Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.
Zeynab PirayeshHossein Mohammad-RahimiNikoo GhasemiSaeed-Reza MotamedianTerme Sarrafan SadeghiHediye KoohiRata RokhshadShima Moradian LotfiAnahita NajafiShahd A AlajajiZaid H KhouryMaryam JessriAhmed S SultanPublished in: Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology (2024)
Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.