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)

Xiaoyi Chen; Shuangshuang Zhang; Wei Shi; Dechao Wu; Bingxuan Huang; Hongwei Tao; Xuezhi He; Na Xu

doi:10.3389/fped.2023.1293320

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)

Front Pediatr. 2023 Nov 16:11:1293320. doi: 10.3389/fped.2023.1293320. eCollection 2023.

Authors

Xiaoyi Chen¹, Shuangshuang Zhang¹, Wei Shi², Dechao Wu², Bingxuan Huang³, Hongwei Tao³, Xuezhi He³, Na Xu^{1

3}

Affiliations

¹ Department of Ultrasound, Shenzhen Children's Hospital of China Medical University, Shenzhen, China.
² Department of Orthopedics, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China.
³ Department of Ultrasound, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China.

Abstract

Objective: To 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).

Method: We 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).

Results: According 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.

Conclusion: The 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.

Keywords: deep learning; developmental dysplasia of the hip; individuation; infant; ultrasonography.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.