Background We aimed to evaluate the prevalence of predisposing factors and oral manifestations in SARS-CoV-2 infection. Material and Methods 204 SARS-CoV-2 positive patients were included in the study. Questions regarding the systemic, periodontal health, oral hygiene habits, common symptoms and, oral manifestations of COVID-19 such as oral lesions, and dry mouth were included in the survey. The Visual Analogue Scale (VAS) was used. Results 47.5% of individuals had various systemic diseases. Dry mouth (44.2%) and oral lesions (22.4%) were the most common oral manifestations in COVID-19 patients. Also, dry mouth had the highest VAS score. The most common oral lesion locations were buccal mucosa (15.2%) and tongue (10.8%). The majority of participants (142 patients) were affected by taste disorders. Patients who received periodontal treatment before SARS-CoV-2 infection reported fewer oral complaint and manifestations than those who did not receive periodontal therapy ( p =0.032). There was no statistically significant difference between males and females on the presence of any oral manifestations, and taste disorders. Conclusions Our results showed that SARS-CoV-2 could cause oral manifestations. However various predisposing factors may be part of the etiology and promote oral findings. Key words: SARS-CoV-2, COVID-19, xrestomia, dysgeusia, oral manifestation.

Objectives: Automatically detecting dental conditions using Artificial intelligence (AI) and reporting it visually are now a need for treatment planning and dental health management. This work presents a comprehensive computer-aided detection system to detect dental restorations. Methods: The state of art ten different Deep Learning detection models was used including R-CNN, Faster R-CNN, SSD, YOLOv3 and RetinaNet as detectors. ResNet-50, ResNet-101, XCeption-101, VGG16 and DarkNet53 were integrated as backbone and feature extractor in addition to efficient approaches such Side-Aware Boundary Localization, cascaded structures and simple model frameworks like Libra and Dynamic. Total 684 objects in panoramic radiographs were used to detect available three classes, namely dental restorations, denture and implant. Each model was evaluated by mean average precision (mAP), average recall (AR) and precision-recall curve using Common Objects in Context (COCO) detection evaluation metrics. Results: mAP varied between 0.755 and 0.973 for ten models explored while AR ranges between 0.605 and 0.771. Faster R-CNN RegnetX provided the best detection performance with mAP of 0.973 and AR of 0.771. Area under precision-recall curve was 0.952. Precision-recall curve indicated that errors were mainly dominated by localization confusions. Conclusions: Results showed that the proposed AI-based computer-aided system had great potential with reliable, accurate performance detecting dental restorations, denture and implant in panoramic radiographs. As training models with more data and standardization in reporting, AI-based solutions will be implemented to dental clinics for daily use soon.

Objectives: This study aimed to analyze the impact of the COVID-19 pandemic on using dental radiography. Materials and Methods: This retrospective study included adult patients who applied at 3-time intervals reflecting changes in the course of the COVID-19 pandemic in 2020 (T1-T2-T3). Patients’ demographics, the number of radiographic and clinical procedures provided, and radiographic findings were noted during T1-T2-T3. Results: The frequency of using dental radiography was the lowest at the beginning of the pandemic and significantly increased over time. Using radiography increased when the number of COVID-19 cases increased. The course of the COVID-19 disease did not affect using radiography by dentists. The use of dental radiography in elderly patients was found to be less than in younger patients. Invasive treatments applied to the patients who had radiographs were significantly higher than those who had not at T2 and T3. Conclusion: This study demonstrated the changes in dental radiographic procedures during the pandemic. It was ordered to avoid intraoral radiography as much as possible due to aerosol production during the COVID-19 pandemic. To make better use of dental radiography, manufacturers should improve extraoral radiography with better image quality with lower radiation doses.

Amaç: Bu çalışmanın amacı, bir grup Türk popülasyonunda, diş hekimliği fakültesi hastanesine başvuran hastaların sistemik hastalık prevalansının belirlenmesi ve medikal profilinin değerlendirilmesidir. Gereç ve Yöntem: Bu retrospektif çalışmaya, Diş Hekimliği fakültesi Ağız, Diş ve Çene Radyoloji kliniğine Mart- Eylül 2022 tarihleri arasında çeşitli nedenlerle muayene için başvuran hastalar dahil edildi. Hastaların yaş, cinsiyet gibi demografik bilgileri ve sistemik hastalıkları belirlendi. Sistemik hastalık görülme sıklığı ile cinsiyet ve yaş arasındaki farklılık ki-kare testi ile değerlendirildi. Bulgular: Çalışmada, 14-89 yaş aralığında, 2007 kadın ve 1284 erkek olmak üzere toplam 3291 hasta değerlendirildi. Çalışmaya dahil edilen bireylerden %71.77’sinde (n=2362) sistemik hastalık gözlenmezken, %28.23’ünde (n=929) en az bir tane sistemik hastalık belirlendi. Sistemik hastalık görülme sıklığı, cinsiyetler ve yaş grupları arasında istatiksel anlamda farklılık gösterdi (p

Understanding usual anatomical structures and unusual root formations is crucial for root canal treatment and surgical treatments. Root dilaceration is a tooth formation with sharp bends or curves, which causes dental treatments to fail, especially root canal treatments. The aim of the study was to apply recent deep learning models to develop an artificial intelligence-based computer-aided detection system for root dilaceration in panoramic radiographs. A total of 983 objects in 636 anonymized panoramic radiographs were initially labelled by an oral and maxillofacial radiologist and were then used to detect root dilacerations. A total of 19 state-of-the-art deep learning models with distinct backbones or feature extractors were used with the integration of alternative frameworks. Evaluation was carried out using Common Objects in Context (COCO) detection evaluation metrics, mean average precision (mAP), accuracy, precision, recall, F1 score and area under precision-recall curve (AUC). The duration of training was also noted for each model. Considering the detection performance of all models, mAP, accuracy, precision, recall, and F1 scores of up to 0.92, 0.72, 0.91, 0.87 and 0.83, respectively, were obtained. AUC were also analyzed to better understand where errors originated. It was seen that background confusion limited performance. The proposed system can facilitate root dilaceration assessment and alleviate the burden of clinicians, especially for endodontists and surgeons.