Comparison of artificial intelligence vs. junior dentists' diagnostic performance based on caries and periapical infection detection on panoramic images.

Background: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists. Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists. Conclusions: Regarding the evaluation time needed, AI performed faster, on average.

Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs.

OBJECTIVE: This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms. STUDY DESIGN: The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system. In order to examine the relationship between the number of data used in image processing algorithms and model performance, four different datasets which include 1000, 1500, 2000 and 2500 panoramic X-rays, were used. The training of the models was carried out with the YOLOv4 algorithm and trained models were tested on a fixed test dataset with 500 data and compared based on F1 score, mAP, sensitivity, precision and recall metrics. RESULTS: The performance of the model increased as the number of data used during the training of the model increased. Therefore, the last model trained with 2500 data showed the highest success among all the trained models. CONCLUSION: Dataset size is important for dental enumeration, and large samples should be considered as more reliable.

Deep Learning for Diagnostic Charting on Pediatric Panoramic Radiographs.

Artificial intelligence (AI) based systems are used in dentistry to make the diagnostic process more accurate and efficient. The objective of this study was to evaluate the performance of a deep learning program for detection and classification of dental structures and treatments on panoramic radiographs of pediatric patients. In total, 4821 anonymized panoramic radiographs of children aged between 5 and 13 years old were analyzed by YOLO V4, a CNN (Convolutional Neural Networks) based object detection model. The ability to make a correct diagnosis was tested samples from pediatric patients examined within the scope of the study. All statistical analyses were performed using SPSS 26.0 (IBM, Chicago, IL, USA). The YOLOV4 model diagnosed the immature teeth, permanent tooth germs and brackets successfully with the high F1 scores like 0.95, 0.90 and 0.76 respectively. Although this model achieved promising results, there were certain limitations for some dental structures and treatments including the filling, root canal treatment, supernumerary tooth. Our architecture achieved reliable results with some specific limitations for detecting dental structures and treatments. Detection of certain dental structures and previous dental treatments on pediatric panoramic x-rays by using a deep learning-based approach may provide early diagnosis of some dental anomalies and help dental practitioners to find more accurate treatment options by saving time and effort.

The association between sleep quality, depression, anxiety and stress levels, and temporomandibular joint disorders among Turkish dental students during the COVID-19 pandemic.

OBJECTIVE: This study aimed to examine the association between sleep quality, depression, anxiety and stress levels, and the frequency of temporomandibular disorders in a sample of Turkish dental students during the COVID-19 pandemic. METHODS: The current cross-sectional study was conducted with 699 dental university students during the COVID-19 pandemic. Fonseca Anamnestic Index (FAI), Pittsburgh Sleep Quality Index (PSQI), and Depression Anxiety Stress Scale-21 (DASS-21) were used in the present study. RESULTS: The incidence of temporomandibular joint disorders in the present study was found to be 77.5%. Female students' FAI scores were found to be statistically significantly higher than males (p < 0.05). Additionally, higher depression and anxiety and stress levels caused increased PSQI and FAI scores. CONCLUSION: During the COVID-19 pandemic, increased temporomandibular joint disorders were observed with increased impaired sleep quality and higher depression, anxiety and stress levels among dental university students.

Quality of information on YouTube about artificial intelligence in dental radiology.

OBJECTIVES: This study was designed to investigate Artificial Intelligence in Dental Radiology (AIDR) videos on YouTube in terms of popularity, content, reliability, and educational quality. METHODS: Two researchers systematically searched about AIDR on YouTube on January 27, 2020, by using the terms "artificial intelligence in dental radiology," "machine learning in dental radiology," and "deep learning in dental radiology." The search was performed in English, and 60 videos for each keyword were assessed. Video source, content type, time since upload, duration, and number of views, likes, and dislikes were recorded. Video popularity was reported using Video Power Index (VPI). The accuracy and reliability of the source of information were measured using the adapted DISCERN score. The quality of the videos was measured using JAMAS and modified Global Quality Score (mGQS) and content via Total Concent Evaluation (TCE). RESULTS: There was high interobserver agreement for DISCERN (intraclass correlation coefficient [ICC]: 0.975; 95% confidence interval [CI]: 0.957-0.985; P: 0.000; P < 0.05) and mGQS (ICC: 0.904; 95% CI: 0.841-0.943; P: 0.000; P < 0.05). Academic source videos had higher DISCERN, GQS, and TCE, revealing both reliability and quality. Also, positive relationship of VPI with mGQS (30.1%) (P: 0.035) and DISCERN (38.1%) (P: 0.007) is detected. The scores revealed 51.9% relationship between mGQS and DISCERN (P: 0.001); and educational quality predictor scores revealed 62.5% relationship between TCE and GQS (P: 0.000). CONCLUSION: Despite the limited number of relevant videos, YouTube involves reliable and quality videos that can be used by dentists about learning AIDR.

Comparative determination of skeletal maturity by hand-wrist radiograph, cephalometric radiograph and cone beam computed tomography.

OBJECTIVES: The purpose of this study is to assess the stages of skeletal maturity in cone beam computed tomography (CBCT), hand-wrist radiography (HWR) and cephalometric radiography (CR) techniques of orthodontic patients, and associate skeletal maturity stages with chronological age, in a Turkish subpopulation. METHODS: Hand-wrist radiographs, cephalometric radiographs and CBCT of 105 patients were evaluated. For evaluation of HWR, the "Hand Bone Age A Digital Atlas of Skeletal Maturity" of Vicente Gilsanz and Osman Ratib (2005) was used. Skeletal maturation in the cephalometric radiographs and sagittal sections of cervical vertebrae obtained by CBCT were evaluated with Hassel and Farman's method (1995). All results were re-evaluated 3 weeks later to assess intra-observer reliability. RESULTS: Intra-observer reliability coefficients of the skeletal maturity stages in HWR, CR, and CBCT were 0.912, 0.595, 0.756 respectively (p < 0.05). Spearman's correlation coefficient value between skeletal developmental stages in in HWR, CR, and CBCT was found to be 0.785, 0.875, and 0.791, respectively (p < 0.05). CONCLUSION: Results of this study reveal that the determination of the skeletal development status with analysis of cervical vertebrae using cephalometric radiographs and CBCT is as reliable method as the evaluation of the hand-wrist radiographs and is compatible with chronological age in a subgroup of the Turkish population. When assessing the skeletal development stages of patients, both CBCT and CR can be used validly, so no extra hand-wrist radiography is required. This information is important for the prevention of increased radiation doses in patients.