Clinical trial NCT06325514: Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
Official title: The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Oral Cancer
Oral Lichen Planus
Fordyce Granule
Leukoplakia
Erythroplakia
Leukoedemas, Oral
Lichenoid Reaction
Lead sponsor: Cairo University
Sponsor class: OTHER
First posted: 2024-03-22
Enrollment: 241
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: ECOLOGIC_OR_COMMUNITY
Time perspective: CROSS_SECTIONAL
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.

Brief 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: risk stratification
timeFrame: 3 months to develop the program
Secondary outcomes:
Other outcomes:
Locations:
city: Cairo
country: Egypt
facility: Faculty of dentistry, CairoU
Study officials:
affiliation: Cairo University
name: Noran AM AbdelMoaty, MsC
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
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.
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.
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.