ArticleSkeletal radiology2025
Deep learning algorithm enables automated Cobb angle measurements with high accuracy.
Article in Skeletal radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Algorithmic fairness reporting in artificial intelligence for spinal deformity: a systematic review.Spine deformity · 2026Review
- Diagnostic accuracy of artificial intelligence for spinopelvic parameters in standing total spine X-ray and limitations after fusion surgery.Skeletal radiology · 2026Article
- Large language models for lumbar spondylolisthesis detection: a multi-center pilot comparative radiographic accuracy study.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2026Article
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Geometric foundations of measurement uncertainty and clinical relevance in radiographic spinal angle assessment.Spine deformity · 2026Article
- AI model for automatic spinopelvic and spinal alignment parameters measurement from EOS.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Performance of a deep learning-based algorithm for automated measurements of Cobb angles on preoperative spine radiographs in adolescent idiopathic scoliosis.Pediatric radiology · 2026Article
- Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors.Frontiers in bioengineering and biotechnology · 2026Article
- [Current Landscape and Commercialization of AI Models in Musculoskeletal Imaging].Journal of the Korean Society of Radiology · 2025Review
- Artificial intelligence in musculoskeletal radiology: practical aspects and latest perspectives.BJR open · 2025Review
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13 authors.
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Abstract
objectiveTo determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs. MATERIALS AND
methodsFull spine radiographs of patients aged > 2 years were screened using the radiology reports to identify radiographs for performing Cobb angle measurements. Two senior musculoskeletal radiologists and one senior orthopedic surgeon independently annotated Cobb angles exceeding 7° indicating the angle location as either proximal thoracic (apices between T3 and T5), main thoracic (apices between T6 and T11), or thoraco-lumbar (apices between T12 and L4). If at least two readers agreed on the number of angles, location of the angles, and difference between comparable angles was < 8°, then the ground truth was defined as the mean of their measurements. Otherwise, the radiographs were reviewed by the three annotators in consensus. The DL software (BoneMetrics, Gleamer) was evaluated against the manual annotation in terms of mean absolute error (MAE).
resultsA total of 345 patients were included in the study (age 33 ± 24 years, 221 women): 179 pediatric patients (< 22 years old) and 166 adult patients (22 to 85 years old). Fifty-three cases were reviewed in consensus. The MAE of the DL algorithm for the main curvature was 2.6° (95% CI [2.0; 3.3]). For the subgroup of pediatric patients, the MAE was 1.9° (95% CI [1.6; 2.2]) versus 3.3° (95% CI [2.2; 4.8]) for adults.
conclusionThe DL algorithm predicted the Cobb angle of scoliotic patients with high accuracy.
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