AI-powered classification of oral pre-cancer: a histopathological image approach
A high fatality rate characterises the prognosis outlook for oral cancer due to delayed diagnosis and significantly hinders the advancements in early detection techniques. Traditional histological investigation, a cornerstone of precancerous lesion diagnosis, faces substantial challenges in terms of labour intensity in manually processing tissue samples, is time-consuming and the inferences are mostly guided by microscopic investigation. Therefore, a system is proposed for oral dysplasia grading from histopathological tissues using machine learning. Distinguished from other methodologies, this approach incorporates a comprehensive array of features, not limited to cell nuclei properties but also morphometric analysis of epithelial stratification and enhancements in digital pathology. Moreover, early pre-cancer grading enhances the survival chances of patients by identifying abnormalities that might be missed through conventional examination. The study presented here has an overall accuracy of 94%, considering the limited training cases available under each feature. This approach would benefit both doctors and patients by streamlining the diagnostic process and improving its outcomes. The fundamentals of oral cancer surgical management, when it comes to risk factors, regional predispositions, treatment response, and outcome, oral cavity squamous cell carcinoma (OSCC) is a heterogeneous illness. Surgery is the main treatment option for oral malignancies, even though non-surgical treatment is used in other head and neck subsites. Adjuvant treatment, such as radiation or chemoradiation, is then administered based on the risk factors of the final histopathology. Before beginning treatment, a multidisciplinary tumor board must discuss each patient and develop a treatment strategy. This chapter aims to explain the fundamental surgical concepts used in the reconstruction of defects and the management of various oral cavity subsites. The surgical management the technical process of brain arteriovenous malformation (bAVM) excision, patient selection for surgery, and perioperative care that optimizes the likelihood of the best result are all included in surgical treatment.
Authors
- Marcella Bellani (ORCID: https://orcid.org/0000-0003-1727-5396)
- R Priya
- G. Devanand Venkatasubbu (ORCID: https://orcid.org/0000-0002-3486-892X)
- Sharmila Sengupta (ORCID: https://orcid.org/0000-0003-1126-1049)
- Anuj Bagad
- Aayush Talreja
- Dr Harsha Karwa
Institutions
- Government Dental College and Hospital (IN)
- Vivekananda Institute of Medical Sciences (IN)
Publication Details
- Journal
- International Journal of Basic and Applied Sciences
- Published
- 2025-05-23
- DOI
- https://doi.org/10.14419/8c1tmt79
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00