Abstract
This study introduces a SMARTphone-based, expert annotated dataset of Oral Mucosa images (SMART-OM), collected to facilitate the development of Artificial Intelligence and Machine Learning (AI/ML) technologies for automated diagnosis of Oral Cancer (OC) and Oral Potentially Malignant Disorders (OPMD). The dataset consists of 2,469 images from 331 subjects from four distinct classes: healthy/normal, variations from normal, OPMD, and OC. The images are captured using Android and iOS smartphone cameras under real-world clinical conditions in visible light. Each image is annotated by expert dental surgeons using the open-source VGG image annotator. Elaborate patient metadata, including clinical diagnosis, age, sex, and lifestyle-based risk indicators such as smoking, smokeless tobacco usage, alcohol consumption, and areca nut chewing, are recorded via a customized Jotform. The data collection and handling procedures are adhered to the ethical guidelines outlined in the Declaration of Helsinki and its amendments for research involving human subjects, with informed consent obtained from each subject. The SMART-OM dataset is intended to advance research and development of AI/ML algorithms for automated oral lesion detection.
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Data availability
The SMART-OM dataset has been deposited in the Figshare repository and can be accessed here15 https://doi.org/10.6084/m9.figshare.31341790.
Code availability
The GitHub repository containing the codes for technical analysis, model training and inference, as well as hyperparameter tuning, can be accessed at https://github.com/Anwesh2000/SMART_OM_Dataset_Technical_Validation32.
References
Rai, P. et al. Oral Cancer in Asia-a systematic review. Advances in Oral and Maxillofacial Surgery 8, 100366 (2022).
Sankaranarayanan, R., Ramadas, K., Amarasinghe, H., Subramanian, S. & Johnson, N., Oral cancer: prevention, early detection, and treatment. Cancer: disease control priorities. 3rd ed. Washington, DC: The International Bank for Reconstruction and Development/The World Bank, 3, 85-99 (2015).
Jain, A. K. Oral cancer screening: insights into epidemiology, risk factors, and screening programs for improved early detection. Cancer Screening and Prevention 3(2), 97–105 (2024).
Mira, E. S. et al. Early diagnosis of oral cancer using image processing and Artificial intelligence. Fusion: Practice & Applications, 14(1) (2024).
Chaudhary, N. et al. High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma. Scientific Data 11(1), 1050 (2024).
Talwar, V. et al. AI-assisted screening of oral potentially malignant disorders using smartphone-based photographic images. Cancers 15(16), 4120 (2023).
Di Fede, O., Panzarella, V., Buttacavoli, F., La Mantia, G. & Campisi, G. Doctoral: A smartphone-based decision support tool for the early detection of oral potentially malignant disorders. Digital Health 9, 20552076231177141 (2023).
Dixit, S., Kumar, A. & Srinivasan, K. A current review of machine learning and deep learning models in oral cancer diagnosis: recent technologies, open challenges, and future research directions. Diagnostics 13(7), 1353 (2023).
Song, B. et al. Bayesian deep learning for reliable oral cancer image classification. Biomedical Optics Express 12(10), 6422–6430 (2021).
Fu, Q. et al. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study. EClinicalMedicine, 27 (2020).
Sengupta, N., Sarode, S. C., Sarode, G. S. & Ghone, U. Scarcity of publicly available oral cancer image datasets for machine learning research. Oral Oncology 126, 105737 (2022).
Barot, S. Oral cancer (lips and tongue) images. https://www.kaggle.com/datasets/shivam17299/oral-cancer-lips-and-tongue-images (2020).
Piyarathne, N. S. et al. A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders. Oral Oncology 156, 106946 (2024).
Dutta, A. & Zisserman, A. The VIA annotation software for images, audio and video. In Proceedings of the 27th ACM international conference on multimedia 2276-2279 (2019).
