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Radiomics and Machine Learning in Early Detection of Oral Potentially Malignant Disorders.

Ashwini Dhopte¹, Vikas Singh², J Sophia J Priya³

¹Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India.

Journal of Pharmacy & Bioallied Sciences

|January 12, 2026

Summary

Early detection of oral potentially malignant disorders (OPMDs) is crucial. Radiomics and machine learning (ML) show promise for improving diagnostic accuracy and survival rates in OPMD detection.

Keywords:

Imaging modalities machine learning oral potentially malignant disorders oral squamous cell carcinoma radiomics

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Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies

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Published on: August 11, 2023

Area of Science:

Oral medicine
Radiology
Artificial intelligence

Background:

Oral potentially malignant disorders (OPMDs) pose a significant risk of progressing to oral squamous cell carcinoma (OSCC).
Early diagnosis of OPMDs is critical for improving patient outcomes and survival rates.
Traditional diagnostic methods can be limited in accuracy and timeliness.

Purpose of the Study:

To review the application of radiomics and machine learning (ML) in the early detection of OPMDs.
To highlight the potential of these technologies to enhance diagnostic accuracy.
To discuss current research, challenges, and future directions in this field.

Main Methods:

Extraction of quantitative features from medical images using radiomics.
Application of machine learning algorithms for pattern recognition and predictive modeling.
Analysis of various imaging modalities and data processing techniques relevant to OPMDs.

Main Results:

Radiomics and ML demonstrate significant potential in improving the accuracy of OPMD detection.
These technologies can aid in identifying subtle changes indicative of malignant transformation.
Quantitative imaging analysis offers new avenues for risk stratification.

Conclusions:

Radiomics and ML are promising tools for the early detection and diagnosis of OPMDs.
Further research and validation are needed to fully integrate these technologies into clinical practice.
Advancements in AI and imaging hold the key to improved OPMD management and patient survival.