System and method for real-time identification and display of similar historical medical images

Abstract

The present disclosure relates to a method for identifying and displaying historical classified images. The method can be applied to a mobile device or other computing device. The computing device may display, on a user interface, at least one image of patient anatomy viewed during a medical procedure. The computing device may further receive an indication of an abnormality that is visible within the at least one image of the patient's anatomy viewed during the medical procedure. The computing device may similarly analyze the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the abnormality. The computing device may display, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

Description

Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020SYSTEM AND METHOD FOR REAL-TIME IDENTIFICATION AND DISPLAY OF SIMILAR HISTORICAL MEDICAL IMAGESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of United States provisional application no.63 / 751,116, filed 29 January 2025, which is hereby incorporated by reference as though fully set forth herein.FIELD OF THE INVENTION

[0002] The present disclosure relates to the field of medical imaging and diagnostic assistance systems. More specifically, it pertains to a system and method for assisting healthcare professionals in the real-time identification and classification of anatomical structures, such as polyps, during medical procedures like colonoscopies. The system utilizes a database of historically classified images to provide comparative visual references to the operator, thereby facilitating the accurate and efficient diagnosis of potential abnormalities within the colon.BACKGROUND OF THE INVENTION

[0003] In the field of medicine, particularly in the performance of routine medical screening examinations such as colonoscopies and esophagogastroduodenoscopies, there is a clinical need to aid in analyzing patient anatomy and abnormalities. For example, colonoscopy is a widely used medical procedure for examining the interior lining of the colon and rectum to detect abnormalities such as polyps, which are growths that may develop into cancer if left untreated. The early detection and classification of polyps are paramount in preventing colorectal cancer, one of the leading causes of cancer-related deaths worldwide. Colonoscopists, also known as gastroenterologists, are medical professionals who specialize in performing colonoscopies and these professionals dedicate significant time to studying images of polyps and other anatomical features that have already been visually classified by expert visual analysis or even confirmation of diagnosis via pathology testing (e.g., biopsy).DYKEMA GOSSETT PLLC PAGE 1 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020

[0004] During a colonoscopy, a colonoscopist will carefully navigate a colonoscope through the entire length of the large intestine, looking for abnormalities such as polyps, inflammation, or signs of disease. Identifying polyps, for example, can be challenging due to several factors, including the fact that the colon lining is often wrinkled and has folds, making it easy for small polyps to hide. Additionally, some polyps may blend in with the surrounding tissue, appearing similar in color and texture. The endoscopist relies on careful examination, skill, and experience to detect these subtle abnormalities. Moreover, polyps can vary in size, shape, and location within the colon, further complicating their identification. Despite these difficulties, early detection remains crucial for preventing potential malignancy, and thorough colonoscopies play a vital role in achieving this goal.

[0005] Even after a polyp is identified during a colonoscopy, physicians may encounter challenges in classifying it as cancerous or non-cancerous. The task of determining how to classify a particular polyp is challenging because some polyps appear visually similar to normal tissue, and the presence of cancerous cells within a polyp may be evidenced only by very subtle distinguishing visual characteristics. Under existing techniques, colonoscopists rely on their own expertise with little or no real-time assistance with classifying a polyp.

[0006] There is, therefore, a need for a system that can provide real-time assistance to colonoscopists by offering visual comparisons with previously classified polyps. Such a system would enhance the accuracy of polyp classification, potentially leading to better patient outcomes through more precise and timely interventions.SUMMARY

[0007] Examples of the present disclosure provide a system and method for identifying and displaying historical classified images.

