provided for the operator to select when an image should be captured and saved. For example, if an abnormality is detected on the inspection image display 332, the capture button is selected to save the image. Alternatively, the capture button can be used to capture and save a video recording. In some embodiments, the operator can switch between an image capture mode or a video capture mode.

[0161] In some embodiments, the user can adjust one or more settings via settings button 344 .

[0162] In some embodiments, medical device test coordinator 16 provides the operator with detailed step-by-step instructions that guide the operator through the completion of the workflow steps. The instructions may also be displayed on interface 330.

[0163] In some embodiments, an asset database (e.g., asset database 18 shown in FIG. 1) can store a list of landmarks, such as hot spots, for each medical device. The list of hot spots identifies specific parts of the medical device where anomalies are most likely to be discovered. Hot spots can be components of the medical device that are prone to wear and may need to be replaced. Hot spots can also be locations where contaminants are likely to accumulate. Hot spots can also be locations where damage is more likely to occur, such as locations along flexible members that are more likely to bend or crack. The list of hot spots can be provided as a helpful guide to the operator or presented as a mandatory checklist of areas that the operator must carefully evaluate. In such cases, interface 330 can function to guide the operator through the evaluation of each hot spot and record data regarding the condition of each (automatically, based on user input, or a combination of both).

[0164] In some embodiments, the user interface 330 displays other data, such as the location of the medical device inspection scope 30 within the medical device. Location information can help the operator locate hot spots and can also help the operator document the location of possible anomalies. Location information can also be saved with the saved image. Similarly, time can be displayed and registered. Data related to the image (including anomalies, operator annotations, device ID, location information, time information, etc.) can be saved in a variety of ways, such as as data in an asset database, as metadata in the image (or video) file, as part of the file name, or any other way that data can be associated with the image. Such information may also be stored in the asset database record, for example, as data related to the completion of certain workflow steps or to document and verify that such steps have been completed.

[0165] 6 shows an exemplary user interface 360 ​​as may be displayed during a medical device exam. In this example, user interface 360 ​​includes a medical device display area 362 and a data display area 364. Exemplary medical device display area 362 includes a medical device display 363, a plurality of location indicators 366 (including location indicators 366A-H), and a current location indicator 368. Exemplary data display area 364 includes a current image display 380, a reference image display 382, ​​and other exam data 384.

[0166] The exemplary medical device display area 362 includes a medical device view 363, which is a graphical representation of a medical device or a portion of a medical device. By way of example, the graphical representation is a top view. In other embodiments, other views (such as side, bottom, front) can be used. In some embodiments, a combination of views can also be included. The graphical representation can be a photograph, schematic, drawing, block diagram, animation, or other graphical representation of the medical device or portion of the medical device. Additionally, in some embodiments, the medical device display area 362 can move or update as the examination progresses to depict only the current portion of the medical device.

[0167] The exemplary user interface 360 ​​provides a live status of a medical device inspection as it occurs. In this example, the inspection scope 30 (FIG. 3) has already been inserted through its distal tip (proximal position indicator 366A). The position of the inspection scope 30 can be determined, for example, by the position tracker 138 (FIG. 3). The inspection scope 30 has advanced through positions identified by position indicators 366A, 366B, and 366C and is currently positioned at the position identified by position indicator 366D. Inspected positions are indicated in the user interface 360 ​​using a first graphical element (e.g., a filled circle). The current position is marked in the display by current position indicator 368. Uninspected positions, identified by the remaining position indicators 366E through 366I, are indicated in the user interface 360 ​​using a second graphical element different from the first (e.g., an unfilled circle) to provide a visual indication of which portions of the medical device have already been inspected and which portions are being inspected during the current inspection process. In this example of user interface 360 ​​, the inspection scope 30 is not shown, although other embodiments include a graphical representation of the inspection scope 30 within the medical device display area 362 .

[0168] 2, when a test is in progress (step 106), test data 132 can be stored (step 108), the test data can be analyzed (step 110), analysis data can be generated (step 112), the analysis data can be stored (step 114), and output can be generated (step 116). Any one or more of these steps can occur during the test (step 106) or, alternatively, can occur after the test is completed.

