The goal of this clinical trial is to learn if smartphone-based deep learning system works to accurately detect oral potentially malignant disorders and oral cancer in adults. It will also learn about if it is as effective as assessments conducted by dentists and non-certified health provider.
We expect that the deep learning system will have higher sensitivity in detecting oral potentially malignant disorders and oral cancer, where as the dentists and non-certified health providers will exhibit higher specificity in screening.
Participants will be grouped into three arms: deep learning system (arm A) or board-certified dentist with deep learning system (arm B) or non-certified health providers (general practitioners) with deep learning system (arm C).
Oral cancer risk factors, such as habits of smoking or having chewed betel nut or alcohol drinking, would be recorded by anonymous questionnaires.
Adult patients (age ≥18) visiting cancer screening center
Unable to cooperate to fully open mouth/ navigate tongue
Unable to cooperate for the assessment
Parallel
Smartphone-based deep learning system
DeviceThe smartphone-based deep learning system was trained using a dataset of over 50,000 white-light macroscopic images collected between 2006 and 2013 to develop the YOLOv7 model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red).
Effectiveness and accuracy
The primary outcome is the sensitivity and specificity for the three referral grades (green, yellow and red) by the deep learning system, dentists and non-certified health providers. The area under the curve (AUC) for each receiver operating characteristic (ROC) curve will also be calculated.
Questionnaire
The secondary outcome is subjects' feedback of comfortability during exam evaluated by the visual analog scale (VAS) (a score out of 10). The time needed for screening will also be recorded for the assessment of efficiency.
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