[0008] According to a first aspect of the present disclosure, a computer-implemented method for interactive visualization of colonoscopy procedural statistics is provided. The method may be implemented on a mobile device, a desktop computer, or another computing device. The method may include displaying, on a user interface, at least one image of patient anatomy viewed during a medical procedure. The method may also include receiving an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medicalDYKEMA GOSSETT PLLC PAGE 2 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020procedure. The method may further include analyzing the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the abnormality. The method may also include displaying, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

[0009] According to a second aspect of the present disclosure, a computing device, such as a server, is provided. The computing device may include one or more processors, a non-transitory computer-readable memory storing instructions executable by the one or more processors. The one or more processors may be configured to display, on a user interface, at least one image of patient anatomy viewed during a medical procedure. The one or more processors may further be configured to receive an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medical procedure. The one or more processors may also be configured to analyze the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the abnormality. The one or more processors may further be configured to display, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

[0010] According to a third aspect of the present disclosure, a non-transitory computer-readable storage medium having stored therein instructions is provided. When the instructions are executed by one or more processors of the apparatus, the instructions may cause the apparatus to perform displaying, on a user interface, at least one image of patient anatomy viewed during a medical procedure. The instructions may also cause the apparatus to receiving an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medical procedure. The instructions may also cause the apparatus to analyzing the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that matches the abnormality. The instructions may also cause the apparatus to display, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.DYKEMA GOSSETT PLLC PAGE 3 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020

[0011] These and other aspects and advantages will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference to the accompanying drawings. Further, it should be understood that the foregoing summary is merely illustrative and is not intended to limit in any manner the scope or range of equivalents to which the appended claims are lawfully entitled.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate examples consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

[0013] FIG. l is a user interface displaying a colonoscopy procedure, according to an example of the present disclosure.

[0014] FIG. 2 is a user interface displaying a colonoscopy procedure and similar historical image, according to an example of the present disclosure.

[0015] FIG. 3 is a flow chart illustrating a method for identifying and displaying historical classified images, according to an example of the present disclosure.

[0016] FIG. 4 is a flow chart illustrating a method for identifying and displaying historical classified images, according to an example of the present disclosure.

[0017] FIG. 5 is a block diagram of an endoscopic system, according to an example of the present disclosure.DETAILED DESCRIPTION

[0018] Although the present invention is capable of being embodied in various forms, for simplicity and illustrative purposes, the principles of the invention are described by referring to certain embodiments thereof. It is understood, however, that the present disclosure is to be considered as an exemplification of the claimed subject matter and is not intended to limit the appended claims to the specific embodiments illustrated. It will be apparent to one of ordinary skill in the art that the invention may be practiced without limitation to these specific details. In other instances, well-known methods and structures have not been described in detail so as not to unnecessarily obscure the invention. For example, while embodiments herein are described withDYKEMA GOSSETT PLLC PAGE 4 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020reference to a colon, it is understood that the system and methods may be implemented similarly with respect to the gastrointestinal (GI) track more generally or to other aspects of a patient’s anatomy.

[0019] The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to limit the present disclosure. As used in the present disclosure and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It shall also be understood that the terms “and” and “or” used herein is intended to signify and include any or all possible combinations of one or more of the associated listed items. As used herein, the term “if’ may be understood to mean “when” or “upon” or “in response to a judgment” depending on the context.

[0020] The present disclosure relates to an interactive procedural statistics user interface. In one or more embodiments, the interactive procedural statistics user interface provides a graphical representation of procedural statistics and dynamically updates a graphical representation of a specific patient anatomy (e.g., a colon, a lung, etc.) with user selected criteria. The interactive procedural statistics user interface can be used to aid in the review of key procedural statistics to ensure high-quality outcomes in medical screening examinations and associated treatments, such as colonoscopies, without requiring manual review of multiple different sources of information. For example, the system may be used to identify and diagnose polyps identified in a colonoscopy.

[0021] Polyps in the colon are abnormal growths that form on the inner lining of the colon and can vary in size and shape. There are several types, including hyperplastic polyps, which are generally benign; adenomatous polyps, which have the potential to become cancerous; and inflammatory polyps, often associated with inflammatory bowel disease. Visually, polyps can appear as small, flat bumps or raised mushroom-like structures, with adenomatous polyps often appearing larger and more irregular in shape, while hyperplastic polyps tend to be smaller and smoother.

