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Assessment of the Mouth

Assessment of the Mouth

A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review.

Michele Nieri¹, Lapo Serni¹, Tommaso Clauser²

¹Department of Experimental and Clinical Medicine, University of Florence, Italy.

|March 31, 2025

Summary

Deep learning models demonstrate comparable diagnostic accuracy to human experts for oral cancer detection. These AI tools outperformed medical students in sensitivity, highlighting their potential in clinical settings.

Keywords:

artificial intelligence deep learning oral cancer oral medicine oral pathology

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Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Oral cancer diagnosis relies on expert interpretation of mucosal lesions.
Deep learning (DL) models offer potential for automated image analysis.
Direct comparisons between DL and human experts are crucial for clinical adoption.

Purpose of the Study:

To directly compare the diagnostic accuracy of deep learning models against human experts and other methods for oral cancer detection.
To evaluate the performance of DL models in identifying oral mucosal lesions from photographic images.

Main Methods:

A systematic review and Bayesian meta-analysis of comparative diagnostic studies.
Inclusion criteria: studies using DL methods on oral mucosal lesion images (cancer/non-cancer).
Databases searched: Medline, EMBASE, Scopus, Google Scholar, ClinicalTrials.gov; bias assessed using QUADAS-C.

Main Results:

Eight studies were included; none had a low risk of bias.
DL models showed comparable sensitivity and specificity to human experts.
DL models outperformed postgraduate medical students in sensitivity (difference: 0.108).

Conclusions:

Deep learning models exhibit diagnostic accuracy comparable to human experts for oral cancer.
AI models show promise, outperforming less experienced clinicians (medical students).
Further prospective clinical trials are essential to validate real-world DL performance.