Clinical trial · Observational
Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
NCT06325514CI-TRIAL-00090563completedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (7)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Erythroplakia | — | UNRESOLVED | — |
| Fordyce Granule | — | UNRESOLVED | — |
| Leukoedemas, Oral | — | UNRESOLVED | — |
| Leukoplakia | — | UNRESOLVED | — |
| Lichenoid Reaction | — | UNRESOLVED | — |
| Oral Cancer | Malignant Lip and Oral Cavity Neoplasm | CURATED_BROADER | 0.80 |
| Oral Lichen Planus | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Artificial intelligence based program | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (3)
- label
- normal/variations of normal anatomical landmarks
- description
- 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
- interventionNames
- Diagnostic Test: Artificial intelligence based program
- label
- low risk referral
- description
- 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.
- interventionNames
- Diagnostic Test: Artificial intelligence based program
- label
- high risk referral
- description
- patients that needs referral for a high risk of malignancy or a premalignant disease, such as: oral lichen planus, leukoplakia, erythroplakia, squamous cell carcinoma.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (4)
- BACKGROUNDKhanagar 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. PMID 33384840
- BACKGROUNDVarela-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. PMID 27751768
- BACKGROUNDSeoane 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. PMID 11871397
- BACKGROUNDTanriver 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. PMID 34199471