[0022] In one or more embodiments, the system provides a real-time identification and display interface for visually similar historical medical images during medical procedures such as colonoscopies. The system is configured to analyze medical images (e.g., still images orDYKEMA GOSSETT PLLC PAGE 5 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020videos) to identify and display historically classified images of structures of interest having similarity to a structure currently being viewed. The system, for example, utilizes an anonymized dataset of historically classified images and employs a polyp similarity algorithm or abnormality similarity algorithm to analyze and compare the current image against this dataset. The algorithm can take advantage of the visual characteristics of polyps to match the identified polyp to the dataset. Additionally, algorithms can determine polyp characteristics during a procedure, including, a Narrow-band imaging International Colorectal Endoscopic (NICE) score, a polyp size, location within the colon, a macroscopic aspect, a KUDO classification, and a Paris classification.

[0023] The NICE score classifies polyps into three types based on their vascular and surface features to help distinguish between benign, adenomatous, and malignant lesions. The KUDO classification system assesses polyps using magnified pit patterns, ranging from benign types (I and II) to potentially malignant types (III-V), while the Paris classification categorizes polyps by shape, such as flat, sessile, or pedunculated, with flat lesions more likely to be cancerous.Additionally, the location and macroscopic aspect, such as size and color, are important factors in evaluating the risk and nature of the polyps found during endoscopy.

[0024] In one or more embodiments, the user interface includes a selectable “View Similar Polyps” button that, when activated, triggers the system to analyze the currently displayed polyp or structure against the anonymized dataset. The system then exposes a set of historical images determined to be similar based on computer-generated similarity scores. These similar images are displayed alongside the current image, along with associated pathology information such as biopsy results, diagnoses, or treatments performed on the historically similar structures.

[0025] Thus, the system offers several key advantages over existing technology. Firstly, it provides real-time assistance to colonoscopists in classifying polyps or other structures of interest during a procedure, potentially improving diagnostic accuracy and decision-making. Secondly, by presenting visually similar historical cases with known outcomes, it leverages a vast repository of medical knowledge to aid in current diagnoses. Thirdly, the system can operate at various stages including pre-procedural, intra-procedural, or post-procedural, offering flexibility in its application. These are a few sample advantages that the system offers.

[0026] Furthermore, the system allows for customization of the similarity criteria, such asDYKEMA GOSSETT PLLC PAGE 6 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020displaying a predetermined number of images with the highest similarity scores or all images above a certain similarity threshold. It can also incorporate filtering based on factors like country or region, enhancing its relevance to specific clinical contexts.

[0027] In an embodiment, the similarity score can be compared to a predetermined score that is designed to identify when a classified image is similar enough to the current visual characteristics being analyzed. For example, the predetermined score includes a high quality score, a medium score, and a low score for the number of visual characteristics of the classified image that match the current image. A low score can be less than 50% of matching characteristics, a medium score can be between 50% and 75% of matching characteristics, and a high quality score can be over 75% of matching characteristics.

[0028] Turning now to the figures, FIGS. 1 and 2 show a user interface 100, 200 according to multiple embodiments of the present disclosure. The user interface 100, 200 includes a polyp information section 122, a view similar polyps button 124, a viewer 130, a detected polyp section 132, and a polyp analysis information section 134. The polyp information section 122 displays information about the currently detected polyp on the viewer 130. Polyp information includes size, a NICE score, location, macroscopic aspect, KUDO classification, and Paris classification. The viewer 130 displays the camera stream or images from a colonoscopy scan, including detected polyps. The viewer 130 includes the detected polyp section 132 that frames any polyps detected for the user to view and the polyp analysis information section 134 where information about the polyp is provided to the user for reviewing. The information in the polyp analysis information section 134 includes information in the polyp information section 122.

[0029] FIG. 1 shows the user interface 100, including a colonoscopy scan / image in the viewer 130 with a detected polyp in the detected polyp section 132 (i.e., the lower dashed box overlying the colon image). Also overlying the colonoscopy scan / image is the polyp analysis information section 134. Polyp information is provided in the polyp information section 122.

