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Objective To describe the development of a platform for image collection and annotation that resulted in a multi‐sourced international image dataset of oral lesions to facilitate the development of automated lesion classification algorithms. Materials and Methods We developed a web‐interface, hosted on a web server to collect oral lesions images from international partners. Further, we developed a customised annotation tool, also a web‐interface for systematic annotation of images to build a rich clinically labelled dataset. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions. Results The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders and other oral lesions that were collected through MeMoSA® UPLOAD. Eight‐hundred images were annotated by seven oral medicine specialists on MeMoSA®ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3%–100%). Conclusion This is the first description of a database with clinically labelled oral lesions. This database could accelerate the improvement of AI algorithms that can promote the early detection of high‐risk oral lesions.

Purpose To establish an oral lesion image database that could accelerate the development of artificial intelligence systems for lesion recognition and referral decision. Materials and Methods We describe the establishment of a multi-sourced image dataset through the development of a platform for the collection and annotation of images. Further, we developed a used-friendly tool (MeMoSA® ANNOTATE) for systematic annotation to collect a rich dataset associated with the images. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions to identify lesions that are challenging to identify through images alone. Results The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders, benign lesions, normal anatomical variants and normal mucosa that were collected through our platform, MeMoSA® UPLOAD. Over 800 images were annotated by seven oral medicine specialists on MeMoSA®ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3–100%). Conclusion This is the first description of a database with well-annotated oral lesions. This database has already been used for the development of AI algorithm for classifying oral lesions. Further expansion of this database could accelerate the improvement in AI algorithms that can facilitate the early detection of oral potentially malignant disorders and oral cancer.

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