Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders and Oral Cancer Screening
NCT06862414
Cancer Screening, Head and Neck Neoplasms
Taipei, Taiwan
View Trial DetailsNCT Number: NCT07529769
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.
Trial opening soon.
Get Notified18 year and older
All sexes
Observational
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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
Contact information is provided by the study sponsor or research team.
Jebrane BOUAOUD, MD, PhD
CONTACT
Jinmi BAEK
CONTACT
Assistance Publique - Hôpitaux de Paris
Other
Acronym: AID-OraL
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