Rajendran, Senthilmani and Lim, Jian Han and Yogalingam, Kohgulakuhan and Kallarakkal, Thomas George and Zain, Rosnah and Jayasinghe, Ruwan Duminda and Rimal, Jyotsna and Kerr, Alexander Ross and Amtha, Rahmi and Patil, Karthikeya and Welikala, Roshan Alex and Lim, Ying Zhi and Remagnino, Paolo and Gibson, John and Tilakaratne, Wanninayake Mudiyanselage and Liew, Chee Sun and Yang, Yi-Hsin and Barman, Sarah Ann and 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 1354-523X, DOI https://doi.org/10.1111/odi.14206.
Full text not available from this repository.Abstract
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 (R) UPLOAD. Eight-hundred images were annotated by seven oral medicine specialists on MeMoSA (R) 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.
| Item Type: | Article |
|---|---|
| Funders: | UK Research & Innovation (UKRI) Medical Research Council UK (MRC) [Grant No: MR/S013865/1] |
| Uncontrolled Keywords: | Access to care; Annotation tool; Oral cancer; Oral lesion image database; Oral potentially malignant disorders |
| Subjects: | R Medicine > RK Dentistry R Medicine > RK Dentistry > Oral surgery |
| Divisions: | Faculty of Dentistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 10 Nov 2025 06:41 |
| Last Modified: | 10 Nov 2025 06:41 |
| URI: | http://eprints.um.edu.my/id/eprint/49553 |
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