Evidence map›Paper›PMID 42192639›Full record

ArticleBMJ open2026

Efficacy of artificial intelligence-based digital therapeutics versus traditional Schroth exercises for adolescent idiopathic scoliosis: protocol for a randomised controlled trial.

Ruisi Ma, Ziqi Huang, Xiaowen Zhu, Xiaoyu Ma, Wanru Cheng, Di Tang, Jinde Liu, Lili Shu

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07341633 (Efficacy of Artificial Intelligence-Based Digital Therapeutics Versus Traditional Schroth Exercises for Adolescent Idiopathic Scoliosis), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT07341633 naactive not recruitingnot on this map

Efficacy of Artificial Intelligence-Based Digital Therapeutics Versus Traditional Schroth Exercises for Adolescent Idiopathic Scoliosis: A Randomized Controlled Trial

TypeinterventionalSponsorJinan University GuangzhouRan2026 to 2028Enrolled300ConditionsAdolescent Idiopathic Scoliosis (AIS)ArmsAI-Based Digital Therapeutic System, Standard Outpatient Schroth Therapy
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ruisi Ma *School of Physical Education, Jinan University, Guangzhou, China.ORCID http://orcid.org/0000-0002-0460-7243
Ziqi Huang *School of Physical Education, Jinan University, Guangzhou, China.
Xiaowen Zhu *School of Nursing, Jinan University, Guangzhou, China.
Xiaoyu MaSchool of Physical Education, Jinan University, Guangzhou, China.
Wanru ChengSchool of Physical Education, Jinan University, Guangzhou, China.
Di TangFaculty of Medicine, The Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong, China.
Jinde LiuFaculty of Physical Education, Fudan University, Shanghai, China.
Lili ShuDepartment of Pediatric Orthopedics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China 719393008@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAdolescent idiopathic scoliosis (AIS) requires long-term conservative management to prevent curve progression. While physiotherapeutic scoliosis-specific exercises, specifically the Schroth method, are considered the gold standard for conservative treatment, their clinical efficacy is often limited by accessibility barriers, high costs and suboptimal treatment adherence. This study aims to evaluate the efficacy of a novel artificial intelligence (AI)-based digital therapeutic system, which uses computer vision for remote, personalised posture analysis and adaptive exercise prescription compared with traditional outpatient Schroth therapy. METHODS AND ANALYSIS: This parallel-group randomised controlled trial will be conducted at Guangzhou Women and Children's Medical Center (Guangzhou, China). 300 adolescents aged 10-18 years with AIS who present with a Cobb angle between 10° and 30° and a Risser sign of 0-2 will be recruited and randomised in a 1:1 ratio into an intervention group and a control group. The intervention group will use a smartphone application to capture standardised bi-weekly images. These images will be processed by an AI algorithm to classify curve patterns and assign personalised exercise modules with adaptive dosing ranging from maintenance to high-intensity levels. The control group will receive standard outpatient Schroth care. The primary outcome is the absolute change in the major curve Cobb angle from baseline to 6 months. Secondary outcomes include the angle of trunk rotation, trunk appearance perception, Scoliosis Research Society-22 Revised (SRS-22r) quality-of-life scores and adherence rates. Statistical analysis will follow the intention-to-treat principle using linear mixed models to account for repeated measures. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Medical Ethics Committee of Guangzhou Women and Children's Medical Center (Guangzhou, China) (approval no. [2025]497A01). Written informed assent and consent will be obtained from participants and their legal guardians respectively. Results will be disseminated through peer-reviewed journals and international conferences. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT07341633.

Indexed as

Artificial IntelligenceExercise TherapyScoliosisAdolescentChildChinaFemaleHumansMobile ApplicationsRandomized Controlled Trials as TopicSmartphoneTreatment OutcomeAdolescentArtificial IntelligenceDigital TechnologyRandomized Controlled Trial

Identifiers

PMID42192639
PMCPMC13202051

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.