Background In previous studies, COVID-19 complications were reported to be associated with periodontitis. Accordingly, this study was designed to test the hypothesis that a history of periodontal therapy could be associated with lower risk of COVID-19 complications. Methods A case–control study was performed using the medical health records of COVID-19 patients in the State of Qatar between March 2020 and February 2021 and dental records between January 2017 and December 2021. Cases were defined as COVID-19 patients who suffered complications (death, ICU admissions and/or mechanical ventilation); controls were COVID-19 patients who recovered without major complications. Associations between a history of periodontal therapy and COVID-19 complications were analysed using logistic regression models adjusted for demographic and medical factors. Blood parameters were compared using Kruskal–Wallis test. Results In total, 1,325 patients were included. Adjusted odds ratio (AOR) analysis revealed that non-treated periodontitis was associated with significant risk of need for mechanical ventilation (AOR = 3.91, 95% CI 1.21–12.57, p = 0.022) compared to periodontally healthy patients, while treated periodontitis was not (AOR = 1.28, 95% CI 0.25–6.58, p = 0.768). Blood analyses revealed that periodontitis patients with a history of periodontal therapy had significantly lower levels of D-dimer and Ferritin than non-treated periodontitis patients. Conclusion Among COVID-19 patients with periodontal bone loss, only those that have not received periodontal therapy had higher risk of need for assisted ventilation. COVID-19 patients with a history of periodontal therapy were associated with significantly lower D-dimer levels than those without recent records of periodontal therapy. Clinical relevance The fact that patients with treated periodontitis were less likely to suffer COVID-19 complications than non-treated ones further strengthen the hypothesis linking periodontitis to COVID-19 complications and suggests that managing periodontitis could help reduce the risk for COVID-19 complications, although future research is needed to verify this.

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IntroductionOpen‐source generative artificial intelligence (AI) applications are fast‐transforming access to information and allow students to prepare assignments and offer quite accurate responses to a wide range of exam questions which are routinely used in assessments of students across the board including undergraduate dental students. This study aims to evaluate the performance of Chat Generative Pre‐trained Transformer (ChatGPT), a generative AI‐based application, on a wide range of assessments used in contemporary healthcare education and discusses the implications for undergraduate dental education.Materials and MethodsThis was an exploratory study investigating the accuracy of ChatGPT to attempt a range of recognised assessments in healthcare education curricula. A total of 50 independent items encompassing 50 different learning outcomes (n = 10 per item) were developed by the research team. These included 10 separate items based on each of the five commonly used question formats including multiple‐choice questions (MCQs); short‐answer questions (SAQs); short essay questions (SEQs); single true/false questions; and fill in the blanks items. Chat GPT was used to attempt each of these 50 questions. In addition, ChatGPT was used to generate reflective reports based on multisource feedback; research methodology; and critical appraisal of the literature.ResultsChatGPT application provided accurate responses to majority of knowledge‐based assessments based on MCQs, SAQs, SEQs, true/false and fill in the blanks items. However, it was only able to answer text‐based questions and did not allow processing of questions based on images. Responses generated to written assignments were also satisfactory apart from those for critical appraisal of literature. Word count was the key limitation observed in outputs generated by the free version of ChatGPT.ConclusionNotwithstanding their current limitations, generative AI‐based applications have the potential to revolutionise virtual learning. Instead of treating it as a threat, healthcare educators need to adapt teaching and assessments in medical and dental education to the benefits of the learners while mitigating against dishonest use of AI‐based technology.

Dentists could fail to notice periapical lesions (PLs) while examining panoramic radiographs. Accordingly, this study aimed to develop an artificial intelligence (AI) designed to address this problem. Materials and methods: a total of 18618 periapical root areas (PRA) on 713 panoramic radiographs were annotated and classified as having or not having PLs. An AI model consisting of two convolutional neural networks (CNNs), a detector and a classifier, was trained on the images. The detector localized PRAs using a bounding-box-based object detection model, while the classifier classified the extracted PRAs as PL or not-PL using a fine-tuned CNN. The classifier was trained and validated on a balanced subset of the original dataset that included 3249 PRAs, and tested on 707 PRAs. Results: the detector achieved an average precision of 74.95%, while the classifier accuracy, sensitivity and specificity were 84%, 81% and 86%, respectively. When integrating both detection and classification models, the proposed method accuracy, sensitivity, and specificity were 84.6%, 72.2%, and 85.6%, respectively. Conclusion: a two-stage CNN model consisting of a detector and a classifier can successfully detect periapical lesions on panoramic radiographs.