Download PDFOpen PDF in browserAutomatically Extracted Metrics for Diagnosis of Developmental Dysplasia of the Hip are Sensitive to Assumptions About Morphological Priors6 pages•Published: December 17, 2024AbstractDeep learning techniques for diagnosing Developmental Dysplasia of the Hip (DDH) in newborns from ultrasound (US) images of the hip have demonstrated improved reliability over manual annotations of US scans. While volumetric 3D US has been shown to better represent hip bone morphology, most of the proposed automatic diagnostic approaches to measure 3D equivalents of the commonly used 2D Graf angle rely on strong morphological (geometric) priors. We have found that a significant fraction of cases (~20%) result in metrics which expert assessors regard as incorrect or implausible. We hypothesize that the lack of robustness of existing algorithms is due to their assumption that selected morphological priors are always valid, and this may not hold in a number of cases. In this study, we evaluate the differences between extracted DDH metrics based on expert labels and automatic segmentations. We show that a metric extraction process that uses morphological priors is sensitive to relatively small variations in the segmentation results.Keyphrases: automatic diagnosis, deep learning, developmental dysplasia of the hip, diagnostic metrics, volumetric ultrasound In: Joshua W Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 7, pages 25-30.
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