Scholarlink

Background <p>Oral cancer remains a major global health issue, with timely diagnosis being essential due to its varied clinical presentation. This study explores how artificial intelligence (AI) can support early detection by analyzing intraoral photographs.</p> Methods <p>A cross-sectional analysis was performed using 518 intraoral clinical images collected from the Department of Oral Medicine, Kerman Faculty of Dentistry, between 2009 and 2023. The dataset comprised 104 images of malignant lesions and 414 of benign or normal tissue, all confirmed by a specialist in oral pathology. Three pretrained deep learning models, DenseNet-121, EfficientNet-B0, and ResNet-50, were evaluated for their ability to classify lesions as malignant or benign. The data were split into training (80%) and testing (20%) sets, with preprocessing completed before analysis.</p> Results <p>Among the models, DenseNet-121 demonstrated superior performance, achieving 91% accuracy, 75% sensitivity, 98% specificity, 75% positive predictive value, 96% negative predictive value, an F1 score of 84%, and an area under the curve of 90%. These results exceeded the diagnostic accuracy of an experienced oral specialist.</p> Conclusion <p>AI-based analysis of clinical images can significantly improve early oral cancer detection and should be integrated into clinical workflows to enhance diagnostic precision.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

“Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification”

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

查看全文
  • Shima Soleimani Sardou,
  • Mohammad Mehdi Ghaemi,
  • Fatemeh Sadat Rezvaninejad,
  • Abolfazl Seyrfar,
  • Arash Shahravan,
  • Nader Navabi,
  • Raziyehsadat Rezvaninejad

摘要

Background

Oral cancer remains a major global health issue, with timely diagnosis being essential due to its varied clinical presentation. This study explores how artificial intelligence (AI) can support early detection by analyzing intraoral photographs.

Methods

A cross-sectional analysis was performed using 518 intraoral clinical images collected from the Department of Oral Medicine, Kerman Faculty of Dentistry, between 2009 and 2023. The dataset comprised 104 images of malignant lesions and 414 of benign or normal tissue, all confirmed by a specialist in oral pathology. Three pretrained deep learning models, DenseNet-121, EfficientNet-B0, and ResNet-50, were evaluated for their ability to classify lesions as malignant or benign. The data were split into training (80%) and testing (20%) sets, with preprocessing completed before analysis.

Results

Among the models, DenseNet-121 demonstrated superior performance, achieving 91% accuracy, 75% sensitivity, 98% specificity, 75% positive predictive value, 96% negative predictive value, an F1 score of 84%, and an area under the curve of 90%. These results exceeded the diagnostic accuracy of an experienced oral specialist.

Conclusion

AI-based analysis of clinical images can significantly improve early oral cancer detection and should be integrated into clinical workflows to enhance diagnostic precision.