Title |
Digital pathology: Revolutionizing oral and maxillofacial diagnostics |
Authors |
Mrunali Ghanasham Gharat1, Sneha Masne Deshpande1,*, Swati Dhone2,
Vibhuti Shreesh |
Affiliation |
1Department of Oral Pathology and Microbiology Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Belapur, Navi Mumbai - 400614, Maharashtra, India; 2Department of Pediatric and Preventive Dentistry, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Belapur, Navi Mumbai - 400614, Maharashtra, India; 3Department of Oral and Maxillofacial Pathologist, Oral Pathology and Microbiology, YMT Dental College and Hospital, Navi Mumbai, Maharashtra, India; 4Department of Prosthodontics, Crown and Bridge, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Belapur, Navi Mumbai, Maharashtra - 400614, India; 5Department of Conservative Dentistry & Endodonotics, Bharati Vidyapeeth Deemed to be University Dental College and Hospital, Navi Mumbai, Maharashtra - 400614, India; *Corresponding author |
|
Mrunali Ghanasham Gharat - E - mail: mrunalgj1912@gmail.com
|
Article Type |
Research Article
|
Date |
Received December 1, 2024; Revised December 31, 2024; Accepted December 31, 2024, Published December 31, 2024 |
Abstract |
Digital pathology (DP) has revolutionized oral and maxillofacial pathology (OMP) by enhancing diagnostic accuracy and efficiency, leading to improved patient outcomes. Therefore, it is of interest to report on the potential of Whole Slide Imaging (WSI), Artificial Intelligence (AI) and telepathology in OMP, highlighting their role in facilitating remote consultations and automated image analysis. AI and Machine Learning (ML) have further advanced cancer diagnosis by improving pattern recognition and predictive accuracy. While DP offers numerous benefits, challenges such as data management and ethical considerations remain. Future research should explore ways to further integrate DP into OMP practice. |
Keywords |
Computational pathology, digital slide scanning, machine learning in pathology, maxillofacial diagnostic precision.
|
Citation |
Gharat et al. Bioinformation 20(12): 1834-1840 (2024)
|
Edited by |
P Kangueane
|
ISSN |
0973-2063
|
Publisher |
|
License |
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
|
|