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

Maria Chiara Bonanno¹, Hubert Ducou le Pointe², Mathilde Gaume³, Marion Durteste⁴, Mordjane Benhabiles², Alrick Cohen², Korentin Le Floch², Théodore Vuong², Wen Fan Xia², Raphael Vialle³, Toan Nguyen²

¹Department of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France. mariachiara.bonanno@aphp.fr.

²Department of Pediatric Radiology, Hôpital Armand-Trousseau, 26 Avenue du Dr Arnold Netter, 75012, Paris, France.

³Department of Pediatric Orthopedic and Reconstructive Surgery, Hôpital Armand-Trousseau, Paris, France.

⁴Gleamer, Paris, France.

PubMed DOI

Abstract

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 wi…

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