Oral Potentially Malignant DisordersAID-OraLNCT07529769
Artificial Intelligence for the Diagnosis of Oral Lesions
An observational study of Oral Potentially Malignant Disorders and Oral Cavity Squamous Cell Carcinoma, run by Assistance Publique - Hôpitaux de Paris.
Trial summary
Squamous cell carcinomas of the upper aerodigestive tract are among the most common cancers worldwide, with the oral cavity being the most frequent site. Oral cavity squamous cell carcinomas (OCSCC) represent a major cause of morbidity and mortality, mainly due to high rates of locoregional or metastatic recurrence… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Oral Potentially Malignant Disorders, Oral Cavity Squamous Cell Carcinoma
- How many people
- 5,000 participants
- Where
- No sites listed yet
- Study dates
- Apr 2026to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- No healthy volunteers
- Adult, Older adult
Study population: Patients followed in the Department of Oral Mucosal Pathology (Maxillofacial Surgery and Stomatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris) from January 1, 1970, to December 31, 2023, with a diagnosis of potentially malignant oral lesions and/or oral cavity cancer.
Who may be able to join
- Patient aged ≥ 18 years
- Patients followed up in the Oral Mucosa Pathology Department (Maxillofacial Surgery and Stomatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris) between January 1, 1970, and December 31, 2023, with a diagnosis of potentially malignant oral lesion and/or oral cavity cancer.
Who cannot join
- Photograph of the lesion unavailable (in standard care)
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Squamous cell carcinomas of the upper aerodigestive tract are among the most common cancers worldwide, with the oral cavity being the most frequent site. Oral cavity squamous cell carcinomas (OCSCC) represent a major cause of morbidity and mortality, mainly due to high rates of locoregional or metastatic recurrence and the frequent occurrence of second primary tumors. Unlike oropharyngeal squamous cell carcinomas, human papillomavirus (HPV) is not involved in the carcinogenesis of OCSCC.
In some cases, OCSCC develop from oral potentially malignant disorders (OPMDs), such as leukoplakia and erythroplakia, which have a worldwide incidence of 3-5%. The malignant transformation rate of OPMDs ranges from 3% to 50%, reflecting their marked heterogeneity. Although several clinical, histological, and molecular factors have been proposed to identify patients at high risk of malignant transformation, none have demonstrated sufficient clinical utility to date. In other cases, OCSCC arise from clinically normal oral mucosa in patients with OPMDs located at a distance and/or with established risk factors, particularly tobacco and alcohol use.
Read more
Currently, no chemopreventive or preventive strategy has been established as a standard of care to prevent malignant transformation of OPMDs. Improving the prognosis of OCSCC therefore requires the development of tools to better identify high-risk OPMDs and to enable the earliest possible diagnosis. Early detection of OPMDs is essential for secondary prevention of OCSCC. However, conventional oral examination based on visual inspection and palpation has limited sensitivity, and clinical recognition of OPMDs remains challenging. Consequently, there is a clear need for improved methods to enhance early detection and risk stratification of OPMDs.
Main objective:
To develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI). This tool appears promising in meeting the current needs of the oral cavity practitioner community.
- Keywords
- Oral Potentially Malignant Disorders, Oral Cavity Squamous Cell Carcinoma, diagnosis, oral cancer, Artificial Intelligence
- Medical subject headings
- Squamous Cell Carcinoma of Head and Neck, Disease, Mouth Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI)
To develop a tool to aid in the diagnosis of cancerous lesions in theoral cavity using Artificial Intelligence (AI). This tool appears promisingin meeting the current needs of the oral cavity practitioner community.
To achieve this objective, anonymized clinical photographs of oral lesions, along with relevant clinical data routinely recorded in medical charts, will be collected.
All photographs will undergo retrospective review by experienced specialists in oral and maxillofacial surgery. The experts will independently assess the images and establish a reference diagnosis. In cases of disagreement, a consensus diagnosis will be reached.
