Deep learning-based automated guide for defining a standard imaging plane for developmental dysplasia of the hip screening using ultrasonography: a retrospective imaging analysis
Year: 2025
Type: article
Abstract: BACKGROUND: We aimed to propose a deep-learning neural network model for automatically detecting five landmarks during a two-dimensional (2D) ultrasonography (US) scan to develop a standard plane for developmental dysplasia of the hip (DDH) screening. METHOD: A model of global and local networks was developed to detect five landmarks for DDH screening during 2D US. Patients (N = 532) who underwent hip US for DDH screening from January 2016 to December 2021 at a tertiary medical center were enrolled. All datasets were randomly split into training, validation, and test sets in a 70:10:20 ratio for the final assessment of landmark detection. The performance of this model for detecting five landmarks for guiding DDH was analyzed using the root mean square error (RMSE) and dice similarity coefficient. RESULTS: The RMSE value for the five landmarks for diagnosing and classifying DDH using global and local networks was 4.023 ± 3.723. The point results using EfficientNetB2 were 1.69 ± 1.26 (first point), 3.34 ± 2.37 (second point), 2.54 ± 1.61 (third point), 5.92 ± 4.25 (fourth point), and 6.61 ± 4.82 (fifth point). CONCLUSIONS: Our deep-learning network model is feasible for detecting five landmarks for DDH using ultrasound images. The primary parameters to determine DDH will be significantly detected by applying the deep-learning model in clinical settings.
Authors: Kyung-Sik Ahn, Ji Hye Choi, Heejou Kwon, Seoyeon Lee, Yongwon Cho, Woo Young Jang
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W4407697173