Akademik Veri Yönetim Sistemi
15th Biennial Congress of European Association of Oral Medicine (EAOM21), Porto, Portekiz, 24 - 26 Eylül 2021
Introduction: A
computing system that replicates a natural system is known as Artificial Intelligence
(AI). Deep
learning methods have recently been applied for the processing of medical
images, and they have shown promise in a variety of applications
Aim:
This study aimed to develop a deep learning approach for identifying oral lichenoid
lesions using photographic images.
Methods: Anonymous retrospective photographic images of buccal mucosa with
65 healthy and 72 oral lichenoid lesions were identified using CranioCatch
program (CranioCatch, Eskişehir, Turkey). All images were re-checked and
verified by Oral and Maxillofacial Radiology experts. This data set was divided
into training (n =51; n=58), verification (n =7; n=7) and test (n =7; n=7) sets
for healthy mucosa and mucosa with oral lichenoid lesion, respectively. In the study,
an artificial intelligence model was developed using Tensorflow Inception V3
architecture, which is a deep learning approach.
Results:
AI deep learning model provided the classification of all test images for both
healthy and diseased mucosa with a 100% success rate.
Conclusion: In
the health-care business, AI offers a wide range of uses and applications.
Increased effort, increased complexity of job, and probable doctor fatigue may
jeopardize diagnostic abilities and results. Artificial intelligence (AI)
components in imaging equipment would lessen this effort and increase
efficiency.They can also detect oral lesions and have access to more data than
their human counterparts. Our preliminary findings show that deep learning has
the potential to handle this significant challenge.