[0169] In some embodiments, a current position indicator 368 is provided on the medical device display 363 (in the medical device display area 362 of the user interface 360) to visually identify the current position of the inspection scope 30, such as the position of the tip of the inspection scope 30 within the medical device. In some embodiments, the inspection data, the analysis data, or the reference data, or any combination of one or more of these, may be displayed simultaneously in the user interface 360, such as in the data display area 364.

[0170] In this example, location indicator 366 is graphically depicted over the corresponding location of the medical device in medical device representation 363. In another example, location indicator 366 is displayed adjacent to the corresponding location of the medical device, similar to current location indicator 368, which in this example has a graphical element (an arrow) adjacent to medical device representation 363 and pointing to the corresponding location.

[0171] In some embodiments, other position indicators 366 are displayed that are selectable by the operator to display data associated with the corresponding positions. For example, when a position indicator 366A-366C corresponding to an inspected position is selected, the data display area 364 may display data corresponding to the selected position, such as images captured during the inspection, reference images, and other inspection and / or analysis data. Similarly, when a position indicator 366E-366I corresponding to an uninspected position is selected, the data display area 364 may display data corresponding to the selected position, such as reference images, as well as other inspection and / or analysis data.

[0172] In this example, user interface 360 ​​includes a data display area 364 that displays inspection and / or analysis data during the inspection. For example, current image 380 is a live video display of an image from the inspection scope 30 camera. In another example, current image 380 is a still image captured at current position 366D, or the last captured image.

[0173] In some embodiments, the data display area 364 includes a reference image display 382. Various possible reference images can be displayed. One example of a reference image is a historical image obtained from a previous inspection of the same medical device. For example, the reference image can be a previous image from an inspection scope inspection taken at the same location 366D. In some embodiments, the data display area 364 can display the date and / or time the reference image was taken. In another possible embodiment, the reference image can be a sample image of the same type of medical device, such as an image from a manufacturer or other data provider. In some embodiments, the reference image shows a normal state of the medical device at the location 366D. In other embodiments, the reference image shows an abnormal state of the medical device at the location 366D. In some embodiments, multiple reference images are available, such as one showing a standard state and one or more abnormal states. The reference image 382 can be displayed to allow a human operator to compare the current image 380 with the reference image 382. Furthermore, as discussed herein, the reference image can also be used by the automated medical device inspection coordinator 16 to automatically determine or predict whether a medical device may have an abnormality at a corresponding location. In another possible embodiment, the reference image may be provided to guide or assist the user in performing or determining the results of an examination of the medical device M.

[0174] In some embodiments, the data display area 364 displays other test data 384. Any available data may be displayed individually or in combination within the data display area 364, including any data discussed herein. In this example, the data display area 364 includes test data 384 such as the current location, a description of the current location, a hotspot identifier (indicating whether the current location is a known hotspot), a prior status, analysis results (e.g., indicating whether an anomaly may exist at the current location 366D), and historical notes about the current location 366D (e.g., past test annotations or abnormal findings from past tests). As noted above, other test, analysis, or reference data may be displayed.

[0175] In some embodiments, the user interface 360 ​​further includes a medical device identification display area that displays identifying information about the medical device being tested. An example of a medical device identification display area 390 is shown and described in further detail with reference to FIG.

[0176] FIG. 7 illustrates another example of a user interface 360 ​​showing a display of medical device test data after a test has been completed. Some aspects of the example user interface 360 ​​are similar to those shown in FIG. 6 and will not be repeated in detail separately herein. Specifically, the example user interface 360 ​​shown in FIG. 7 includes a medical device display area 362, a data display area 364, and a location indicator 366 (including location indicators 366J through 366Q). In addition, the example user interface 360 ​​further shows an example medical device identification display area 390 and a graphical location display 392.

[0177] In some embodiments, user interface 360 ​​includes a medical device identification display area 390 that displays identification information about the medical device. A variety of possible medical device identification information can be displayed. In this example, the identification information includes a device ID, a manufacturer, a model number, and a serial number. The device ID may be an identification assigned by the manufacturer, or, for example, in some embodiments, may be an identifier assigned by a medical facility or asset tracking system that uniquely identifies the medical device and distinguishes it from all other medical devices in medical device inspection system 10 (shown in FIG. 1 ). Medical device identification display area 390 can display one or more identifiers, including any combination of the identifiers described herein.

