Diagnostic accuracy of tongue diagnosis for stroke syndrome differentiation in traditional Chinese medicine: a systematic review, meta-analysis, and narrative synthesis of artificial intelligence-assisted models.
6 affiliations
- 1School of Acupuncture, Moxibustion and Tuina, Anhui University of Chinese Medicine, Hefei, Anhui, China.
- 2School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, Anhui, China.
- 3The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
- 4The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
- 5Department of Clinical Medicine, North Anhui Health Vocational College, Suzhou, Anhui, China.
- 6First Clinical Medical College, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Abstract
Background
Tongue examination is central to traditional Chinese medicine (TCM) syndrome differentiation after stroke, but its diagnostic accuracy has not been evaluated using contemporary diagnostic test accuracy (DTA) methods.Methods
We searched PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP from inception to 23 February 2026 (PROSPERO: CRD420261288502). Track 1 studies of manual tongue diagnosis were pooled using bivariate or univariate random-effects models; Track 2 studies of artificial intelligence (AI)-assisted models were synthesized narratively according to SWiM. Risk of bias was assessed with QUADAS-2 and the certainty of Track 1 evidence with GRADE-DTA.Results
Ten studies from mainland China included 3,437 participants: six manual-diagnosis studies (N = 2,191) and four AI-assisted studies (N = 1,246). Because evidence certainty was very low and reference-standard independence was not assured, all estimates were treated as exploratory agreement with existing syndrome-classification processes rather than clinically validated diagnostic accuracy. The Deficient-Cold Tongue cluster showed pooled specificity of 0.97 (95% CI: 0.87-1.00) and a positive likelihood ratio (PLR) of 31.0 (95% CI: 2.8-341.6). The Greasy Coating cluster showed pooled sensitivity of 0.83 (95% CI: 0.52-0.96), specificity of 0.80 (95% CI: 0.68-0.88), PLR of 4.1 (95% CI: 2.5-6.8), and a negative likelihood ratio of 0.21 (95% CI: 0.06-0.71). Blood Stasis Tongue showed low sensitivity and unstable, heterogeneous estimates. The small analyzable evidence base contrasted with the much larger descriptive literature.Conclusion
Current evidence remains too uncertain to support manual tongue diagnosis for routine or standalone clinical use, and applicability is largely limited to mainland Chinese TCM stroke-care settings. Manual and AI-assisted approaches require prospective multicenter validation using independent syndrome adjudication that excludes the index tongue feature. Future studies should stratify analyses by stroke etiology and report calibration and clinical utility for AI models.Systematic review registration
PROSPERO, CRD420261288502.
Related
- Artificial Intelligence-Based Burn Assessment Demonstrates High Diagnostic Accuracy: Systematic Review and Meta-Analysis
- From underdiagnosis to scalable screening: Applications of artificial intelligence for vertebral compression fracture detection – a systematic review and meta-analysis of diagnostic performance
- Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis
- Artificial Intelligence Versus Human Interpretation in Diagnostic Radiology: A Systematic Review of Diagnostic Accuracy and Operational Efficiency.
- Artificial Intelligence Versus Manual Measurement of Spinopelvic Sagittal Parameters Across Preoperative, Postoperative, and Instrumented Spines: A Systematic Review of Diagnostic Accuracy, Failure Rates, and Inferred Surgical Planning Implications.
BibTeX
@article{huang2026diagnostic,
title = {{Diagnostic accuracy of tongue diagnosis for stroke syndrome differentiation in traditional Chinese medicine: a systematic review, meta-analysis, and narrative synthesis of artificial intelligence-assisted models.}},
author = {Yibo Huang and Jiashun Yin and Siyuan Shao and Pengyu Jiang and Zilong Wang and Hui Xu},
journal = {Frontiers in neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1874937},
url = {https://europepmc.org/article/MED/42908634},
note = {CC BY},
}
RIS
TY - JOUR
TI - Diagnostic accuracy of tongue diagnosis for stroke syndrome differentiation in traditional Chinese medicine: a systematic review, meta-analysis, and narrative synthesis of artificial intelligence-assisted models.
AU - Yibo Huang
AU - Jiashun Yin
AU - Siyuan Shao
AU - Pengyu Jiang
AU - Zilong Wang
AU - Hui Xu
PY - 2026
DA - 2026/10/07
JO - Frontiers in neurology
DO - 10.3389/fneur.2026.1874937
UR - https://europepmc.org/article/MED/42908634
AB - Background Tongue examination is central to traditional Chinese medicine (TCM) syndrome differentiation after stroke, but its diagnostic accuracy has not been evaluated using contemporary diagnostic test accuracy (DTA) methods.Methods We searched PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP from inception to 23 February 2026 (PROSPERO: CRD420261288502). Track 1 studies of manual tongue diagnosis were pooled using bivariate or univariate random-effects models; Track 2 studies of artificial intelligence (AI)-assisted models were synthesized narratively according to SWiM. Risk of bias was assessed with QUADAS-2 and the certainty of Track 1 evidence with GRADE-DTA.Results Ten studies from mainland China included 3,437 participants: six manual-diagnosis studies (N = 2,191) and four AI-assisted studies (N = 1,246). Because evidence certainty was very low and reference-standard independence was not assured, all estimates were treated as exploratory agreement with existing syndrome-classification processes rather than clinically validated diagnostic accuracy. The Deficient-Cold Tongue cluster showed pooled specificity of 0.97 (95% CI: 0.87-1.00) and a positive likelihood ratio (PLR) of 31.0 (95% CI: 2.8-341.6). The Greasy Coating cluster showed pooled sensitivity of 0.83 (95% CI: 0.52-0.96), specificity of 0.80 (95% CI: 0.68-0.88), PLR of 4.1 (95% CI: 2.5-6.8), and a negative likelihood ratio of 0.21 (95% CI: 0.06-0.71). Blood Stasis Tongue showed low sensitivity and unstable, heterogeneous estimates. The small analyzable evidence base contrasted with the much larger descriptive literature.Conclusion Current evidence remains too uncertain to support manual tongue diagnosis for routine or standalone clinical use, and applicability is largely limited to mainland Chinese TCM stroke-care settings. Manual and AI-assisted approaches require prospective multicenter validation using independent syndrome adjudication that excludes the index tongue feature. Future studies should stratify analyses by stroke etiology and report calibration and clinical utility for AI models.Systematic review registration PROSPERO, CRD420261288502.
SN - 1664-2295
ER -