Recent Advance in Diagnostic Oral Medicine – A Review

Oral medicine plays a pivotal role in diagnostic decision-making and the field has witnessed significant advancements over time, revolutionizing the way oral diseases are diagnosed and managed. Recent advancements in diagnostic oral medicine offer promising solutions for early detection and accurate diagnosis. These innovations include the use of salivary biomarkers, advanced imaging technologies, and artificial intelligence (AI). Early detection is crucial for improving patient outcomes. Saliva-based biomarkers, such as miRNAs and proteins, provide a non-invasive method for early detection. Imaging techniques like auto fluorescence imaging, Raman spectroscopy, and optical coherence tomography enhances the visualization of abnormal tissues. AI-powered tools, particularly deep learning algorithms, can analyze images and data to improve diagnostic accuracy. By combining these technologies, we can achieve earlier detection, more accurate diagnosis, and personalized treatment plans for oral cancer patients.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-27
DOI
https://doi.org/10.5281/zenodo.14558001
Primary Topic
Oral Health Pathology and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Recent Advance in Diagnostic Oral Medicine – A Review

Zenodo (CERN European Organization for Nuclear Research)
Oral Health Pathology and Treatment
article

Recent Advance in Diagnostic Oral Medicine – A Review

article en

Abstract

Oral medicine plays a pivotal role in diagnostic decision-making and the field has witnessed significant advancements over time, revolutionizing the way oral diseases are diagnosed and managed. Recent advancements in diagnostic oral medicine offer promising solutions for early detection and accurate diagnosis. These innovations include the use of salivary biomarkers, advanced imaging technologies, and artificial intelligence (AI). Early detection is crucial for improving patient outcomes. Saliva-based biomarkers, such as miRNAs and proteins, provide a non-invasive method for early detection. Imaging techniques like auto fluorescence imaging, Raman spectroscopy, and optical coherence tomography enhances the visualization of abnormal tissues. AI-powered tools, particularly deep learning algorithms, can analyze images and data to improve diagnostic accuracy. By combining these technologies, we can achieve earlier detection, more accurate diagnosis, and personalized treatment plans for oral cancer patients.

Zenodo (CERN European Organization for Nuclear Research)
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Oral Health Pathology and Treatment
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