[0178] This example also illustrates an alternative user interface 360 ​​configuration in which the medical device representation 363 in the medical device display area 362 is separate from the graphical location representation 392. This alternative configuration may also be used in place of the example shown in FIG.

[0179] The exemplary graphical position display 392 includes a linear position indicator with a start point (far left) and an end point (far right). The start and end points may be reversed in other embodiments, and in some embodiments, the test may proceed in either direction.

[0180] The graphical position display 392 includes multiple position indicators 366 along its length that represent points at which test data was captured and stored. In some embodiments, data may be collected continuously along the length of the medical device (or portion thereof), at regular intervals along the length of the medical device (e.g., every cm or every second), at predetermined locations (e.g., at predetermined landmarks such as hotspots), at locations where possible abnormalities are detected, or a combination of one or more of these.

[0181] In some embodiments, the location indicator 366 is selectable to display additional information about the corresponding location of the medical device. For example, when the location indicator 366J is selected, the data display area 364 displays information about the corresponding location. The information may include test data, analysis data, or reference data collected during the most recent test.

[0182] In the illustrated example, the data display area 364 includes an image 400, a reference image 402, and other exam data 404. The image 400 displays images or video from an exam that was performed that was taken at a location corresponding to the location indicator 366J. The date and / or time of the last exam may also be displayed (e.g., October 23, 2022).

[0183] The reference image 402 is similar to the reference image 382 described with reference to FIG.

[0184] Other test data 404 may be displayed, such as medical device location, location description, hotspot identifier, prior status, analysis results, and historical notes. As described above, other test, analysis, or reference data may be displayed. Any available data may be displayed individually or in combination within the data display area 364, including any data discussed herein.

[0185] 6 and 7 illustrate exemplary user interface displays, various modifications can be made to the user interface displays to include more, fewer, or different graphical elements, display areas, images, or data that, in various possible combinations, form further possible embodiments according to the present disclosure. The exemplary user interfaces can be generated and / or displayed on any computing device that is part of medical device inspection system 10 (FIG. 1) or that is connected to or receives data originating from medical device inspection system 10.

[0186] Aspects of the present disclosure may also be described by the following embodiments: Any feature or combination of features disclosed in the following description may also be included in any of the other embodiments disclosed elsewhere in this specification.

[0187] Embodiment 1 is a method for inspecting a medical device, the method including identifying the medical device, inspecting the medical device using an inspection scope, storing inspection data, analyzing the inspection data, generating analysis data based on the analysis, and generating one or more outputs based on the analysis.

[0188] Embodiment 2 is the method of embodiment 1, wherein the test data is any one or more of the test data disclosed herein.

[0189] Embodiment 3 is the method of any of Embodiments 1 and 2, wherein the analytical data is any one or more of the analytical data disclosed herein.

[0190] Embodiment 4 is the method of any one of embodiments 1 to 3, wherein the output is any one or more of the outputs disclosed herein.

[0191] Embodiment 5 is the method of any one of embodiments 1 to 4, wherein the one or more outputs include one or more actions.

[0192] Embodiment 6 is a medical device inspection system comprising an inspection scope including a camera, the inspection scope operable to perform an inspection of a medical device, a position tracker for determining the relative position of the inspection scope with respect to the medical device, the inspection scope and position tracker generating inspection data, and a computing device comprising an inspection analyzer, the inspection analyzer analyzing the inspection data to identify possible abnormalities in the medical device.

[0193] Embodiment 7 is the medical device testing system of embodiment 6, wherein the testing analyzer comprises an anomaly detector.

[0194] Embodiment 8 is the medical device inspection system of any of embodiments 6 and 7, wherein the inspection analyzer includes one or more machine learning neural networks.

[0195] Embodiment 9 is the medical device inspection system of embodiment 8, wherein the machine learning neural network is trained with training data including positive training examples and negative training examples, including images of medical devices without abnormalities and images of medical devices with abnormalities.

