Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a retrospective, prospective, multicenter study in China.

ABSTRACT:

Background

Hip ultrasound is the first-line tool to identify developmental dysplasia of the hip (DDH) among suspected infants, yet it has limitations including poor reproducibility and high diagnostic error rates. This study aims to develop and validate a deep convolutional neural network algorithm, named HipSonoNeuNet model (HSNN), using multicenter hip ultrasound data.

Methods

This multicenter cross-sectional study combined data from 22 Chinese hospitals (September 2022-January 2025), enrolling 3082 participants. A total of 7286 hip ultrasound images (1429 dynamic, 5857 static) were collected and were divided into three datasets. The study was conducted in three phases. Phase I trained the models using 2431 participants (Dataset 1). Phase II compared diagnostic performance

SUBMITTER: Xu N 

PROVIDER: S-EPMC12547208 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature