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    SELECTION OF INTRAOCULAR LENS BASED ON A PLURALITY OF MACHINE LEARNING MODELS
    1.
    发明申请
    SELECTION OF INTRAOCULAR LENS BASED ON A PLURALITY OF MACHINE LEARNING MODELS 有权

    公开(公告)号：US20210106385A1

    公开(公告)日：2021-04-15

    申请号：US17064254

    申请日：2020-10-06

    Applicant: Alcon Inc.

    Inventor： Sinchan Bhattacharya ,  Ramesh Sarangapani

    IPC: A61B34/10 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G16H20/40 ,  G16H50/70 ,  A61F2/16 ,  A61B8/10 ,  G06T7/62 ,  G06T7/70

    Abstract: A method and system for selecting an intraocular lens, with a controller having a processor and tangible, non-transitory memory. A plurality of machine learning models is selectively executable by the controller. The controller is configured to receive at least one pre-operative image of the eye and extract, via a first input machine learning model, a first set of data. The controller is configured to receive multiple biometric parameters of the eye and extract, via a second input machine learning model, a second set of data. The first set of data and the second set of data are combined to produce a mixed set of data. The controller is configured to generate, via an output machine learning model, at least one output factor based on the mixed set of data. An intraocular lens is selected based in part on the at least one output factor.

    AUGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY IMAGE OF EYE BASED ON LEARNING MODULES
    3.
    发明申请
    AUGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY IMAGE OF EYE BASED ON LEARNING MODULES 有权

    公开(公告)号：US20220198654A1

    公开(公告)日：2022-06-23

    申请号：US17507644

    申请日：2021-10-21

    Applicant: Alcon Inc.

    Inventor： Sinchan Bhattacharya ,  Hyo Won Choi ,  Ramesh Sarangapani ,  Bryan Stanfill

    IPC: G06T7/00 ,  A61F9/008

    Abstract: A system and method for augmenting an original OCT (optical coherence tomography) image includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The system includes one or more learning modules selectively executable by the controller. The learning modules are trained by a training network with a training dataset having a plurality of training ultrasound bio-microscopy images and respective training OCT images. Execution of the instructions by the processor causes the controller to obtain the original OCT image, captured through an OCT device. The controller is configured to generate an augmented OCT image based in part on the original OCT image, by executing the (trained) learning modules. The augmented OCT image at least partially extends a peripheral portion of the original OCT image.

    EXPERT PANEL MODELS FOR NEURAL NETWORK ANOMALY DETECTION AND THWARTING ADVERSARIAL ATTACKS
    5.
    发明申请
    EXPERT PANEL MODELS FOR NEURAL NETWORK ANOMALY DETECTION AND THWARTING ADVERSARIAL ATTACKS 有权

    公开(公告)号：US20250165595A1

    公开(公告)日：2025-05-22

    申请号：US18951860

    申请日：2024-11-19

    Applicant: Alcon, Inc.

    Inventor： Shruti Siva Kumar ,  Thomas E. Jackson ,  Sinchan Bhattacharya ,  Ramesh Sarangapani ,  Uma Chandrashekhar ,  Pranava Kumar Vemula

    IPC: G06F21/55 ,  H04L9/40

    Abstract: A system includes a machine learning (ML) engine in which a data store is coupled to a processing system. The processing system executes code to receive a dataset from the data store, and produces three models each comprising three tests. Each of the tests seeks to detect one of three anomaly types corresponding to each of the models. The processing system performs at least two of the three tests relating to each of the three anomaly types. Separately for each anomaly type, the processing system detects an anomaly when two-out-of-three (2oo3) tests conclude that the anomaly is present in the dataset. The dataset including the flagged anomalies is stored in a data repository. The anomaly is filtered from the dataset. The processing system is configured to use data from the dataset to retrain an existing trained ML model.

    Selection of intraocular lens based on a plurality of machine learning models
    6.
    发明授权
    Selection of intraocular lens based on a plurality of machine learning models 有权

    公开(公告)号：US11547484B2

    公开(公告)日：2023-01-10

    申请号：US17064254

    申请日：2020-10-06

    Applicant: Alcon Inc.

    Inventor： Sinchan Bhattacharya ,  Ramesh Sarangapani

    IPC: A61B34/10 ,  G06T7/62 ,  G06T7/70 ,  G16H20/40 ,  G16H30/20 ,  G16H50/30 ,  G16H50/70 ,  G16H50/20 ,  G16H30/40 ,  A61B8/10 ,  A61F2/16

    Abstract: A method and system for selecting an intraocular lens, with a controller having a processor and tangible, non-transitory memory. A plurality of machine learning models is selectively executable by the controller. The controller is configured to receive at least one pre-operative image of the eye and extract, via a first input machine learning model, a first set of data. The controller is configured to receive multiple biometric parameters of the eye and extract, via a second input machine learning model, a second set of data. The first set of data and the second set of data are combined to produce a mixed set of data. The controller is configured to generate, via an output machine learning model, at least one output factor based on the mixed set of data. An intraocular lens is selected based in part on the at least one output factor.

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