[0196] Embodiment 10 is the medical device inspection system of any one of embodiments 6 to 9, wherein the computing device is configured to display a user interface.

[0197] An eleventh embodiment is the medical device inspection system of the tenth embodiment, wherein the user interface includes a reference image of the medical device without anomalies and an inspection image rendered from the inspection data.

[0198] Embodiment 12 is a computer system comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause a server to receive test data capturing an inspection of a medical device with an inspection scope and process the test data using artificial intelligence to determine one or more conditions of the medical device.

[0199] Embodiment 13 is the computer system of embodiment 12, wherein the instructions further cause the server to generate a user interface presenting one or more statuses of the medical device and provide the user interface to a user computing device.

[0200] Example 14 is the computer system of Example 13, wherein the user interface is updated during testing of the medical device when a condition is detected in the test data.

[0201] Embodiment 15 is the computer system of any of embodiments 13 and 14, wherein the user interface is configured to receive input providing annotations of the test data.

[0202] Example 16 is the computer system of Example 15, in which the annotations are used to further train the artificial intelligence.

[0203] Embodiment 17 is a computer system of any of embodiments 12 to 16, wherein the instructions further cause the server to search for historical data corresponding to the medical device, and the determination of one or more conditions of the medical device is further based on the historical data corresponding to the medical device.

[0204] Embodiment 18 is a computer system of any of embodiments 12 to 17, wherein the server is configured to interact with a manufacturer system associated with a manufacturer of the medical device and provide one or more statuses of the medical device to the manufacturer.

[0205] Embodiment 19 is a computer system of any of embodiments 12 to 18, wherein the server is configured to interact with a health management system to provide one or more statuses of the medical device.

[0206] Embodiment 20 is the computer system of embodiment 19, wherein the health management system is configured to automatically take an action related to the medical device based on receiving one or more statuses of the medical device.

[0207] The various embodiments described above are provided by way of example only and should not be construed as limiting the scope of the claims appended hereto. Those skilled in the art will readily recognize various modifications and changes that may be made without following the exemplary embodiments and applications shown and described herein and without departing from the full scope of the following claims.

Claims

1. 1. A method for inspecting a medical device, comprising: identifying the medical device; inspecting the medical device with an inspection scope to generate inspection data; analyzing the inspection data using a machine learning model; generating analysis data based on an analysis of the inspection data; generating one or more outputs based on the analytical data; A method for inspecting a medical device, comprising:

2. The inspection data is (a) image data; (b) video data; (c) study metadata; (d) Processing data documenting the inspection system process; or (e) any combination of (a), (b), (c) and (d); The method of claim 1 , comprising any one or more of:

3. The method of claim 1 , wherein the analytical data includes a prediction of whether the medical device may have an abnormality.

4. The method of claim 1 , wherein the analytical data includes a confidence score associated with a probability that the medical device may have an abnormality.

5. generating a user interface, the user interface comprising: (a) The area of ​​the equipment to be inspected; (b) known hotspots according to the type of device; (c) the history of the device; (d) a reference image; (e) historical test data associated with the device; (f) Instructions for Use (IFU) for the device; (g) the analytical data; (g) one or more images from within the medical device taken during the examination; or (h) any combination of (a), (b), (c), (d), (e), (f) and (g); The method of claim 1 , comprising any one or more of:

6. The analytical data is (i) a prediction that the medical device may be malfunctioning; (j) a prediction that the medical device may be normal; (k) An indication that no abnormalities were detected; (l) An indication that a possible anomaly has been detected; or (m) any combination of (i) to (m); 6. The method of claim 5, comprising any one or more of:

7. The method of claim 1 , wherein the one or more outputs include one or more suggested actions.

8. The one or more proposed actions may include: (i) No further action is required; (ii) re-cleaning; (iii) Contact the device manufacturer; (iv) Send it in for repair; (v) replacing the equipment; (vi) preparing it for use in a patient; (vii) Not for use in patients; (viii) Quarantine until further notice; (ix) the medical device has reached the end of its life; or (x) any combination of (i) to (ix); 8. The method of claim 7, comprising any one or more of:

9. A medical device inspection system, comprising: an inspection scope including a camera, the inspection scope performing an inspection of a medical device to capture inspection data; a computing device including a test analyzer, the test analyzer analyzing the test data to identify possible abnormalities in the medical device; and A medical device inspection system comprising:

10. 10. The medical device inspection system of claim 9, further comprising a position tracker for determining a relative position of the inspection scope with respect to the medical device, the inspection data including data from the position tracker.

