Evidence map›Paper›PMID 41389104›Full record

ArticlePediatric radiology2026

Performance of a deep learning-based algorithm for automated measurements of Cobb angles on preoperative spine radiographs in adolescent idiopathic scoliosis.

Maria Chiara Bonanno, Hubert Ducou le Pointe, Mathilde Gaume, Marion Durteste, Mordjane Benhabiles, Alrick Cohen, Korentin Le Floch, Théodore Vuong, Wen Fan Xia, Raphael Vialle and 1 more

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Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Maria Chiara BonannoDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France. mariachiara.bonanno@aphp.fr.
Hubert Ducou le PointeDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.
Mathilde GaumeDepartment of Pediatric Orthopedic and Reconstructive Surgery, Hôpital Armand-Trousseau, Paris, France.
Marion DurtesteGleamer, Paris, France.
Mordjane BenhabilesDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.
Alrick CohenDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.
Korentin Le FlochDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.
Théodore VuongDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.
Wen Fan XiaDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.
Raphael VialleDepartment of Pediatric Orthopedic and Reconstructive Surgery, Hôpital Armand-Trousseau, Paris, France.
Toan NguyenDepartment of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate 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.

objectiveThis 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.

methodsA 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.

resultsThe 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].

conclusionThe 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.

Indexed as

AlgorithmsDeep LearningRadiographic Image Interpretation, Computer-AssistedScoliosisAdolescentFemaleHumansMalePreoperative CareReproducibility of ResultsRetrospective StudiesAdolescent idiopathic scoliosisArtificial intelligenceCobb angleDeep learningSurgery

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