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

Oral Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06325514
• Other study ID #: NA2024
• Status: Not yet recruiting
• Start date: April 1, 2024
• Est. completion date: September 1, 2024

Study information

• Verified date: March 2024
• Source: Cairo University
• Contact: Noran A AbdelMoaty, MsC
• Phone: +201063021131
• Email: noran.ayman[@]dentistry.cu.edu.eg
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 241
• Est. completion date: September 1, 2024
• Est. primary completion date: August 1, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Erythroplakia
• Leukoplakia
• Lichen Planus
• Lichen Planus, Oral
• Mouth Neoplasms
• Oral Cancer
• Oral Lichen Planus

Intervention

Diagnostic Test:
• Artificial intelligence based program
the AI based program is based on image analysis

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Cairo University

References & Publications (4)

Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021 Jan;16(1):508-522. doi: 10.1016/j.jds.2020.06.019. Epub 2020 Jun 30. — View Citation

Seoane Leston JM, Aguado Santos A, Varela-Centelles PI, Vazquez Garcia J, Romero MA, Pias Villamor L. Oral mucosa: variations from normalcy, part I. Cutis. 2002 Feb;69(2):131-4. — View Citation

Tanriver G, Soluk Tekkesin M, Ergen O. Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders. Cancers (Basel). 2021 Jun 2;13(11):2766. doi: 10.3390/cancers13112766. — View Citation

Varela-Centelles P, Lopez-Cedrun JL, Fernandez-Sanroman J, Seoane-Romero JM, Santos de Melo N, Alvarez-Novoa P, Gomez I, Seoane J. Key points and time intervals for early diagnosis in symptomatic oral cancer: a systematic review. Int J Oral Maxillofac Surg. 2017 Jan;46(1):1-10. doi: 10.1016/j.ijom.2016.09.017. Epub 2016 Oct 15. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	risk stratification	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