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| Tongue image-based prediction models for diabetes：A systematic review |
| Hits 73 Download times 44 Received:January 17, 2026 |
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| DOI
10.11656/j.issn.1673-9043.2026.06.12 |
| Key Words
diabetes mellitus;tongue diagnosis objective quantification;tongue image;artificial intelligence;machine learning;prediction model;systematic review |
| Author Name | Affiliation | E-mail | | LI Qingqing | School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | | | LIU Baoyu | School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | | | SUN Yumei | School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | | | REN Ying | School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | | | PANG Xiaoli | School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | 403033115@qq.com | | WANG Hongyun | School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China | 411192103@qq.com |
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| Abstract
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| [Objective] To systematically review the research advances in diabetes-related predictive models based on tongue images，summarize their characteristics，technical approaches，and performance，evaluate the quality of existing literature，and provide a reference for future research. [Methods] We systematically searched relevant literature in Chinese National Knowledge Infrastructure（CNKI），Wanfang Data，China Biology Medicine disc（CBM），VIP Database，PubMed，Embase，Web of Science，and Cochrane Library. Two researchers independently screened the literature based on predefined inclusion/exclusion criteria and extracted data. The risk of bias and applicability were assessed using the PROBAST and PROBAST-AI tools. [Results] Seventeen articles were included. The research areas covered diabetes diagnosis，prediction of complication risks，and identification of Traditional Chinese Medicine syndrome patterns. Most models integrated tongue images with clinical indicators to construct multimodal models. Deep learning and ensemble learning were the mainstream techniques，with several models demonstrating excellent discriminatory ability. However，the overall methodological quality was moderate. All studies only conducted internal validation，severely lacking independent external validation and calibration assessment. The vast majority of studies relied on single-center data and lacked standardized procedures，casting doubt on the generalizability of the models. [Conclusion] Research on diabetes predictive models based on tongue images is developing rapidly. While current model performance appears promising，their clinical translation is limited. Future efforts should focus on rigorous study design，standardized information collection，and conducting prospective external validation to promote the clinical application of this technology. |
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