The complete dataset, including image data, associated clinical variables, and reference diagnoses, will be used to develop and internally validate a machine learning algorithm for the automated classification of oral lesions
Measured: Through study completion, an average of 9 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Apr 2026
- Study startApr 2026 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Oral Potentially Malignant DisordersStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- BPIfrance, Institut Universitaire de Cancérologie, Sorbonne University, Health Data Hub (France)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07529769
- Sponsor's protocol ID
- APHP251103
- Other IDs
- CEth-SCOMFS 003/2025
- Acronym
- AID-OraL
- Status last verified
- Apr 2026
- First submitted
- 5 Feb 2026
- Last updated
- 14 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 14 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for the Diagnosis of Oral Lesions
- ClinicalTrials.gov ID
- NCT07529769
- Acronym / sponsor ID
- AID-OraL / APHP251103
- Keywords
- Oral Potentially Malignant Disorders, Oral Cavity Squamous Cell Carcinoma, diagnosis, oral cancer, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- BPIfrance, Institut Universitaire de Cancérologie, Sorbonne University, Health Data Hub (France)
- Study start
- Apr 2026 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 14 Apr 2026 (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.
Squamous cell carcinomas of the upper aerodigestive tract are among the most common cancers worldwide, with the oral cavity being the most frequent site. Oral cavity squamous cell carcinomas (OCSCC) represent a major cause of morbidity and mortality, mainly due to high rates of locoregional or metastatic recurrence and the frequent occurrence of second primary tumors. Unlike oropharyngeal squamous cell carcinomas, human papillomavirus (HPV) is not involved in the carcinogenesis of OCSCC.
In some cases, OCSCC develop from oral potentially malignant disorders (OPMDs), such as leukoplakia and erythroplakia, which have a worldwide incidence of 3-5%. The malignant transformation rate of OPMDs ranges from 3% to 50%, reflecting their marked heterogeneity. Although several clinical, histological, and molecular factors have been proposed to identify patients at high risk of malignant transformation, none have demonstrated sufficient clinical utility to date. In other cases, OCSCC arise from clinically normal oral mucosa in patients with OPMDs located at a distance and/or with established risk factors, particularly tobacco and alcohol use.
Currently, no chemopreventive or preventive strategy has been established as a standard of care to prevent malignant transformation of OPMDs. Improving the prognosis of OCSCC therefore requires the development of tools to better identify high-risk OPMDs and to enable the earliest possible diagnosis. Early detection of OPMDs is essential for secondary prevention of OCSCC. However, conventional oral examination based on visual inspection and palpation has limited sensitivity, and clinical recognition of OPMDs remains challenging. Consequently, there is a clear need for improved methods to enhance early detection and risk stratification of OPMDs.
Main objective:
To develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI). This tool appears promising in meeting the current needs of the oral cavity practitioner community.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients followed in the Department of Oral Mucosal Pathology (Maxillofacial Surgery and Stomatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris) from January 1, 1970, to December 31, 2023, with a diagnosis of potentially malignant oral lesions and/or oral cavity cancer.
Key inclusion criteria (2 listed)
- Patient aged ≥ 18 years
- Patients followed up in the Oral Mucosa Pathology Department (Maxillofacial Surgery and Stomatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris) between January 1, 1970, and December 31, 2023, with a diagnosis of potentially malignant oral lesion and/or oral cavity cancer.
Key exclusion criteria (1 listed)
- Photograph of the lesion unavailable (in standard care)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI)
Time frame: Through study completion, an average of 9 months
To develop a tool to aid in the diagnosis of cancerous lesions in theoral cavity using Artificial Intelligence (AI). This tool appears promisingin meeting the current needs of the oral cavity practitioner community. To achieve this objective, anonymized clinical photographs of oral lesions, along with relevant clinical data routinely recorded in medical charts, will be collected. All photographs will undergo retrospective review by experienced specialists in oral and maxillofacial surgery. The experts will independently assess the images and establish a reference diagnosis. In cases of disagreement, a consensus diagnosis will be reached. The complete dataset, including image data, associated clinical variables, and reference diagnoses, will be used to develop and internally validate a machine learning algorithm for the automated classification of oral lesions
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Apr 2026.
- Individual participant dataSharing planned: Yes
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Oral Potentially Malignant Disorders and Oral Cavity Squamous Cell Carcinoma, run by Assistance Publique - Hôpitaux de Paris.
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 2 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: opening soon, as of the sponsor's last update on 14 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jebrane BOUAOUD, MD, PhD (33142161301, jebrane.bouaoud@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI) (time frame: Through study completion, an average of 9 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 14 Apr 2026.