[0030] FIG. 2 shows the user interface 200, including the colonoscopy scan / image in the viewer 130, the detected polyp in the detected polyp section 132, polyp information in the polyp information section 122, the polyp analysis information section 134, and a similar polyp window 240. The similar polyp window 240 includes a polyp image 242 and reference polyp information 244. The polyp image 242 is the image of the similar polyp detected from the anonymizedDYKEMA GOSSETT PLLC PAGE 7 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020dataset. The reference polyp information 244 includes information about the similar polyp depicted in the polyp image 242. This information also includes pathology information like a diagnosis of the polyp. For example, the similar polyp being show in the similar polyp window 240 can have a diagnosis of “benign tubular adenoma and no evidence of dysplasia” that is stored in the dataset and shown to a user when the window is selected.

[0031] FIG. 3 shows an example method 300 for identifying and displaying historical classified images in accordance with the present disclosure. The method can be applied to a mobile device or other computing device.

[0032] In step 310, the computing device displays at least one image of patient anatomy viewed during a medical procedure. The at least one image can be a video of a colonoscopy or a single image of a colon.

[0033] In step 312, the computing device receiving an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medical procedure. The abnormality can be, for example, a polyp detected in a colonoscopy.

[0034] In step 314, the computing device analyzing the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the abnormality. The similar classified image may include an abnormality that is similar to the detected abnormality.

[0035] In step 316, the computing device displays, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

[0036] FIG. 4 shows an example method for identifying and displaying historical classified images in accordance with the present disclosure. The method can be applied to a mobile device or other computing device.

[0037] In step 410, the computing device displays, on a user interface, images of a patient’s anatomy viewed during a colonoscopy procedure.

[0038] In step 412, the computing device receives an indication of a polyp that is visible within at least one image of the patient’s anatomy during the colonoscopy procedure.

[0039] In step 414, the computing device receives a user input that includes a view similarDYKEMA GOSSETT PLLC PAGE 8 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020polyp selection.

[0040] In step 416, the computing device receives visual characteristics of the polyp.

[0041] In step 418, the computing device compares the visual characteristics of the polyp against a dataset of historically classified images to generate a score.

[0042] In step 420, the computing device identifies, within a classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the polyp based on the score.

[0043] In step 422, the computing device displays, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

[0044] FIG. 5 shows an interactive endoscopic procedure analysis and visualization system 510 with an endoscopic system 520, a network 540, a medical records database 560, and a computing system 580. According to example embodiments shown schematically in FIG. 5, the endoscopic system 520 includes an endoscope 522, processor 524, memory 526, I / O interface 528, and display 530. The computing system 580 includes a processor 582, memory 584, I / O interface 586, and display 588. The endoscopic system 520 can communicate with the computing system 580 and the medical records database 560 through the network 540.

[0045] The endoscopic system 520 functions as an integrated unit for performing endoscopic procedures. The endoscope 522 is the primary instrument used to examine the patient, capturing visual data and potentially other sensor information from inside the body. This data is then processed by the processor 524, which analyzes the incoming information and generates preliminary findings. Memory 526 stores the captured endoscopic images, video footage, and sensor data from the current procedure. It may also temporarily store patient information, procedure protocols, and system software necessary for the immediate operation of the endoscopic system. This local storage in the memory 526 ensures quick access to critical data during the procedure and allows for initial processing and analysis to occur within the endoscopic system itself.

[0046] The I / O interface 528 allows the medical professional to input additional information, control the endoscope, and interact with the system’s software to, for example, collect or gather polyp characteristics such as visual information and polyp location in the colon. Display 530DYKEMA GOSSETT PLLC PAGE 9 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020presents the live endoscopic imagery, as well as medical information or other relevant information, to the medical professional in real-time. This integrated approach allows for seamless data collection and analysis, location labeling, polyp information analysis and determination, and documentation throughout the endoscopic procedure.

