AI-Guided Histopathological and Molecular Phenotyping of Salivary Glands in Sjögren's Disease: Subtyping, Mechanistic Insights, and Translational Implications-A Systematic Review and Structured Functional Synthesis.
- 1Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
- 2Biomedical Stomatology Research Group, Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia U de A, Medellín 050010, Colombia.
- 3Faculty of Dentistry, Institución Universitaria Visión de las Américas, Medellín 050040, Colombia.
Abstract
Background/objectives
Salivary-gland biopsy is central to Sjögren's disease (SjD) assessment, but conventional measures incompletely capture glandular heterogeneity. This systematic review evaluated artificial intelligence (AI)/machine learning (ML) applications to SjD salivary-gland histopathology and molecular profiling, including biological and translational validation.Methods
PubMed, Scopus, and Embase were searched. Eligible studies used salivary-gland tissue as the primary analytical substrate or provided substantive glandular validation of systemic AI/ML findings. Evidence was synthesized functionally because of methodological heterogeneity.Results
Twenty-five studies were included. AI/ML identified histopathological and molecular phenotypes spanning immune/interferon activation, tissue architecture and immune-cell organization, epithelial stress and metabolic/cell-death remodeling, and translational response. Only a limited subset progressed beyond computational or tissue-level confirmation to functional perturbation, pharmacological testing, in vivo validation, or human treatment-response assessment. Recurrent public-dataset reuse, small cohorts, heterogeneous pipelines, and limited independent validation constrained reproducibility.Conclusions
AI/ML supports multidimensional SjD glandular phenotyping and shows emerging translational potential, but demonstrated clinical or therapeutic utility remains unestablished. Prospective multicenter validation is required before AI-derived glandular phenotypes can inform routine patient stratification or therapeutic decision-making.
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BibTeX
@article{ardila2026aiguided,
title = {{AI-Guided Histopathological and Molecular Phenotyping of Salivary Glands in Sjögren's Disease: Subtyping, Mechanistic Insights, and Translational Implications-A Systematic Review and Structured Functional Synthesis.}},
author = {Carlos M Ardila and Eliana Pineda-Vélez and Anny M Vivares-Builes},
journal = {Medical sciences (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/medsci14060677},
url = {https://europepmc.org/article/MED/42930596},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Guided Histopathological and Molecular Phenotyping of Salivary Glands in Sjögren's Disease: Subtyping, Mechanistic Insights, and Translational Implications-A Systematic Review and Structured Functional Synthesis.
AU - Carlos M Ardila
AU - Eliana Pineda-Vélez
AU - Anny M Vivares-Builes
PY - 2026
DA - 2026/10/15
JO - Medical sciences (Basel, Switzerland)
DO - 10.3390/medsci14060677
UR - https://europepmc.org/article/MED/42930596
AB - Background/objectives Salivary-gland biopsy is central to Sjögren's disease (SjD) assessment, but conventional measures incompletely capture glandular heterogeneity. This systematic review evaluated artificial intelligence (AI)/machine learning (ML) applications to SjD salivary-gland histopathology and molecular profiling, including biological and translational validation.Methods PubMed, Scopus, and Embase were searched. Eligible studies used salivary-gland tissue as the primary analytical substrate or provided substantive glandular validation of systemic AI/ML findings. Evidence was synthesized functionally because of methodological heterogeneity.Results Twenty-five studies were included. AI/ML identified histopathological and molecular phenotypes spanning immune/interferon activation, tissue architecture and immune-cell organization, epithelial stress and metabolic/cell-death remodeling, and translational response. Only a limited subset progressed beyond computational or tissue-level confirmation to functional perturbation, pharmacological testing, in vivo validation, or human treatment-response assessment. Recurrent public-dataset reuse, small cohorts, heterogeneous pipelines, and limited independent validation constrained reproducibility.Conclusions AI/ML supports multidimensional SjD glandular phenotyping and shows emerging translational potential, but demonstrated clinical or therapeutic utility remains unestablished. Prospective multicenter validation is required before AI-derived glandular phenotypes can inform routine patient stratification or therapeutic decision-making.
SN - 2076-3271
ER -