Artificial <scp>Intelligence‐Assisted</scp> Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources

Huang, Bingxuan; Xia, Bei; Qian, Jikuan; Zhou, Xinrui; Zhou, Xu; Liu, Shengfeng; Ao, Chang; Yan, Zhongnuo; Tang, Zi-Jian; Xu, Na; Tao, Hongwei; He, Xingyuan; Yu, Wei; Zhang, Renfu; Huang, Ruobing; Ni, Dong

doi:10.1002/jum.16133

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Chen et al 10.3389/fped.2023.1293320 chosen for automatic scanning using AI (21). The obtained measured values exhibited a high degree of consistency with those that were manually measured, thus indicating that the anatomical structure of the images analyzed in this study was clear, accurate, and diagnosable.…”

Section: Discussionsupporting

confidence: 58%

A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

Chen,

Zhang,

Shi

et al. 2023

Front. Pediatr.

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ObjectiveTo examine the correlation between specific indicators and the quality of hip joint ultrasound images in infants and determine whether the individual infant suit ultrasound examination for developmental dysplasia of the hip (DDH).MethodWe retrospectively selected infants aged 0–6 months, undergone ultrasound imaging of the left hip joint between September 2021 and March 2022 at Shenzhen Children’s Hospital. Using the entropy weighting method, weights were assigned to anatomical structures. Moreover, prospective data was collected from infants aged 5–11 months. The left hip joint was imaged, scored and weighted as before. The correlation between the weighted image quality scores and individual indicators were studied, with the last weighted image quality score used as the dependent variable and the individual indicators used as independent variables. A Long-short term memory (LSTM) model was used to fit the data and evaluate its effectiveness. Finally, The randomly selected images were manually measured and compared to measurements made using artificial intelligence (AI).ResultsAccording to the entropy weight method, the weights of each anatomical structure as follows: bony rim point 0.29, lower iliac limb point 0.41, and glenoid labrum 0.30. The final weighted score for ultrasound image quality is calculated by multiplying each score by its respective weight. Infant gender, age, height, and weight were found to be significantly correlated with the final weighted score of image quality (P < 0.05). The LSTM fitting model had a coefficient of determination (R2) of 0.95. The intra-class correlation coefficient (ICC) for the α and β angles between manual measurement and AI measurement was 0.98 and 0.93, respectively.ConclusionThe quality of ultrasound images for infants can be influenced by the individual indicators (gender, age, height, and weight). The LSTM model showed good fitting efficiency and can help clinicians select whether the individual infant suit ultrasound examination of DDH.

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Section: Discussionsupporting

confidence: 58%

A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

Chen,

Zhang,

Shi

et al. 2023

Front. Pediatr.

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The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis

Chen,

Cai,

Zhang

et al. 2024

J Orthop Surg Res

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Screening Accuracy for Developmental Dysplasia of the Hip by Child Health Nurses

Smart,

Cundy,

Williams

et al. 2024

Children

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Background: Developmental Dysplasia of the Hip (DDH) describes a spectrum of abnormalities that impact the normal function of the joint. These abnormalities are detectable during infancy using physical assessment, universal ultrasound, or a combination of both. In Australia, child health nurses predominantly screen for this disease using physical assessment. The aim of this study was to determine if child health nurses can accurately screen for DDH using physical assessment. Methods: This Australian study estimated the accuracy and utility of child health nurses using physical assessment to screen for DDH by comparing it to a handheld ultrasound device. Results: This study (N = 44) concluded that using a point prevalence study design, child health nurses in Australia are unable to accurately assess for DDH in infants using physical assessment; overall, clinicians had 50.5% sensitivity (95% CI, 15.7 to 84.3), a specificity of 75.0% (95% CI, 57.8 to 87.9), and 71.3% accuracy (95% CI, 55.6 to 83.9). Conclusion: This study suggests that child health nurses cannot screen for DDH using physical assessment. Understanding education, experience and what assessments are used suggests that the utility of nurses performing DDH screening requires further investigation.

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

Cited by 3 publications

References 26 publications

A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis

Screening Accuracy for Developmental Dysplasia of the Hip by Child Health Nurses

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