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    Direct medical treatment predictions using artificial intelligence
    31.
    发明授权
    Direct medical treatment predictions using artificial intelligence 有权

    公开(公告)号：US12051490B2

    公开(公告)日：2024-07-30

    申请号：US17541936

    申请日：2021-12-03

    Applicant: Digital Diagnostics Inc.

    Inventor： Michael D. Abramoff

    IPC: G16H20/00 ,  G06F18/214 ,  G06T7/00 ,  G16H50/20

    CPC classification number: G16H20/00 ,  G06F18/214 ,  G06T7/0012 ,  G16H50/20

    Abstract: A device is disclosed herein that receives image data corresponding to an anatomy of a patient. The device applies the image data to one or more feature models trained using training data that pairs anatomical images to an anatomical feature label, and receives, as output from the one or more feature models, scores for each of a plurality of anatomical features corresponding to the image data. The device applies the scores as input to a treatment model, the treatment model trained to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy. The device receives, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment.

    AUTONOMOUS DIAGNOSIS OF A DISORDER IN A PATIENT FROM IMAGE ANALYSIS
    34.
    发明公开
    AUTONOMOUS DIAGNOSIS OF A DISORDER IN A PATIENT FROM IMAGE ANALYSIS 审中-公开

    公开(公告)号：US20230419485A1

    公开(公告)日：2023-12-28

    申请号：US18367384

    申请日：2023-09-12

    Applicant: Digital Diagnostics Inc.

    Inventor： Meindert Niemeijer ,  Ryan Amelon ,  Warren Clarida ,  Michael D. Abramoff

    IPC: G06T7/00 ,  G06N3/08 ,  G06V10/44 ,  G06F18/214 ,  G06N3/045 ,  G06V10/82

    CPC classification number: G06T7/0012 ,  G06N3/08 ,  G06V10/454 ,  G06F18/2148 ,  G06N3/045 ,  G06V10/82 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/30041

    Abstract: Provide are systems methods and devices for diagnosing disease in medical images. In certain aspects, disclosed is a method for training a neural network to detect features in a retinal image including the steps of: a) extracting one or more features images from a Train_0 set, a Test_0 set, a Train_1 set and a Test_1 set; b) combining and randomizing the feature images from Train_0 and Train_1 into a Training data set; c) combining and randomizing the feature images from Test_0 and Test_1 into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached.

    Autonomous diagnosis of ear diseases from biomarker data
    35.
    发明授权
    Autonomous diagnosis of ear diseases from biomarker data 有权

    公开(公告)号：US11786148B2

    公开(公告)日：2023-10-17

    申请号：US16529685

    申请日：2019-08-01

    Applicant: Digital Diagnostics Inc.

    Inventor： Michael D. Abramoff ,  Ryan Amelon

    IPC: A61B5/12 ,  A61B5/00 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  A61B1/227

    CPC classification number: A61B5/121 ,  A61B5/0066 ,  A61B5/4842 ,  A61B5/7275 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  A61B1/227

    Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.

    Systems for Detecting and Identifying Coincident Conditions
    36.
    发明申请
    Systems for Detecting and Identifying Coincident Conditions 有权

    公开(公告)号：US20220367049A1

    公开(公告)日：2022-11-17

    申请号：US17623533

    申请日：2020-06-30

    Applicant: Digital Diagnostics Inc.

    Inventor： Joseph FERRANTE ,  Elliot SWART

    IPC: G16H50/20 ,  G16H30/20

    Abstract: A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.

    Systems for Detecting and Identifying Coincident Conditions
    38.
    发明申请
    Systems for Detecting and Identifying Coincident Conditions 有权

    公开(公告)号：US20250104872A1

    公开(公告)日：2025-03-27

    申请号：US18976114

    申请日：2024-12-10

    Applicant: Digital Diagnostics Inc.

    Inventor： Joseph FERRANTE ,  Elliot SWART

    IPC: G16H50/20 ,  G16H30/20

    Abstract: A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.

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