Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health

TL;DR

This study presents an automated machine learning-based process that analyzes intraoral fluorescent images to detect periodontal disease and its correlation with systemic health conditions, highlighting the importance of oral health in overall health assessments.

Contribution

The paper introduces a novel automated imaging and machine learning approach for correlating periodontal disease with systemic health, validated on a large dataset of intraoral images.

Findings

Machine learning classifier achieved an AUC of 0.677 in segmenting periodontal inflammation.

Periodontal disease correlated with optic nerve abnormalities and systemic health indicators.

The method enables automated diagnosis and systemic health screening using oral images.

Abstract

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indicating a learned association between disease signatures in collected images. Periodontal diseases were more prevalent among males (p=0.0012) and older subjects (p=0.0224) in the screened population. Physicians independently examined the collected images, assigning localized modified gingival indices (MGIs). MGIs and periodontal disease were then cross-correlated with responses to a medical history questionnaire, blood pressure and body mass index measurements, and optic nerve, tympanic membrane, neurological, and cardiac rhythm imaging examinations. Gingivitis and early periodontal disease were associated with subjects diagnosed with optic nerve abnormalities (p <0.0001) in their retinal scans. We also report significant co-occurrences of periodontal disease in subjects reporting swollen joints (p=0.0422) and a family history of eye disease (p=0.0337). These results indicate cross-correlation of poor periodontal health with systemic health outcomes and stress the importance of oral health screenings at the primary care level. Our screening process and analysis method, using images and machine learning, can be generalized for automated diagnoses and systemic health screenings for other diseases.