Image collection and annotation platforms to establish a multi‐source database of oral lesions

Rajendran, Senthilmani, Lim, Jian Han, Yogalingam, Kohgulakuhan, Kallarakkal, Thomas George, Zain, Rosnah Binti, Jayasinghe, Ruwan Duminda, Rimal, Jyotsna, Kerr, Alexander Ross, Amtha, Rahmi, Patil, Karthikeya, Welikala, Roshan Alex, Lim, Ying Zhi, Remagnino, Paolo, Gibson, John, Tilakaratne, Wanninayake Mudiyanselage, Liew, Chee Sun, Yang, Yi‐Hsin, Barman, Sarah Ann, Chan, Chee Seng and Cheong, Sok Ching (2023) Image collection and annotation platforms to establish a multi‐source database of oral lesions. Oral Diseases, 29(5), pp. 2230-2238. ISSN (print) 1354-523X

Abstract

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. 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%). 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.

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