Artificial Intelligence-Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources.

Objectives: Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH.

Methods: An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five-fold cross-validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost.

Results: DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra-class correlation coefficient (ICC) on α angle of 0.96 (95% CI: 0.93-0.97), β angle of 0.97 (95% CI: 0.95-0.98), FHC of 0.98 (95% CI: 0.96-0.99) and PFD of 0.94 (95% CI: 0.90-0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds.

Conclusions: The proposed DDHnet demonstrate state-of-the-art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.