ReviewSpine deformity2026
A scoping review on the application of new technology in the screening and diagnosis of adolescent idiopathic scoliosis.
Review in Spine deformity, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAdolescent Idiopathic Scoliosis (AIS) affects 2-4% of adolescents globally. Current screening methods face challenges, including high false-positive rates, underscoring the need for safer, more accurate alternatives to improve early detection and reduce unnecessary imaging. This scoping review evaluates Artificial Intelligence (AI) applications in non-radiological AIS screening and diagnosis, aiming to (1) identify current AI models, (2) assess performance metrics, (3) examine validation methods, and (4) evaluate feasibility for large-scale implementation in current healthcare settings. MATERIALS AND
methodsFollowing PRISMA-ScR guidelines, 16 studies (2001-2025) were analyzed from PubMed, Scopus, and Web of Science. Inclusion criteria encompassed studies using AI with non-radiological inputs (RGB images, 3D scans, depth maps). Data on model types, performance metrics, validation, and clinical implications were synthesized.
resultsConvolutional Neural Networks (CNNs; 71.4%) predominated, followed by Support Vector Machines (21.4%) and Artificial Neural Networks (7.14%). AI models achieved a weighted mean accuracy of 91.1% (range: 72.4-97.5%) for curve classification and Cobb angle prediction errors of 5.5° ± 3.5°. For diagnosing scoliosis (curves > 10°), models achieved ≥ 87.5% sensitivity (up to 97.4% with advanced models) and specificity ≥ 83.5% (up to 99% with high-precision algorithms). DISCUSSION AND
conclusionsAI-based non-radiological AIS screening demonstrates high diagnostic accuracy and offers a promising alternative to traditional methods by providing accessible, multi-output assessments using smartphone cameras or 3D scanners. Key limitations include small sample sizes, inconsistent validation, and ethical concerns (e.g., data privacy and automation bias). Future research should prioritize randomized controlled trials, standardized protocols, and multi-level stakeholder engagement to facilitate clinical integration.
Indexed as
Identifiers
42581262What 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.