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Automated Neonatal Hip Ultrasound System for Diagnosing Developmental Dysplasia of Hips Using Assistive AI.

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Title: Automated Neonatal Hip Ultrasound System for Diagnosing Developmental Dysplasia of Hips Using Assistive AI.
Authors: Lee, Young Seop1 (AUTHOR) liive2925@gmail.com, Kim, Young Jae1 (AUTHOR), Ryu, Jeong Won2 (AUTHOR), Lee, Su Yeol2 (AUTHOR), Kim, Kwang Gi1,3 (AUTHOR) kimkg@gachon.ac.kr
Source: Journal of Imaging Informatics in Medicine. Feb2026, Vol. 39 Issue 1, p518-531. 14p.
Subjects: Hip joint dislocation, Research funding, Data analysis, T-test (Statistics), Receiver operating characteristic curves, Artificial intelligence, Retrospective studies, Descriptive statistics, Dysplasia, Hip joint, Medical records, Acquisition of data, One-way analysis of variance, Statistics, Automation, Confidence intervals, Comparative studies, Sensitivity & specificity (Statistics), Algorithms, Inter-observer reliability, Children
Geographic Terms: South Korea
Abstract: This study aims to develop and evaluate an artificial intelligence (AI)-based diagnostic system for the diagnosis of developmental dysplasia of the hip (DDH) in infant hip ultrasonography. The Graf algorithm was employed to develop an automated model for diagnosing DDH, resulting in a DDH-assisted AI model with an average Graf angle error rate of 0.21 compared to expert diagnostics. NASNetMobile achieved the highest Area Under the Curve (AUC) of 0.864 (95% CI, 0.850–0.878), closely followed by MobileNetV1, DenseNet121, EfficientNetV2B0, NASNetMobile, and ResNet50. UnestedUNet demonstrated the highest overall performance, achieving Dice coefficients of 0.794 and a runtime of 40.078 ms, demonstrating its strong segmentation accuracy with moderate computational demands. DeepLabV3Plus, a handheld ultrasound device integrated with a smartphone, demonstrated a robust and efficient segmentation performance. This study highlights the transformative potential of integrating AI into portable ultrasound devices, enabling accurate, efficient, and accessible diagnostic solutions. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This study aims to develop and evaluate an artificial intelligence (AI)-based diagnostic system for the diagnosis of developmental dysplasia of the hip (DDH) in infant hip ultrasonography. The Graf algorithm was employed to develop an automated model for diagnosing DDH, resulting in a DDH-assisted AI model with an average Graf angle error rate of 0.21 compared to expert diagnostics. NASNetMobile achieved the highest Area Under the Curve (AUC) of 0.864 (95% CI, 0.850–0.878), closely followed by MobileNetV1, DenseNet121, EfficientNetV2B0, NASNetMobile, and ResNet50. UnestedUNet demonstrated the highest overall performance, achieving Dice coefficients of 0.794 and a runtime of 40.078 ms, demonstrating its strong segmentation accuracy with moderate computational demands. DeepLabV3Plus, a handheld ultrasound device integrated with a smartphone, demonstrated a robust and efficient segmentation performance. This study highlights the transformative potential of integrating AI into portable ultrasound devices, enabling accurate, efficient, and accessible diagnostic solutions. [ABSTRACT FROM AUTHOR]
ISSN:29482925
DOI:10.1007/s10278-025-01498-3