AI-based image analysis for detecting normal images

Abstract

A system and method for identifying abnormal medical images are disclosed. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.

Description

[0001] Cross References to Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 033,875, filed June 3, 2020, entitled "AI-BASED IMAGE ANALYSIS FOR THEDETECTION OF NORMAL IMAGES (ESPECIALLY FOR MULTI-INDICATION EXAMS)," which U.S. The Provisional Patent Application is hereby incorporated by reference in its entirety. Background technique

[0003] The volume of medical images that need to be processed keeps growing every year, placing an increasing burden on the radiologists responsible for analyzing and interpreting these images. Additionally, due to routine medical practice, some types of medical images, such as chest x-rays or chest CT images (eg, for lung cancer screening) have a high rate of "normal" (ie, without any radiation Abnormal cases visible on photography). Reviewing what is normal can take up considerable radiologist time that could be better spent reviewing and analyzing medical images that actually have r...

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