Evaluation of Dental Plaque Area with Artificial Intelligence Model.

OBJECTIVES: This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth. MATERIALS AND METHODS: In this study, photographs of 168 teeth belonging to 20 patients aged between 10 and 15 years, who met our criteria, were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent. To train the AI system to identify plaque on teeth with dental plaque that is not discolored, plaque and teeth were marked on photos with exposed dental plaque. One hundred forty teeth were used to construct the training group, while 28 teeth were used to create the test group. Another dentist reviewed images of teeth with dental plaque that was not discolored, and the effectiveness of AI in detecting plaque was evaluated using pertinent performance indicators. To compare the AI model and the dentist's evaluation outcomes, the mean intersection over union (IoU) values were evaluated by the Wilcoxon test. RESULTS: The AI system showed higher performance in our study with a precision of 82% accuracy, 84% sensitivity, 83% F1 score, 87% accuracy, and 89% specificity in plaque detection. The area under the curve (AUC) value was found to be 0.922, and the IoU value was 76%. Subsequently, the dentist's plaque diagnosis performance was also evaluated. The IoU value was 0.71, and the AUC was 0.833. The AI model showed statistically significantly higher performance than the dentist (P < 0.05). CONCLUSIONS: The AI algorithm that we developed has achieved promising results and demonstrated clinically acceptable performance in detecting dental plaque compared to a dentist.

Is BECLIN-1 Immunoreactivity More Effective than HBME-1 in Diagnosis of Papillary Thyroid Cancer?

BACKGROUND: Even though morphologic findings are generally important in the diagnosis of thyroid tumors, in some cases, morphologic similarities between benign and malignant lesions lead to noticeable differences in evaluation and different diagnoses in the same cases. In our study, we researched whether the autophagy-related protein Beclin-1 (BECN1) is a diagnostic marker in thyroid tumors, as well as its correlation with HBME-1, which has high sensitivity and specificity in distinguishing malignant thyroid lesions. METHODS: Samples from 136 patients that received a thyroidectomy were fixed in 10% formalin. All cases had hematoxylin & eosin (H&E) stains available for review and paraffin blocks for immunohistochemical staining. In immunochemistry tests, BECN1, HBME-1, and Ki-67 were studied. RESULTS: BECN1 immunoreactivity rates were found to be 98.9% in papillary thyroid carcinoma (PTC), 57.1% in follicular carcinoma (FC), and 21.4% in follicular adenoma (FA). HBME-1 immunoreactivity was 100% in PTC, 85.7% in FC, and 64% in FA. In thyroid carcinomas, BECN1 was as effective as HBME-1 as a marker for the diagnosis of malignancy. CONCLUSIONS: We revealed an important role of autophagy in thyroid carcinogenesis, as evidenced by the high rate of BECN1 immunoreactivity in PTC and FC. Moreover, we found that autophagy plays a more important role in PTC, as evidenced by the high immunoreactivity rates. According to our results, BECN1 is a more specific marker than HBME-1 in PTC and has a higher correlation with Ki-67. In routine studies, BECN1 will be more helpful than HBME-1 in the diagnosis of PTC.