Pediatric radiology · 2025
Performance of a deep learning-based algorithm for automated measurements of Cobb angles on preoperative spine radiographs in adolescent idiopathic scoliosis.
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Bonanno Maria Chiara, Ducou le Pointe Hubert, Gaume Mathilde, Durteste Marion, Benhabiles Mordjane, Cohen Alrick, Le Floch Korentin, Vuong Théodore, Xia Wen Fan, Vialle Raphael, Nguyen Toan
Аннотация
BACKGROUND: Accurate Cobb angle measurement is essential in adolescent idiopathic scoliosis (AIS), but evidence on artificial intelligence (AI) performance in pediatric patients, especially with severe curves, is limited.
OBJECTIVE: This study evaluated the accuracy of a commercially available deep learning software in measuring Cobb angles in surgical cases of AIS and compared its performance with that of radiology residents.
METHODS: A total of 151 preoperative anteroposterior whole spine X-rays were analyzed. The ground truth was established by consensus between a pediatric radiologist and a pediatric orthopedic surgeon. The mean absolute error (MAE) of the AI and six radiology residents was calculated. Wilcoxon signed-rank test and intraclass correlation coefficients (ICC) were computed.
RESULTS: The dataset included 151 angles categorized as moderate (13; 20-39°), severe (74; 40-59°), and extreme (64; ≥60°). Overall, the AI's MAE was 4.57° [95%CI 3.85°, 5.29°], significantly higher than that of the residents' (P=0.0017). Scoliosis severity significantly affected MAE in both groups. For extreme scoliosis, the MAE of the AI (6.53° [95%CI 5.06°, 7.86°]) was significantly higher than that of the residents (3.61° [95%CI, 3.17°, 3.97°]) (P<0.001). No significant difference in MAE was observed between patients with moderate and severe scoliosis (P=0.86). The agreement between the ICC between the AI and the ground truth was 0.89 [95%CI 0.69, 0.95].
CONCLUSION: The Cobb angle measurements obtained with this software agree with those of experts for moderate and severe scoliosis with a clinically acceptable average error, without significant difference compared to radiology residents. AI accuracy significantly decreases in patients with extreme scoliosis, highlighting the need for radiologist oversight.
OBJECTIVE: This study evaluated the accuracy of a commercially available deep learning software in measuring Cobb angles in surgical cases of AIS and compared its performance with that of radiology residents.
METHODS: A total of 151 preoperative anteroposterior whole spine X-rays were analyzed. The ground truth was established by consensus between a pediatric radiologist and a pediatric orthopedic surgeon. The mean absolute error (MAE) of the AI and six radiology residents was calculated. Wilcoxon signed-rank test and intraclass correlation coefficients (ICC) were computed.
RESULTS: The dataset included 151 angles categorized as moderate (13; 20-39°), severe (74; 40-59°), and extreme (64; ≥60°). Overall, the AI's MAE was 4.57° [95%CI 3.85°, 5.29°], significantly higher than that of the residents' (P=0.0017). Scoliosis severity significantly affected MAE in both groups. For extreme scoliosis, the MAE of the AI (6.53° [95%CI 5.06°, 7.86°]) was significantly higher than that of the residents (3.61° [95%CI, 3.17°, 3.97°]) (P<0.001). No significant difference in MAE was observed between patients with moderate and severe scoliosis (P=0.86). The agreement between the ICC between the AI and the ground truth was 0.89 [95%CI 0.69, 0.95].
CONCLUSION: The Cobb angle measurements obtained with this software agree with those of experts for moderate and severe scoliosis with a clinically acceptable average error, without significant difference compared to radiology residents. AI accuracy significantly decreases in patients with extreme scoliosis, highlighting the need for radiologist oversight.