Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

241 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-04-01

Study Completion Date

2024-12-01

Brief Summary

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This study aims to develop an AI program that can classify oral findings into Normal/variation of normal or an oral disease by clinical photos analysis, aiding in lowering the percentages of false positive and false negative diagnosis of oral diseases.

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Detailed Description

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Early diagnosis of oral lesions, particularly oral cancer, is crucial for enhancing prognosis, facilitating early intervention and care with the intention of lowering disease-related mortality.

Since conventional oral examination (COE) is the most used method in identifying oral lesions, the average dental practitioner's experience is a decisive factor in early diagnosis.

Visual examination lacks specificity and sensitivity since its highly subjective. Unfortunately, Studies show that the majority of dentists lack expertise in early detection of the disease, resulting in false negative diagnosis of oral lesions.

General practitioners are found to either delay the referral of a suspected oral lesion to an Oral Medicine specialist, or referring numerous false positive cases, unnecessarily pushing the patients into a state of anxiousness and cancer phobia. False positive referrals overburden the specialists, which will eventually cause delayed diagnosis of true positive cases due to the oversaturation with false positive ones.

diagnostic research scope shifts towards noninvasive, easy chair side methods with higher accuracy for early detection of oral lesions. Recent approaches towards using machine based programs indicate that this machine-learning method may be useful in the detection and diagnosis of oral cancer.

Conditions

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Oral Cancer Oral Lichen Planus Fordyce Granule Leukoplakia Erythroplakia Leukoedemas, Oral Lichenoid Reaction

Study Design

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Observational Model Type

ECOLOGIC_OR_COMMUNITY

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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normal/variations of normal anatomical landmarks

patients that have normal oral findings or variations of normal anatomical landmarks such as: leukoedema, fordyce granules, linea alba, physiological pigmentations, torus palatinus, torus mandibularis, geographic tongue, fissured tongue

Artificial intelligence based program

Intervention Type DIAGNOSTIC_TEST

the AI based program is based on image analysis

low risk referral

patients that needs referral for a low risk of malignant transformation disease, such as: hemangiomas, fibromas, oral apthous ulcers, candidal infections, pemphigus valgaris, petechiae, frictional keratosis, smokers' melanosis.

Artificial intelligence based program

Intervention Type DIAGNOSTIC_TEST

the AI based program is based on image analysis

high risk referral

patients that needs referral for a high risk of malignancy or a premalignant disease, such as: oral lichen planus, leukoplakia, erythroplakia, squamous cell carcinoma.

Artificial intelligence based program

Intervention Type DIAGNOSTIC_TEST

the AI based program is based on image analysis

Interventions

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