11. The medical device inspection system of claim 10 , wherein at least a portion of the data from the position tracker is collected manually.

12. The medical device inspection system of claim 10 , wherein the position tracker operates automatically in cooperation with an advancement system.

13. 10. The medical device testing system of claim 9, wherein the test analyzer further comprises an anomaly detector that automatically identifies possible anomalies in the medical device.

14. The medical device inspection system of claim 13 , wherein the anomaly detector automatically detects anomalies in the medical device by processing the inspection data.

15. 14. The medical device testing system of claim 13, wherein the anomaly detector automatically detects anomalies in the medical device by processing the test data, and the test analyzer accepts user input to update the anomaly determination.

16. The medical device inspection system of claim 15 , wherein the user input overrides automatic anomaly detection by the anomaly detector.

17. The medical device inspection system of claim 15 , wherein a user manually identifies anomalies in the medical device that are not detected by the anomaly detector.

18. 10. The medical device testing system of claim 9, wherein the test analyzer comprises one or more machine learning neural networks.

19. 20. The medical device inspection system of claim 18, wherein at least one of the one or more machine learning neural networks is trained using training data including positive and negative training examples that include images of medical devices with and without abnormalities.

20. The medical device inspection system of claim 9 , wherein the computing device presents a user interface.

21. 21. The medical device inspection system of claim 20, wherein the user interface includes a reference image of the medical device without anomalies and an inspection image rendered from the inspection data.

22. 1. A computer system comprising: at least one processor; at least one memory, which when executed by at least one of the processors, provides the computer system with: receiving inspection data documenting an inspection of the medical device with the inspection scope; processing the test data to automatically determine one or more conditions of the medical device; at least one said memory storing instructions; A computer system comprising:

23. 23. The computer system of claim 22, wherein the instructions causing the computer system to process the test data further cause the computer system to output a prediction of whether the medical device may have one or more abnormalities.

24. The instructions further include: generating a user interface that presents the predictions; causing a user computing device to provide the user interface; 23. The computer system of claim 22.

25. 25. The computer system of claim 24, wherein the user interface is updated when a condition is detected in the test data during the test of the medical device.

26. 23. The computer system of claim 22, wherein one or more databases are updated when a condition is detected in the test data during the test of the medical device.

27. 23. The computer system of claim 22, wherein the user interface is configured to receive input providing annotations for the inspection data.

28. 28. The computer system of claim 27, wherein the annotations are used to further train a machine learning model used to generate the predictions.

29. The instructions further include: retrieving historical data corresponding to the medical device, wherein determining the prediction is further based on the historical data corresponding to the medical device.

23. The computer system of claim 22.

30. 30. The computer system of claim 29, wherein the prediction is further determined based on a location of the medical device corresponding to a location where the examination data was acquired.

31. The computer system includes: (a) a database system; (b) the manufacturer's system; (c) third-party repair systems; (d) the U.S. Food and Drug Administration (FDA) system; (e) a globally unique device identification database; (f) a manager system; (g) Hospital systems; (h) Electronic medical record systems; (i) a health care system; or (j) any combination of (a), (b), (c), (d), (e), (f), (g), (h), or (i); 23. The computer system of claim 22, configured to interact with any one or more of:

32. 23. The computer system of claim 22, wherein the computer system is configured to interact with a health care system to provide a prediction.

33. 33. The computer system of claim 32, wherein the health management system is configured to automatically take an action related to the medical device based on receiving the prediction.

34. 1. A method for inspecting a medical device, comprising: identifying the medical device; retrieving medical device data for the identified medical device; inspecting the medical device with an inspection scope to generate inspection data; analyzing the test data; generating analytical data based on an analysis of the inspection data; generating one or more outputs based on the analytical data; A method comprising:

35. 35. The method of claim 34, wherein analyzing the test data includes comparing the test data to the medical device data.

