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

March 25, 2024 updated by: Noran Ayman Mohammad Ismael Abdel-Moaty, Cairo University

The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Observational

Enrollment (Estimated)

241

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

patients should be above 18 years old, with no maximum age limit. Normal oral cavity findings, variations of oral anatomical landmarks, patients with oral lesions are all included in the study.

Description

Inclusion Criteria:

  • Patients above 18 years old
  • Candidates with normal oral cavity findings
  • Candidates with variations of oral cavity findings
  • Candidates with different oral lesions

Exclusion Criteria:

• Patients less than 18 years old

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
Diagnostic test: Artificial intelligence based program
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.
Diagnostic test: Artificial intelligence based program
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.
Diagnostic test: Artificial intelligence based program
the AI based program is based on image analysis

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
risk stratification
Time Frame: 3 months to develop the program
patient is either normal with no risk or need for referral, low risk of malignant transformation disease, high risk of malignant transformation disease.
3 months to develop the program

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Cairo University

Investigators

  • Principal Investigator: Noran AM AbdelMoaty, MsC, Cairo University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

April 1, 2024

Primary Completion (Estimated)

August 1, 2024

Study Completion (Estimated)

September 1, 2024

Study Registration Dates

First Submitted

March 16, 2024

First Submitted That Met QC Criteria

March 16, 2024

First Posted (Actual)

March 22, 2024

Study Record Updates

Last Update Posted (Actual)

March 26, 2024

Last Update Submitted That Met QC Criteria

March 25, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • NA2024

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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