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Published on: February 2, 2015
Artificial Intelligence-Assisted Wireless Handheld Ultrasound for Screening Developmental Dysplasia of the Hip in
Dandan Zhang1, Simiao Tao2, Ping Chen1
1Department of Ultrasonography, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Objectives:
Ultrasonography is increasingly the preferred method for infant hip screening to enable timely diagnosis and treatment of developmental dysplasia of the hip (DDH). However, its reliance on experienced specialists and bulky equipment limits its application in routine screening, particularly in resource-limited and remote settings. We aimed to develop an artificial intelligence wireless handheld ultrasound hip diagnostic system (AI-HUDs) for accurate and robust DDH diagnosis.
Methods:
An AI-HUDs was developed to automatically identify and analyze 6 key anatomical landmarks in static and dynamic hip ultrasound images, employing the Graf method to measure α and β angles. The dataset comprised 1192 ultrasound images and 498 dynamic videos. The YOLOv8 model was trained and tested to construct AI-HUDs. Evaluation focused on the consistency between AI-HUDs' automatic angle measurements and manual measurements by experts using traditional ultrasound equipment. Additionally, 104 hip ultrasound videos from 52 infants were acquired by a resident physician using AI-HUDs, while corresponding static images were manually measured by ultrasound experts to assess agreement between novice-operated AI-HUDs and expert manual assessment.
Results:
Compared with expert manual measurements, the AI-HUDs demonstrated good performance in static mode: the differences in α and β angles exhibited standard deviations of 1.38° and 2.14°, and mean absolute errors (MAEs) of 1.05° and 1.65°, respectively. The intraclass correlation coefficients (ICCs) were 0.82 (α) and 0.64 (β). In dynamic mode, the MAEs increased slightly to 1.18° (α) and 1.86° (β), while the ICCs decreased to 0.77 for α and 0.51 for β. When operated by the resident physician, AI-HUDs maintained good performance with expert manual measurements: MAEs were 1.16° (α) and 1.91° (β); ICCs were 0.69 (α) and 0.61 (β). Bland-Altman analysis showed 96.15% (α) and 95.19% (β) of data points within the limits of agreement.
Conclusion:
AI-HUDs demonstrated high reliability and accuracy in automatically measuring α and β angles. Resident physicians using this system achieved diagnostic performance comparable to experts. AI-HUDs show promise as a convenient DDH screening tool in resource-scarce regions, potentially facilitating wider adoption of infant hip ultrasound screening.
