Deep learning based screening model for hip diseases on plain radiographs.

ABSTRACT:

Introduction

The interpretation of plain hip radiographs can vary widely among physicians. This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs.

Methods

Electronic medical records and plain radiograph from 2004 to 2012 were used to construct two patient groups: the hip disease group (those who underwent total hip arthroplasty) and normal group. A total of 1,726 radiographs (500 normal hip radiographs and 1,226 radiographs with hip diseases, respectively) were included and were allocated for training (320 and 783), validation (80 and 196), and test (100 and 247) groups. Four different models were designed-raw image for both training and test set, preprocessed image for train

SUBMITTER: Park JW 

PROVIDER: S-EPMC11825046 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature