公开(公告)号：US20240020825A1

公开(公告)日：2024-01-18

申请号：US17862553

申请日：2022-07-12

Applicant: Imvaria Inc.

Inventor： Joshua REICHER ,  Michael MUELLY

IPC: G06T7/00 ,  G16H30/40 ,  G16H70/60 ,  G06F40/205 ,  G06F40/258 ,  G06T11/00 ,  G06V30/19

CPC classification number: G06T7/0012 ,  G16H30/40 ,  G16H70/60 ,  G06F40/205 ,  G06F40/258 ,  G06T11/008 ,  G06V30/19 ,  G06T2207/10081 ,  G06T2207/30061 ,  G06V2201/03

Abstract: A method of automated diagnosis of disease database entities includes receiving a case processing request via an input application programming interface (API), extracting image data from the case processing request including at least one medical scan image of the patient, selecting at least a portion of the medical scan image(s) according to specified selection criteria, normalizing the selected at least a portion of the medical scan image(s), supplying the selected at least a portion of the medical scan image(s) to a machine learning model to generate a target medical condition prediction output, wherein the target medical condition prediction output is indicative of a likelihood that a patient will experience a future disease diagnosis event corresponding to the target medical condition, and automatically transmitting the target medical condition prediction output as an electronic transmission via an output API to a provider system associated with the patient.

公开(公告)号：US20240020824A1

公开(公告)日：2024-01-18

申请号：US17862529

申请日：2022-07-12

Applicant: Imvaria Inc.

Inventor： Joshua REICHER ,  Michael MUELLY

IPC: G06T7/00 ,  G16H30/40 ,  G16H70/60 ,  A61B6/03 ,  A61B6/00

CPC classification number: G06T7/0012 ,  G16H30/40 ,  G16H70/60 ,  A61B6/032 ,  A61B6/50 ,  A61B6/5217 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30061 ,  G06T2207/20084 ,  G06T2207/20076 ,  G06T2207/10016

Abstract: A computer system includes memory hardware configured to store a three-dimensional deep learning model, and a pathology outcome database including multiple computed tomography (CT) images each associated with a surgical pathology outcome value. Processor hardware is configured to execute instructions including, for each of the multiple CT images, obtaining the surgical pathology outcome value associated with the CT image, and assigning the CT image to a positive image training dataset or a negative image training dataset in response to the surgical pathology outcome value indicating whether the patient experienced a disease diagnosis corresponding to a target medical condition. The instructions include supplying the positive and negative training image datasets to the three-dimensional deep learning model to train the model to generate a target medical condition prediction output indicative of a likelihood that a patient will experience a future disease diagnosis corresponding to the target medical condition.