Artificial Intelligence for early detection of Oral Squamous Cell Carcinoma: A Systematic Review.
OBJECTIVES: This systematic review aimed to synthesize evidence on artificial intelligence (AI) methods for the detection, classification and segmentation of oral squamous cell carcinoma (OSCC), oral potentially malignant disorders (OPMDs) and oral epithelial dysplasia (OED) across imaging, histopathology, spectroscopic and molecular data modalities. Accuracy was the most commonly reported metric (88.1%), followed by specificity (47.6%), sensitivity (42.9%), AUC (35.7%), and F1-score (33.3%).
OBJECTIVES: This systematic review aimed to synthesize evidence on artificial intelligence (AI) methods for the detection, classification and segmentation of oral squamous cell carcinoma (OSCC), oral potentially malignant disorders (OPMDs) and oral epithelial dysplasia (OED) across imaging, histopathology, spectroscopic and molecular data modalities. Accuracy was the most commonly reported metric (88.1%), followed by specificity (47.6%), sensitivity (42.9%), AUC (35.7%), and F1-score (33.3%).
What the study was
- Study design
- Systematic review
- Category
- Diagnostics
- Maturity
- Potentially Practice-Changing
- Journal
- Journal of stomatology, oral and maxillofacial surgery
Why it surfaced
Systematic review in Early cancer detection|AI/ML in clinical diagnostics and imaging; species: mixed; scored 8/10 (novelty=1, relevance=3, design=2, pop=2)
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