Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning

• Published in: BMC medical informatics and decision making Vol. 22; no. 1; pp. 1 - 216
• Main Authors: Fraiwan, Mohammad, Al-Kofahi, Noran, Ibnian, Ali, Hanatleh, Omar
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 13.08.2022; BioMed Central; BMC
• Subjects: Abnormalities; Accuracy; Arthritis; Artificial intelligence; Datasets; DDH; Decision making; Deep learning; Diagnosis; Dysplasia; Health aspects; Health informatics; Hip; Hospitals; Machine learning; Medical diagnosis; Medical imaging; Methods; Neonates; Neural networks; Pelvis; Performance evaluation; Performance measurement; Recall; Screening; Smart phones; Transfer learning; X ray inspection; X-ray; Jordan
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-022-01957-9

Abstract Background Developmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1–5 per 1000 births. It can lead to developmental abnormalities in terms of mechanical difficulties and a displacement of the joint (i.e., subluxation or dysplasia). An early diagnosis in the first few months from birth can drastically improve healing, render surgical intervention unnecessary and reduce bracing time. A pelvic X-ray inspection represents the gold standard for DDH diagnosis. Recent advances in deep learning artificial intelligence have enabled the use of many image-based medical decision-making applications. The present study employs deep transfer learning in detecting DDH in pelvic X-ray images without the need for explicit measurements. Methods Pelvic anteroposterior X-ray images from 354 subjects (120 DDH and 234 normal) were collected locally at two hospitals in northern Jordan. A system that accepts these images as input and classifies them as DDH or normal was developed using thirteen deep transfer learning models. Various performance metrics were evaluated in addition to the overfitting/underfitting behavior and the training times. Results The highest mean DDH detection accuracy was 96.3% achieved using the DarkNet53 model, although other models achieved comparable results. A common theme across all the models was the extremely high sensitivity (i.e., recall) value at the expense of specificity. The F1 score, precision, recall and specificity for DarkNet53 were 95%, 90.6%, 100% and 94.3%, respectively. Conclusions Our automated method appears to be a highly accurate DDH screening and diagnosis method. Moreover, the performance evaluation shows that it is possible to further improve the system by expanding the dataset to include more X-ray images.