Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip.

Autor: Li R; Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing, China.; Department of General Surgery, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing,, China., Wang X; Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing, China., Li T; Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing, China. ltranmd@yeah.net., Zhang B; Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing, China., Liu X; Department of Research and Development, United Imaging Intelligence(Beijing) Co.Ltd, No. 9, Yongteng Road, Haidian District, Beijing, China., Li W; Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing, China., Sui Q; Department of Radiology, The Fourth Medical Center of Chinese PLA General Hospital, No. 51, Fucheng Road, Haidian District, Beijing, China.
Jazyk: angličtina
Zdroj: BMC musculoskeletal disorders [BMC Musculoskelet Disord] 2024 Nov 13; Vol. 25 (1), pp. 906. Date of Electronic Publication: 2024 Nov 13.
DOI: 10.1186/s12891-024-08035-3
Abstrakt: Objectives: Anteroposterior pelvic radiographs remains the most widely employed method for diagnosing developmental dysplasia of the hip. This study aims to evaluate the accuracy of an artificial intelligence model in measuring angles in pelvic radiographs of the hip. The assessment seeks to demonstrate the efficacy of the artificial intelligence model in diagnosing both developmental dysplasia of the hip and borderline developmental dysplasia of the hip through the analysis of pelvic radiographs.
Methods: A total of 1,029 patients, including 273 men and 757 women, were retrospectively included in this study. The anteroposterior pelvic radiographs were randomly divided into three sets: the training set (720 cases), the validation set (103 cases), and the test set (206 cases). Key anatomical points on the anteroposterior pelvic radiographs were identified. The Sharp, Tönnis, and Center Edge angles were calculated automatically based on the corresponding criteria. The hip development status was compared between measurements obtained from the artificial intelligence model and those defined manually by two radiologists. The area under the receiver operating characteristic curve was utilized to assess the diagnostic performance of the artificial intelligence model.
Results: The results obtained from both manual measurements and the artificial intelligence model demonstrated no significant differences in the Sharp, Tönnis, and Center edge angles (all p > 0.05). The intra-class correlation coefficients and correlation coefficient r values exceeded 0.75, indicating that both the artificial intelligence model and manual measurements exhibited good repeatability and a positive correlation. Notably, the artificial intelligence model provided measurements more faster than those conducted by radiologists (p = 0.001). The artificial intelligence model also demonstrated high diagnostic accuracy, sensitivity, and specificity for developmental dysplasia of the hip. The performance of the artificial intelligence model in diagnosing developmental dysplasia of the hip was robust. Additionally, the results from the artificial intelligence model and manual measurements were largely consistent with clinical diagnosis results (p = 0.01). The artificial intelligence model can effectively evaluate hip conditions by measuring the Sharp, Tönnis, and Center edge angles, which are consistent closely with clinical diagnosis results.
Conclusions: The results of the artificial intelligence model measurements demonstrate a high degree of consistency with those obtained through manual measurements. The angles of Sharp, Tönnis, and Center edge, as evaluated by the deep learning-based convolutional neural network model, exhibit robust diagnostic performance in identifying both developmental dysplasia of the hip and borderline developmental dysplasia of the hip. Consequently, the artificial intelligence model has the potential to fully replace manual measurements of these critical hip angles, providing a more efficient and precise alternative for diagnosing both conditions of the hip.
Competing Interests: Declarations Ethics approval and consent to participate The study received approval from the Institutional Review Board of the Fourth Medical Center of the General Hospital of the Chinese People’s Liberation Army. All methods were conducted in accordance with relevant guidelines and regulations. The procedures adhered to the principles outlined in the Helsinki Declaration. We confirm that informed consent was obtained from all participants. Each author certifies that all investigations were carried out in compliance with ethical standards. Consent for publication Not applicable. Competing interests The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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