ArticleGlobal spine journal2026
Fully Automated Deep Learning-Based Lenke Classification for Adolescent Idiopathic Scoliosis Using Multi-View Full-Spine Radiographs: Development and Clinical Validation.
Article in Global spine journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Study DesignRetrospective validation study.ObjectivesTo develop and validate a fully automated deep learning framework for radiographic measurement and Lenke classification in adolescent idiopathic scoliosis (AIS) using multi-view full-spine radiographs.MethodsConsecutive patients with AIS from 3 hospitals who underwent standardized 4-view full-spine radiography between January 2019 and January 2026 were retrospectively reviewed. The automated framework incorporated vertebral detection, vertebra-level landmark estimation, radiographic parameter computation, and rule-based Lenke classification. Expert manual measurements and consensus Lenke classification served as the reference standard. Performance was assessed on an independent test set using detection and keypoint metrics, Cobb angle measurement agreement, classification accuracy, clinician agreement, and workflow efficiency.ResultsIn 76 independent test cases, the framework achieved a vertebral detection AP@0.75 of 0.858 and an overall keypoint AP of 0.951. Cobb angle measurement showed a mean absolute error of 2.4°, with excellent agreement with expert measurements (ICC = 0.991; Pearson correlation = 0.987; Spearman correlation = 0.983). Accuracy was 0.895 for Lenke curve type, 0.868 for lumbar modifier, 0.908 for sagittal thoracic modifier, and 0.816 for the complete Lenke label. Agreement between the AI system and the senior surgeon was high (
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.