[0047] The computing system 580 serves as a central processing and analysis hub for performing and documenting endoscopic procedures. It includes a processor 582, which can run various detection algorithms and machine learning models and algorithms during the procedure. Memory 584 stores patient data, clinical guidelines, procedure statistics, polyp information, and location information.

[0048] The I / O interface 586 facilitates communication with the endoscopic system 520 and the medical records database 560 through the network 540. This allows the computing system 580 to receive real-time data from the endoscope 522 and access relevant patient history and medical records. Display 588 provides a user interface for medical professionals to interact with the system and review and document procedure statistics, including the current location of the endoscope within the patient’s colon and the detected polyp and similar polyps previously identified.

[0049] The endoscopic system 520 can transmit procedure data, images, and preliminary findings to the computing system 580 via the network 540. Simultaneously, the computing system 580 can retrieve or record relevant patient information, polyp information, images, and diagnosis, and procedure statistics from the medical records database 560.

[0050] The processors 524 and 582 can typically control the overall operations of the endoscopic system 520 and computing system 580, such as the operations associated with data acquisition, data processing, and data communications. Processors 524 and 582 can include one or more processors to execute instructions to perform all or some of the steps in the abovedescribed methods. Moreover, processors 524 and 582 can include one or more modules that facilitate the interaction between processors 524, 582 and other components. The processor may be or include a central processing unit (CPU), a microprocessor, a single chip machine, a graphical processing unit (GPU), a System on a Chip (SoC), Tensor Processing Unit (TPU), Quantum Processor, Vision Processing Unit (VPU) or the like.

[0051] Memory 526 and 584 can store various types of data to support the operation of theDYKEMA GOSSETT PLLC PAGE 10 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020endoscopic system 520 and computing system 580. Memory 526 and 584 can include predetermined software. Examples of such data comprise instructions for any applications or methods operated on the endoscopic system 520 and computing system 580. The memory 526, 584 may be implemented by using any type of volatile or non-volatile memory devices, or a combination thereof.

[0052] In some embodiments, there is also provided a non-transitory computer-readable storage medium comprising a plurality of programs, such as those comprised in the memory 526, 584, executable by the processors 524, 582, for performing the above-described methods. For example, the non-transitory computer-readable storage medium may be a ROM, a RAM, or the like.

[0053] The non-transitory computer-readable storage medium has stored therein a plurality of programs for execution by a computing device having one or more processors, where the plurality of programs for execution by a computing device having one or more processors, where the plurality of programs, when executed by the one or more processors, cause the computing device to perform the above-described method for motion prediction.

[0054] Multiple embodiments are described herein, including the best mode known to the inventors for practicing the claimed invention. Of these, variations of the disclosed embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing disclosure. The inventors expect skilled artisans to employ such variations as appropriate (e.g., altering or combining features or embodiments), and the inventors intend for the invention to be practiced otherwise than as specifically described herein. In addition, while the invention has been described in terms of several preferred embodiments, it should be understood that there are many alterations, permutations, and equivalents that fall within the scope of this invention. It should also be noted that there are alternative ways of implementing both the process and apparatus of the present invention. For example, steps do not necessarily need to occur in the order shown in the accompanying figures and may be rearranged as appropriate. It is therefore intended that the appended claim includes all such alterations, permutations, and equivalents as fall within the true spirit and scope of the present invention.DYKEMA GOSSETT PLLC PAGE 11 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700

Claims

Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020CLAIMSWhat is claimed is:

1. A method for identifying and displaying historical classified images comprising: displaying, on a user interface, at least one image of patient anatomy viewed during a medical procedure;receiving an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medical procedure;analyzing the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the abnormality; anddisplaying, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

2. The method of claim 1, wherein the medical procedure comprises a colonoscopy procedure and the abnormality comprises a polyp.

