# US 12,593,972 B2 · Integrated analysis of multiple spectral information for ophthalmology applications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,593,972 B2 - Document ID: US12593972B2 - Status: Granted - Kind: B2 - Application number: 18/358,881 - Filed: 2023-07-25 - Published: 2024-02-01 - Granted: 2026-04-07 - Earliest claimed: 2022-08-01 - Assignees: Alcon Inc. - Applicants: Alcon Inc. - Inventors: Vignesh Suresh; Shruti Siva Kumar; Lu Yin; Ramesh Sarangapani - Web page: https://patentgrep.com/patent/US12593972B2 ## CPC - A61B 3/0025: {characterised by electronic signal processing, e.g. eye models} - A61B 3/14: Arrangements specially adapted for eye photography - G16H 50/30: for calculating health indices; for individual health risk assessment - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract In certain embodiments, an ophthalmic system and computer-implemented method for analyzing multiple spectral information to generate ophthalmic information are described. In an exemplary ophthalmic system, multiple spectral information associated with an eye of a patient is captured via an imaging system. A first set of information and a second set of information are extracted from the multiple spectral information. A visualization of the multiple spectral information is generated using the first set of information. The first set of information and the second set of information are evaluated using different deep learning models to generate ophthalmic information. The ophthalmic information is sent to a user for diagnostic evaluation. ## Claims ### Claim 1 An ophthalmic system comprising: an imaging system adapted to capture multiple spectral information associated with an eye of a patient; a display; a memory comprising executable instructions; and a processor in data communication with the memory and configured to execute the executable instructions to: extract a first set of information from the multiple spectral information and a second set of information from the multiple spectral information, wherein the first set of information comprises spectral information and the second set of information comprises spatial information; generate a set of ophthalmic information associated with the eye of the patient, based on evaluating the first set of information with a first deep learning model and the second set of information with a second deep learning model; generate a visualization of the multiple spectral information based on evaluating the first set of information with a third deep learning model; and present an indication of the set of ophthalmic information to a user and the visualization to the user via the display. ### Claim 2 (depends on 1) The ophthalmic system of claim 1, wherein the multiple spectral information comprises multispectral imaging (MSI) data. ### Claim 3 (depends on 1) The ophthalmic system of claim 1, wherein the multiple spectral information comprises hyperspectral imaging (HSI) data. ### Claim 4 (depends on 1) The ophthalmic system of claim 1, wherein the processor is configured to execute the executable instructions to extract the second set of information by performing a dimensionality reduction operation on the multiple spectral information. ### Claim 5 (depends on 1) The ophthalmic system of claim 1, wherein: the multiple spectral information comprises a plurality of images, each image associated with a different spectral band; and the visualization comprises a single image comprising a plurality of features from one or more of the plurality of images. ### Claim 6 (depends on 1) The ophthalmic system of claim 1, wherein: the multiple spectral information comprises a plurality of images, each image associated with a different spectral band; and the processor is configured to execute the executable instructions to generate the set of ophthalmic information by (i) extracting, via the first deep learning model, a first set of features from one or more regions of one or more of the plurality of images and (ii) extracting, via the second deep learning model, a second set of features from the spatial information. ### Claim 7 (depends on 6) The ophthalmic system of claim 6, wherein: the first set of features comprises a set of local features; each of the one or more regions comprises a single pixel from one of the plurality of images; and the second set of features comprises a set of global features. ### Claim 8 (depends on 6) The ophthalmic system of claim 6, wherein the set of ophthalmic information comprises a prediction of a condition associated with the eye of the patient. ### Claim 9 (depends on 8) The ophthalmic system of claim 8, wherein the prediction comprises (i) an ophthalmic image comprising one or more segmented regions associated with the condition and (ii) a score indicating a severity of the condition. ### Claim 10 A computer-implemented method comprising: receiving multiple spectral information associated with an eye of a patient; extracting a first set of information from the multiple spectral information and a second set of information from the multiple spectral information, wherein the first set of information comprises spectral information and the second set of information comprises spatial information; generating a set of ophthalmic information associated with the eye of the patient, based on evaluating the first set of information with a first deep learning model and the second set of information with a second deep learning model; generating a visualization of the multiple spectral information based on evaluating the first set of information with a third deep learning model; and presenting an indication of the set of ophthalmic information to a user and the visualization to the user via a display. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the multiple spectral information comprises multispectral imaging (MSI) data. ### Claim 12 (depends on 10) The computer-implemented method of claim 10, wherein the multiple spectral information comprises hyperspectral imaging (HSI) data. ### Claim 13 (depends on 10) The computer-implemented method of claim 10, wherein extracting the second set of information comprises performing a dimensionality reduction operation on the multiple spectral information. ### Claim 14 (depends on 10) The computer-implemented method of claim 10, wherein: the multiple spectral information comprises a plurality of images, each image associated with a different spectral band; and the visualization comprises a single image comprising a plurality of features from one or more of the plurality of images. ### Claim 15 (depends on 10) The computer-implemented method of claim 10, wherein: the multiple spectral information comprises a plurality of images, each image associated with a different spectral band; and generating the set of ophthalmic information comprises (i) extracting, via the first deep learning model, a first set of features from one or more regions of one or more of the plurality of images and (ii) extracting, via the second deep learning model, a second set of features from the spatial information. ### Claim 16 (depends on 15) The computer-implemented method of claim 15, wherein: the first set of features comprises a set of local features; each of the one or more regions comprises a single pixel from one of the plurality of images; and the second set of features comprises a set of global features. ### Claim 17 (depends on 15) The computer-implemented method of claim 15, wherein the set of ophthalmic information comprises a prediction of a condition associated with the eye of the patient. ### Claim 18 A non-transitory computer-readable medium having computer executable instructions stored thereon, the computer executable instructions being executable by one or more processors to cause a system to perform operations, the operations comprising: receiving multiple spectral information associated with an eye of a patient; extracting a first set of information from the multiple spectral information and a second set of information from the multiple spectral information, wherein the first set of information comprises spectral information and the second set of information comprises spatial information; generating a set of ophthalmic information associated with the eye of the patient, based on evaluating the first set of information with a first deep learning model and the second set of information with a second deep learning model; generating a visualization of the multiple spectral information based on evaluating the first set of information with a third deep learning model; and presenting an indication of the set of ophthalmic information to a user and the visualization to the user via a display.