Analysis of Developmental Dysplasia of the Hip Using Deep Learning Techniques

Çelik R., Yokuş A., Gündüz A. M., Canayaz M., Toprak N., Türkoğlu S.

SN COMPREHENSIVE CLINICAL MEDICINE, cilt.7, sa.214, ss.1-6, 2025 (Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 7 Sayı: 214
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s42399-025-01998-x
  • Dergi Adı: SN COMPREHENSIVE CLINICAL MEDICINE
  • Derginin Tarandığı İndeksler: Scopus, EBSCO Legal Collection
  • Sayfa Sayıları: ss.1-6
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Purpose Developmental dysplasia of the hip (DDH) is a relatively common musculoskeletal condition in neonates. Early

detection with ultrasound (US) is crucial for effective treatment. This study aimed to evaluate images obtained from hip

ultrasonography with deep learning methods.

Material and Method Patients who underwent hip ultrasonography between January 2018 and September 2021 and were

found to have normal hips and hip dysplasia were retrospectively screened. A total of 947 patient images, 450 girls and 497

boys, were examined. According to the Graf method, images were classified without any marking. In the first stage, two

groups were created: those with Type 1 mature hips and those with dysplastic hips (other types). In the second stage of the

study, four groups were created using only the α angle: 451 were classified as Type 1, 326 as Type 2a and 2b, 137 as Type

2c and D, and 33 as Type 3 and Type 4.

During the classification, three versions of the EfficientNet model, one of the current deep learning models, were used. Classifiers

were included in the study to improve the accuracy values of the models. In our study, two classifiers named support

vector machine and K-nearest neighbors were used.

Results In the classification phase with deep learning models, the highest accuracy value of 0.9577 was obtained with the

EfficientNetB1 model for 2 classes in the first group, while the highest accuracy value of 0.8571 was obtained with the

EfficientNetB0 model for 4 classes in the second group. By including the classifiers in the evaluation, the highest accuracy

rate was found to be 0.99 with EfficientNetB1 and 1(100%) with EfficientNetB2 in the first group, while it was 0.97 with

EfficientNetB0 in the second group.

Conclusion In the diagnosis of developmental hip dysplasia, high accuracy rates were obtained in deep learning methods

using US images. Accuracy rates increased with the addition of classifiers to the models.