3. The method of claim 1, further comprising:receiving a user input comprising a view similar abnormality selection.

4. The method of claim 1, wherein analyzing the at least one image with respect to a classified image database further comprises:DYKEMA GOSSETT PLLC PAGE 12 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020determining visual characteristics of the abnormality;comparing the visual characteristics of the abnormality against a dataset of historically classified images;generating a similarity score for each comparison; andestablishing that the similar classified image matches the abnormality based on the similarity score.

5. The method of claim 4, wherein analyzing the at least one image with respect to a classified image database further comprises:analyzing the similar classified image to identify a predetermined number of images that match the abnormality based on predetermined score.

6. The method of claim 4, wherein the predetermined score comprises a high quality score.

7. The method of claim 1, further comprising:displaying associated pathology information of the similar classified image.

8. The method of claim 7, wherein the associated pathology information comprises biopsy results, diagnosis, or treatment performed.DYKEMA GOSSETT PLLC PAGE 13 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-2010209. The method of claim 1, wherein determining a similar classified image matches the abnormality comprises using an abnormality similarity algorithm to determine visual similarity.

10. The method of claim 1, wherein displaying the medical procedure comprises: displaying the medical procedure in real-time.

11. The method of claim 1, wherein displaying the similar classified image comprises: displaying the similar classified image during a pre-procedural, intra-procedural, or postprocedural stage.

12. A computing device comprising:one or more processors; anda non-transitory computer-readable storage medium storing instructions executable by the one or more processors, wherein the one or more processors are configured to:display, on a user interface, at least one image of patient anatomy viewed during a medical procedure;receive an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medical procedure;analyze the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormality having visual characteristics that match the abnormality; andDYKEMA GOSSETT PLLC PAGE 14 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020display, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

13. The computing device of claim 12, wherein the one or more processors are further configured to:receive a user input comprising a view similar abnormality selection.

14. The computing device of claim 12, wherein the one or more processors configured to analyze the at least one image with respect to a classified image database are further configured to:determine visual characteristics of the abnormality;compare the visual characteristics of the abnormality against a dataset of historically classified images;generate a similarity score for each comparison; andestablish that the similar classified image matches the abnormality based on the similarity score.

15. The computing device of claim 14, wherein the one or more processors configured to analyze the at least one image with respect to a classified image database are further configured to:analyze the similar classified image to identify a predetermined number of images that match the abnormality based on predetermined score.DYKEMA GOSSETT PLLC PAGE 15 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-20102016. The computing device of claim 14, wherein the predetermined score comprises a high quality score.

17. The computing device of claim 12, wherein the one or more processors are further configured to:display associated pathology information of the similar classified image, wherein the associated pathology information comprises biopsy results, diagnosis, or treatment performed.

18. The computing device of claim 12, wherein determining a similar classified image matches the abnormality comprises using an abnormality similarity algorithm to determine visual similarity.

19. Anon-transitory computer-readable storage medium storing a plurality of programs for execution by a computing device having one or more processors, wherein the plurality of programs, when executed by the one or more processors, cause the computing device to perform:displaying, on a user interface, at least one image of patient anatomy viewed during a medical procedure;receiving an indication of an abnormality that is visible within the at least one image of the patient’s anatomy viewed during the medical procedure;analyzing the at least one image with respect to a classified image database to identify, within the classified image database, a similar classified image depicting a classified abnormalityDYKEMA GOSSETT PLLC PAGE 16 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700Att. Dock. No. GAP24091-DUNV-WO1 | 067655-201020having visual characteristics that match the abnormality; anddisplaying, on the user interface in association with the abnormality, the similar classified image and classification data corresponding to the classified abnormality.

20. The non-transitory computer-readable storage medium of claim 19, wherein the plurality of programs further cause the computing device to perform:determining visual characteristics of the abnormality;comparing the visual characteristics of the abnormality against a dataset of historically classified images;generating a similarity score for each comparison; anddetermining the similar classified image matches the abnormality based on the similarity score.DYKEMA GOSSETT PLLC PAGE 17 of 18 10 South Wacker DriveChicago, Illinois 60606Telephone: (312) 876-1700

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