36. 36. The method of claim 35, further comprising determining that the medical device may have an abnormality based at least in part on the comparison of the test data and the medical device data.

37. 35. The method of claim 34, wherein one or more of the outputs include at least some of the retrieved medical device data.

38. 35. The method of claim 34, wherein the one or more outputs include at least one image of the medical device taken during the examination and at least one representative image of the medical device or another related medical device from the retrieved medical data for comparison.

39. 39. The method of claim 38, wherein the at least one representative image is at least one historical image of the medical device taken during a previous said examination.

40. 35. The method of claim 34, wherein the retrieved medical device data is examination support information.

41. The examination support information is (a) one or more historical images of the medical device; (b) one or more of said previous analytical data from a previous test; (c) one or more landmarks for the medical device; (d) at least some instructions for use (IFU) for use of said medical device; (e) one or more reference images; and (f) a combination of (a) to (e); 36. The method of claim 35, comprising any one or more of:

42. 1. A method for inspecting a medical device, comprising: positioning an inspection scope relative to the medical instrument; collecting examination data including at least one image taken by the examination scope of the medical device; generating a user interface; Including, The user interface includes: a graphical representation of at least a portion of the medical device; a position indicator representing the corresponding position of the medical device where the image was taken; A method comprising:

43. moreover, determining a position of the inspection scope when the inspection scope is positioned within the medical device; positioning the position indicator at a location within the user interface based on the determined position of the inspection scope; 43. The method of claim 42, comprising:

44. 44. The method of claim 43, wherein determining the position of the inspection scope is performed using a position tracker.

45. 43. The method of claim 42, further comprising displaying the user interface.

46. 43. The method of claim 42, further comprising transmitting the user interface for display by a computing device.

47. 43. The method of claim 42, wherein the location indicator is graphically displayed over a corresponding location of the graphical representation of at least a portion of the medical device.

48. 43. The method of claim 42, wherein the location indicator is graphically displayed adjacent to a corresponding location on the graphical representation of at least a portion of the medical device.

49. 43. The method of claim 42, wherein the user interface further comprises a linear position display separate from the graphical representation of at least a portion of the medical device, and the position indicator indicates a corresponding position of the medical device on top of the linear position display.

50. The user interface may further include: displaying at least one of the images from within the medical device; and a reference image for the medical device at the corresponding position; 43. The method of claim 42, comprising:

51. The user interface includes: (a) displaying at least one of said images from inside said medical device; (b) a reference image for the medical device at the corresponding location; (c) the corresponding location; (d) a description of the corresponding location; (e) a hotspot identifier; (f) previous circumstances; (g) analysis results; (h) Historical notes, or (i) any combination of (a) to (h); 43. The method of claim 42, further comprising one or more display devices of:

52. 1. A method for generating a user interface, the method comprising: using a computing device to acquire inspection data related to an inspection of a medical device with an inspection scope, the inspection data including at least one image of an interior of the medical device and a corresponding location at which the at least one image was taken; generating a user interface associated with testing the medical device, the user interface comprising: a graphical representation of at least a portion of the medical device; a position indicator representing a corresponding position of the medical device where at least one of the images was captured; A method comprising:

53. 1. A computing device, comprising: at least one processing unit; at least one computer-readable storage device that, when executed by at least one of said processing units, causes said computing device to: acquiring inspection data related to an inspection of a medical device with an inspection scope, the inspection data including at least one image of an interior of the medical device and a corresponding location at which the at least one image was taken; generating a user interface associated with testing the medical device, the user interface comprising: a graphical representation of at least a portion of the medical device; a location indicator representing a corresponding location of the medical device where at least one of the images was captured; at least one computer-readable storage device storing data of instructions for A computing device comprising:

54. When executed by at least one processing unit of at least one computing device, At least one of the computing devices acquiring inspection data related to an inspection of a medical device with an inspection scope, the inspection data including at least one image of an interior of the medical device and a corresponding location at which the at least one image was taken; generating a user interface associated with testing the medical device, the user interface comprising: a graphical representation of at least a portion of the medical device; a location indicator representing a corresponding location of the medical device where at least one of the images was captured; A computer-readable storage device for storing data of instructions.

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