ArticleGlobal spine journal2024
The Comparison of Convolutional Neural Networks and the Manual Measurement of Cobb Angle in Adolescent Idiopathic Scoliosis.
Article in Global spine journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.Spine deformity · 2025Pooled it
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Vitamin-D status in sixty adolescents with idiopathic scoliosis: An observational study.Pakistan journal of medical sciences · 2026Article
- Semi-automated end-to-end quality control system for annotation data based on automated Cobb angle measurement.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
- A fully automatic Cobb angle measurement framework of full-spine DR images based on deep learning.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 · 2025Article
- Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning automates Cobb angle measurement compared with multi-expert observers.BJR artificial intelligence · 2025Article
- Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment.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 · 2024Article
- Current issues in the treatment of adolescent idiopathic scoliosis: a comprehensive narrative review.Asian spine journal · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups.Scientific reports · 2024Article
- Validation of Artificial Intelligence in the Classification of Adolescent Idiopathic Scoliosis and the Compairment to Clinical Manual Handling.Orthopaedic surgery · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designComparative study.
objectiveTo compare manual and deep learning-based automated measurement of Cobb angle in adolescent idiopathic scoliosis.
methodsWe proposed a fully automated framework to measure the Cobb angle of AIS patients. Whole-spine images of 500 AIS individuals were collected. 200 digital radiographic (DR) images were labeled manually as training set, and the remaining 300 images were used to validate by mean absolute error (MAE), Pearson or spearman correlation coefficients, and intra/interclass correlation coefficients (ICCs). The relationship between accuracy of vertebral boundary identification and the subjective image quality score was evaluated.
resultsThe PT, MT, and TL/L Cobb angles were measured by the automated framework within 300 milliseconds. Remarkable 2.92° MAE, .967 ICC, and high correlation coefficient (r = .972) were obtained for the major curve. The MAEs of PT, MT, and TL/L were 3.04°, 2.72°, and 2.53°, respectively. The ICCs of these 3 curves were .936, .977, and .964, respectively. 88.7% (266/300) of cases had a difference range of ±5°, with 84.3% (253/300) for PT, 89.7% (269/300) for MT, and 93.0% (279/300) for TL/L. The decreased bone/soft tissue contrast (2.94 vs 3.26; P=.039) and bone sharpness (2.97 vs 3.35; P=.029) were identified in the images with MAE exceeding 5°.
conclusionThe fully automated framework not only identifies the vertebral boundaries, vertebral sequences, the upper/lower end vertebras and apical vertebra, but also calculates the Cobb angle of PT, MT, and TL/L curves sequentially. The framework would shed new light on the assessment of AIS curvature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.