Application of deep learning for medical imaging evaluation
AssigneeQure.ai Technologies Private Limited
InventorsSasank Chilamkurhy, Rohit Ghosh, Swetha Tanamala, Pooja Rao, Prashant Warier
Life of this patent
Estimating the term…
Abstract
This disclosure generally pertains to methods and systems for processing electronic data obtained from imaging or other diagnostic and evaluative medical procedures. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems configured to detect and localize medical abnormalities on medical imaging scans by a deep learning algorithm.
Claims30
A method for streamlining a head scan interpretation workflow by automating an initial screening and triage process, comprising:
- receiving and processing images from CT head scan imaging procedure data;
- detecting and localizing medical abnormalities of the images using a deep learning system,
- wherein the deep learning system carried out by a computer is developed by the steps of:
- selecting medical imaging scans and extracting the medical abnormalities using natural language processing (NLP) algorithms, wherein each type of the medical abnormalities is annotated at scan, slice and pixel levels;
- training a slice-wise deep learning algorithm with the selected medical imaging scans to segment pixel-level annotated scans;
- training the deep learning algorithm with the selected medical imaging scans to generate slice-level confidences;
- predicting confidences for a presence of each type of medical abnormalities;
- and
- generating a score that corresponds to a level of recognition of the medical abnormalities and outputting a mask representing the precise location and extent of the medical abnormalities;
- and
- prioritizing an assignment of a medical evaluation to an evaluator based on the score generated for the images.
An automated head CT scan screening and triage system for streamlining a head scan interpretation workflow, comprising:
- a deep learning system carried out by a computer to detect and localize medical abnormalities on non-contrast head CT scans, wherein the deep learning system is developed by the steps of:
- selecting medical imaging scans and extracting medical abnormalities using natural language processing (NLP) algorithms, wherein each type of the medical abnormalities is annotated at scan, slice and pixel levels;
- training a slice-wise deep learning algorithm with the selected medical imaging scans to segment pixel-level annotated scans;
- training the deep learning algorithm with the selected medical imaging scans to generate slice-level confidences;
- predicting confidences for a presence of each type of medical abnormalities;
- and
- generating a score that corresponds to a level of recognition of the medical abnormalities and outputting a mask representing the precise location and extent of the medical abnormalities;
- wherein the automated head CT scan screening and triage system is configured to receive and process images from CT head scan imaging procedure data;
- wherein the automated head CT scan screening and triage system is configured to detect and localize the medical abnormalities of the images using the deep learning system;
- and
- wherein the automated head CT scan screening and triage system is configured to prioritize an assignment of a medical evaluation to an evaluator based on the score generated for the images.
Dependencies are inferred from the claim text.