DePerio: Deep Learning‐Based Oral Inflammatory Load Quantification for Periodontal Applications
Periodontal disease (PD) is a chronic condition associated with systemic risks like cardiovascular disease and diabetes. Traditional diagnostics detect advanced PD but often miss early‐stage cases, where timely intervention is critical. Oral polymorphonuclear neutrophils (oPMNs) are emerging as key biomarkers for periodontal health. This study presents DePerio, an AI‐driven deep neural network (DNN) method that isolates and quantifies oPMNs from saliva using their natural hydrophilic adhesion on treated surfaces. Trained on thousands of annotated bright‐field images, DePerio accurately detects and counts oPMNs within milliseconds. Validation against standard techniques confirms its precision in measuring oral inflammatory load (OIL). Clinical testing on 51 samples from healthy and periodontitis patients demonstrates DePerio's capability to distinguish five OIL levels, assisting in PD severity assessment. This low‐complexity, AI‐powered tool offers a rapid, reliable approach for early PD detection and management in dental practices.
Authors
- Shahin Ebrahimi (ORCID: https://orcid.org/0000-0002-9873-6352)
- Michael Glogauer (ORCID: https://orcid.org/0000-0002-6248-5977)
- Fatemeh Soheili (ORCID: https://orcid.org/0000-0001-6329-4630)
- Navid Mohaghegh (ORCID: https://orcid.org/0000-0002-2479-5106)
- Mahdi S. M. H. Daneshvar
- Ebrahim Ghafar‐Zadeh (ORCID: https://orcid.org/0000-0002-7090-2304)
- Chunxiang Sun (ORCID: https://orcid.org/0009-0002-8392-7065)
- Negin Masoudifar
- Yasaman Tahernezhad
- Mahdi Amrollahi Biouki
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- York University (CA)
Publication Details
- Journal
- Advanced Intelligent Systems
- Published
- 2025-08-31
- DOI
- https://doi.org/10.1002/aisy.202500357
- Primary Topic
- Oral microbiology and periodontitis research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- York University
- Canada First Research Excellence Fund
- Natural Sciences and Engineering Research Council of Canada