P D, Madan Kumar et al. SMART-OM: A SMARTphone based expert annotated dataset of Oral Mucosa images. figshare. Dataset, https://doi.org/10.6084/m9.figshare.31341790.v1 (2026).
Radford, A. et al. Learning transferable visual models from natural language supervision. In International conference on machine learning. 8748-8763. PmLR (2021).
Wiggins, W. F. & Tejani, A. S. On the opportunities and risks of foundation models for natural language processing in radiology. Radiology: Artificial Intelligence 4(4), e220119 (2022).
Kirillov, A. et al. Segment anything. In Proceedings of the IEEE/CVF international conference on computer vision 4015-4026 (2023).
Azad, B. et al. Foundational models in medical imaging: A comprehensive survey and future vision. arXiv preprint arXiv:2310.18689 (2023).
Ito FA et al. Standardization in Oral Photography. In Clinical Decision-Making in Oral Medicine: A Concise Guide to Diagnosis and Treatment pp. 11–16. Cham: Springer International Publishing (2023).
Lin, I., Datta, M., Laronde, D. M., Rosin, M. P. & Chan, B. Intraoral photography recommendations for remote risk assessment and monitoring of oral mucosal lesions. international dental journal 71(5), 384–389 (2021).
Rajendran, S. et al. Image collection and annotation platforms to establish a multi‐source database of oral lesions. Oral Diseases 29(5), 2230–2238 (2023).
Casaglia, A., De Dominicis, P., Arcuri, L., Gargari, M. & Ottria, L. Dental photography today. Part 1: basic concepts. ORAL & implantology 8(4), 122 (2016).
Momin, S. et al. Comparison of image quality, color accuracy, and resolution in intraoral photography using digital single lens reflex camera and smartphone cameras: A pilot study. Journal of Dental Sciences. (2025).
Piemonte, E. D., Gilligan, G. M., Costa, M. F. G. & Lazos, J. P. How to improve photographs with smartphones for oral telemedicine. Exploration of Digital Health Technologies 2(5), 249–258 (2024).
Shahrul, A. I., Shukor, N. & Norman, N. H. Technique for orthodontic clinical photographs using a smartphone. International Journal of Dentistry 2022(1), 2811684 (2022).
Ferreira, C. D. A. P., Pereira, F. D. A. V., Rodrigues, M. V. B., Cabral, J. L. D. O. A. & de Campos Tuña, I. T. Art and science of dental photography: suggested photographic protocol with cellular device. Observatório. De La Economía Latinoamericana 22(8), e6167–e6167 (2024).
Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. Preprint at arXiv:2010.11929 (2020).
He, K., Zhang, X., Ren, S. & Sun, J., Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778 (2016).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at arXiv:1409.1556 (2014).
Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, 6105-6114. PMLR (2019).
SMART-OM Dataset Technical Validation, https://github.com/Anwesh2000/SMART_OM_Dataset_Technical_Validation Accessed on 08-10-2025.
Acknowledgements
The authors acknowledge the use of AI-assisted tools to aid in rephrasing sections of the manuscript. This study is part of an Indian Council of Medical Research (ICMR), India project (Project ID IIRP-2023-1049) funded by Small Extramural Grants – 2023.
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P.D.M.K., K.R., C.L., and S.R. contributed to data acquisition, data interpretation, image annotation, drafting, expert validation, and revision of the manuscript. A.N. performed technical validation and was responsible for model development, training, and evaluation of the deep learning models. R.K., R.B.D., and S.S.B. contributed to the conceptualization of the technical framework and study design, provided overall mentoring, and were involved in drafting and critical revision of the manuscript. All authors reviewed and approved the final manuscript.
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Madan Kumar, P.D., Ranganathan, K., Lavanya, C. et al. A Smartphone-based Comprehensive Dataset of Annotated Oral Cavity Images for Enhanced Oral Disease Diagnosis. Sci Data (2026). https://doi.org/10.1038/s41597-026-06954-5
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DOI: https://doi.org/10.1038/s41597-026-06954-5
