Oral cancer remains a serious global health concern due to its high morbidity and mortality rates, primarily caused by late-stage diagnosis. The presence of oral potentially malignant disorders (OPMDs) provides an opportunity for early intervention, as these lesions precede the development of oral squamous cell carcinoma. However, the accurate detection and classification of OPMDs remain challenging due to their diverse clinical presentations. Conventional diagnostic methods, including visual examination and histopathological analysis, have limitations such as subjectivity, invasiveness, and high dependency on expert interpretation. In recent years, artificial intelligence (AI) and deep learning (DL) have emerged as promising tools in medical imaging, offering automated, objective, and efficient diagnostic capabilities.
Deep Learning in the Diagnosis of OPMDs
Various deep learning models, particularly convolutional neural networks (CNNs), have been applied to different imaging modalities to improve the diagnosis of OPMDs. These models have demonstrated expert-level accuracy in detecting and classifying OPMDs using clinical photographic images, autofluorescence images, exfoliative cytology, histopathology, and optical coherence tomography (OCT) images.
Deep Learning in Prognostic Prediction of OPMDs
Beyond diagnosis, AI models are being utilized to predict the likelihood of malignant transformation in OPMDs. Machine learning techniques, including random forest classifiers and survival models such as DeepSurv, have been used to integrate clinical, histopathological, and imaging data to assess cancer risk. These models provide individualized risk assessments, aiding in clinical decision-making and patient management.
Challenges and Future Directions
Despite its potential, the application of deep learning in OPMD diagnosis and prognosis faces several challenges. These include the need for large, standardized image datasets, variability in image quality, and algorithm limitations such as overfitting and interpretability issues. Future research should focus on developing multimodal AI systems that integrate imaging, molecular, and clinical data for more accurate and personalized diagnosis and prognosis of OPMDs.
Conclusion
Deep learning has demonstrated significant potential in improving the diagnosis and prognosis of OPMDs through various imaging modalities. AI-driven approaches offer a noninvasive, cost-effective, and objective means to enhance early detection, ultimately improving patient outcomes. As AI technology continues to advance, its integration into clinical workflows may revolutionize the management of OPMDs and oral cancer prevention.
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https://www.xiahepublishing.com/2835-3315/CSP-2024-00025
The study was recently published in the Cancer Screening and Prevention .
Cancer Screening and Prevention (CSP) publishes high-quality research and review articles related to cancer screening and prevention. It aims to provide a platform for studies that develop innovative and creative strategies and precise models for screening, early detection, and prevention of various cancers. Studies on the integration of precision cancer prevention multiomics where cancer screening, early detection and prevention regimens can precisely reflect the risk of cancer from dissected genomic and environmental parameters are particularly welcome.
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Cancer Screening and Prevention
Deep Learning in the Diagnosis and Prognosis of Oral Potentially Malignant Disorders
23-Dec-2024