Oral CancerNCT06325514
Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
An observational study of Oral Cancer and Oral Lichen Planus, run by Cairo University at 1 site in Egypt.
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Oral Cancer, Oral Lichen Planus, Fordyce Granule
- What is being tested?
- Artificial intelligence based program
- Type of treatment
- Pill or oral medicine
- How many people
- 241 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Apr 2024to 1 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Patients above 18 years old
- Candidates with normal oral cavity findings
- Candidates with variations of oral cavity findings
- Candidates with different oral lesions
Who cannot join
- Patients less than 18 years old
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Oral Cancer, Oral Lichen Planus, Fordyce Granule, Leukoplakia, Erythroplakia, Leukoedemas, Oral
- Being tested
- Artificial intelligence based program
- Medical subject headings
- Mouth Neoplasms, Lichen Planus, Oral, Leukoplakia, Erythroplasia, Leukoedema, Oral, Lichenoid Eruptions
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence based programDiagnostic test
the AI based program is based on image analysis
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Diagnostic Test: Artificial intelligence based program | |
| 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. Receives: Diagnostic Test: Artificial intelligence based program | |
| 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. Receives: Diagnostic Test: Artificial intelligence based program |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 3 months to develop the program
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Mar 2024
- Study start1 Apr 2024
- Primary completion1 Oct 2024
- Study completion1 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Oral CancerStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Ecologic or community
- Time perspective
- Cross-sectional
- Enrolment
- 241 participants (actual)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Noran Ayman Mohammad Ismael Abdel-Moaty, Cairo University
- Investigators
- Noran AM AbdelMoaty, MsC, Principal investigator, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06325514
- Sponsor's protocol ID
- NA2024
- Status last verified
- Jun 2025
- First submitted
- 16 Mar 2024
- Last updated
- 4 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 33384840Varela-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.
PubMed 27751768Seoane 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.
PubMed 11871397Tanriver 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.
PubMed 34199471
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
- ClinicalTrials.gov ID
- NCT06325514
- Acronym / sponsor ID
- NA2024
- Conditions
- Oral Cancer, Oral Lichen Planus, Fordyce Granule, Leukoplakia, Erythroplakia, Leukoedemas, Oral
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 241 participants (actual)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 1 Apr 2024 (actual)
- Primary completion
- 1 Oct 2024 (actual)
- Study completion
- 1 Dec 2024 (actual)
- First posted
- 22 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Ecologic or community
- Time perspective
- Cross-sectional
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| normal/variations of normal anatomical landmarks | – | Diagnostic Test: Artificial intelligence based programpatients 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 |
| low risk referral | – | Diagnostic Test: Artificial intelligence based programpatients 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. |
| high risk referral | – | Diagnostic Test: Artificial intelligence based programpatients that needs referral for a high risk of malignancy or a premalignant disease, such as: oral lichen planus, leukoplakia, erythroplakia, squamous cell carcinoma. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- Patients above 18 years old
- Candidates with normal oral cavity findings
- Candidates with variations of oral cavity findings
- Candidates with different oral lesions
Key exclusion criteria (1 listed)
- Patients less than 18 years old
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence based programthe AI based program is based on image analysis | Diagnostic test | high risk referral, low risk referral, normal/variations of normal anatomical landmarks |
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2025.
- Background: PubMed 33384840Khanagar 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.
- Background: PubMed 27751768Varela-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.
- Background: PubMed 11871397Seoane 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.
- Background: PubMed 34199471Tanriver 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Noran AM AbdelMoaty, MsC principal investigator
Cairo University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Oral Cancer and Oral Lichen Planus, run by Cairo University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
risk stratification (time frame: 3 months to develop the program). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06325514, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jun 2025.