Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

ABSTRACT: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images.

Introduction

In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT).

Methods

The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if

SUBMITTER: Gebre RK 

PROVIDER: S-EPMC8813821 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature