Deep learning and radiomics-based system for early diagnosis of hip synovitis in juvenile idiopathic arthritis.

ABSTRACT:

Objective

Juvenile Idiopathic Arthritis (JIA) frequently affects children's hips, causing severe progression, but early hip synovitis lacks obvious symptoms and is hard to detect via conventional ultrasound, delaying diagnosis. magnetic resonance imaging (MRI), though accurate, is costly and inaccessible for routine use. This study aims to develop an automatic identification system for the early diagnosis of hip synovitis in JIA through the integration of deep learning and radiomics techniques.

Methods

A YOLO-JIA model specifically designed for the automatic segmentation of hip ultrasound images was developed. Radiomic features were extracted from these segmented regions. Subsequently, feature selection was performed using the analysis of variance (ANOVA) test followed by le

SUBMITTER: Kou J 

PROVIDER: S-EPMC12855055 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature