Journal of Tissue Engineering and Reconstructive Surgery ›› 2024, Vol. 20 ›› Issue (1): 114-.

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Deep-learning system assisted staged training for developmental dysplasia of the hip

  

  • Published:2024-03-07

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Abstract:

Objective To develop a deep learning system for assisted diagnosis of Crowe staging in adult patients with
developmental dysplasia of the hip （DDH）， and to analyze the feasibility of the system in assisting clinical medical students
to master DDH staging. Methods A training set of 149 X-rays， a test set of 42 cases， and a validation set of 21 cases were
included， and the pelvis was segmented， localized image blocks of DDH were extracted， and the gold-standard results were
compared with those assessed by medical students and AI-assisted medical students. Results A total of 42 cases， including
30 females and 12 males， aged （69±12） years， were included in the test set， and 67 dysplastic hips were involved （30 on the left and 37 on the right） . The correlation of the AI， medical student， and AI-assisted medical student assessment results with the gold standard was 0.906［ 95% CI（ 0.850， 0.941）］， 0.823［ 95% CI（ 0.726， 0.887）］， 0.886［ 95% CI（0.821， 0.929）］ . The accuracy of AI， medical students and AI-assisted medical students was 0.87，
0.78 and 0.88， the precision was 0.88，0.83 and 0.89， the recall rate was 0.87，0.78 and 0.88， and F1 value was 0.87，0.80 and 0.88， respectively. The results ofthe confusion matrices and conditional probabilities showed that the accuracy of the three groups of type Ⅰ were 0.98，0.88，0.96， and 0.40，0.20，0.40 for type Ⅱ trio， and 0.56，0.67，0.78 for type Ⅲ trio， and 0.88，0.75，0.88 for type Ⅳ trio.
Conclusion Deep learning-assisted diagnostic system can effectively improve the medical students' assessment of the DDH
patients with various types of DDH， and can be used as a training tool for medical students to learn and master the diagnosis
of DDH imaging.

Key words:

medical students