Akademik Veri Yönetim Sistemi
JOURNAL OF THE COLLEGE OF PHYSICIANS AND SURGEONS--PAKISTAN : JCPSP, cilt.35, ss.1-7, 2025 (SCI-Expanded)
Objective: To assess the effectiveness of deep learning-based diagnostic software in identifying oral leukoplakia lesions from intraoral patient photographs.
Study Design: Observatinal study
Place and Duration of the Study: Marmara University, Faculty of Dentistry, İstanbul-Türkiye , from February to November 2024
Methodology: A dataset comprising 222 anonymized retrospective intraoral images—confirmed as oral leukoplakia via incisional biopsy—was labeled using the polygonal annotation tool in CranioCatch software. Expert reviewers validated all annotations. The dataset was split into training (n = 178), validation (n = 22), and test (n = 22) subsets. The models used for lesion detection included YOLOv5, YOLOv8, Mask R-CNN, and U-Net. The model's effectiveness was measured using the confusion matrix. Precision, Recall, and F1 Scores provided quantitative assessments of the model's predictive capabilities.
Results: YOLOv5 demonstrated the highest performance with an F1 score of 0.845, sensitivity of 0.967, and precision of 0.750. YOLOv8 followed closely with an F1 score of 0.760, sensitivity of 0.871, and precision of 0.675. In contrast, Mask R-CNN and U-Net showed comparatively lower effectiveness, achieving F1, sensitivity, and precision values of 0.408, 0.588, and 0.312 for Mask R-CNN, and 0.629, 0.518, and 0.800 for U-Net, respectively.
Conclusion: YOLOv5 and YOLOv8 models outperformed Mask R-CNN and U-Net in accurately detecting oral leukoplakia lesions. These findings highlight the promise of artificial intelligence in facilitating early diagnosis of potentially malignant oral conditions. As datasets expand and algorithms improve, the clinical utility of such AI systems is expected to grow, contributing to timely intervention and improved patient outcomes.
Keywords: Oral mucosal lesions, Oral leukoplakia, Deep learning, Artificial intelligence
Objective: To assess the effectiveness of deep learning-based diagnostic software in identifying oral leukoplakia lesions from intraoral patient photographs.
Study Design: Observatinal study
Place and Duration of the Study: Marmara University, Faculty of Dentistry, İstanbul-Türkiye , from February to November 2024
Methodology: A dataset comprising 222 anonymized retrospective intraoral images—confirmed as oral leukoplakia via incisional biopsy—was labeled using the polygonal annotation tool in CranioCatch software. Expert reviewers validated all annotations. The dataset was split into training (n = 178), validation (n = 22), and test (n = 22) subsets. The models used for lesion detection included YOLOv5, YOLOv8, Mask R-CNN, and U-Net. The model's effectiveness was measured using the confusion matrix. Precision, Recall, and F1 Scores provided quantitative assessments of the model's predictive capabilities.
Results: YOLOv5 demonstrated the highest performance with an F1 score of 0.845, sensitivity of 0.967, and precision of 0.750. YOLOv8 followed closely with an F1 score of 0.760, sensitivity of 0.871, and precision of 0.675. In contrast, Mask R-CNN and U-Net showed comparatively lower effectiveness, achieving F1, sensitivity, and precision values of 0.408, 0.588, and 0.312 for Mask R-CNN, and 0.629, 0.518, and 0.800 for U-Net, respectively.
Conclusion: YOLOv5 and YOLOv8 models outperformed Mask R-CNN and U-Net in accurately detecting oral leukoplakia lesions. These findings highlight the promise of artificial intelligence in facilitating early diagnosis of potentially malignant oral conditions. As datasets expand and algorithms improve, the clinical utility of such AI systems is expected to grow, contributing to timely intervention and improved patient outcomes.
Keywords: Oral mucosal lesions, Oral leukoplakia, Deep learning, Artificial intelligence