Artificial Intelligence (AI) can optimize treatment approaches in dental healthcare due to its high level of accuracy and wide range of applications. This study seeks to propose a new deep learning (DL) ensemble model based on deep Convolutional Neural Network (CNN) algorithms to predict tooth position, detect shape, detect remaining interproximal bone level, and detect radiographic bone loss (RBL) using periapical and bitewing radiographs. 270 patients from January 2015 to December 2020, and all images were deidentified without private information for this study. A total of 8000 periapical radiographs with 27,964 teeth were included for our model. AI algorithms utilizing the YOLOv5 model and VIA labeling platform, including VGG-16 and U-Net architecture, were created as a novel ensemble model. Results of AI analysis were compared with clinicians' assessments. DL-trained ensemble model accuracy was approximately 90% for periapical radiographs. Accuracy for tooth position detection was 88.8%, tooth shape detection 86.3%, periodontal bone level detection 92.61% and radiographic bone loss detection 97.0%. AI models were superior to mean accuracy values from 76% to 78% when detection was performed by dentists. The proposed DL-trained ensemble model provides a critical cornerstone for radiographic detection and a valuable adjunct to periodontal diagnosis. High accuracy and reliability indicate model's strong potential to enhance clinical professional performance and build more efficient dental health services.

Alzheimer’s disease (AD) is a neurodegenerative disease that usually affects older individuals. Owing to the higher incidence of root caries and missing teeth in elderly individuals, the bacteria involved in these dental concerns might potentially deteriorate their cognitive function. Altered microbiota in the oral cavity may induce neuroinflammation through migration from the oral cavity to the brain. However, the correlation between the composition of the oral microbiota and neurodegenerative disease remains unclear. In this study, we evaluated sequence to determine the relative abundance and diversity of bacterial taxa in the dental plaque of elderly patients with AD and controls. Oral samples; the DMFT index; and other clinical examination data were collected from 17 patients with AD and 18 normal elderly individuals as the control group. Patients with AD had significantly more missing teeth and higher dental plaque weight but lower microbial diversity than controls. Significantly increased numbers of Lactobacillales, Streptococcaceae, and Firmicutes/Bacteroidetes and a significantly decreased number of Fusobacterium were observed in patients with AD. In conclusion, using the PacBio single-molecule real-time (SMRT) sequencing platform to survey the microbiota dysbiosis biomarkers in the oral cavity of elderly individuals could serve as a tool to identify patients with AD.

Obese patients present a challenge to safe general anesthesia because of impaired cardiopulmonary physiology and increased risks of aspiration and acute upper airway obstruction. Since studies are lacking regarding the postoperative effects on recovery from general anesthesia in morbidly obese patients, we conducted a systematic review and meta-analysis of recovery outcomes in morbidly obese patients who had undergone general anesthesia. We systematically searched the PubMed, EMBASE™, Cochrane, and Scopus™ databases for randomized controlled trials that evaluated the outcome of anesthesia with desflurane, sevoflurane, isoflurane, or propofol in morbidly obese patients. Using a random effects model, we conducted meta-analyses to assess recovery times (eye opening, hand squeezing, tracheal extubation, and stating name or birth date), time to discharge from the postanesthesia care unit (PACU), and the incidence and severity of postoperative nausea and vomiting (PONV). We reviewed results for 11 trials and found that patients given desflurane took less time: to respond to commands to open their eyes (weighted mean difference [WMD] −3.10 min; 95% confidence interval (CI): −5.13 to −1.08), to squeeze the investigator’s hand (WMD −7.83 min; 95% CI: −8.81 to −6.84), to be prepared for tracheal extubation (WMD −3.88 min; 95% CI: −7.42 to −0.34), and to state their name (WMD −7.15 min; 95% CI: −11.00 to −3.30). We did not find significant differences in PACU discharge times, PONV, or the PACU analgesic requirement. Postoperative recovery was significantly faster after desflurane than after sevoflurane, isoflurane, or propofol anesthesia in obese patients. No clinically relevant differences were observed regarding PACU discharge time, incidence of PONV, or postoperative pain scores. The systematic review was registered with PROSPERO (CRD42014009480). Les patients obèses constituent un défi pour une anesthésie générale sécuritaire en raison d’une physiologie cardiopulmonaire altérée et de risques accrus d’aspiration et d’obstruction aiguë des voies aériennes hautes. Considérant que l’on manque d’études sur la récupération des effets postopératoires de l’anesthésie générale chez des patients présentant une obésité morbide, nous avons effectué une revue systématique et une méta-analyse des critères de jugement de la récupération chez ces patients ayant une obésité morbide et ayant subi une anesthésie générale. Nous avons effectué une recherche systématique dans les bases de données PubMed, MEDLINE™, Cochrane et Scopus™ pour identifier des études randomisées et contrôlées qui avaient évalué les aboutissements de l’anesthésie par desflurane, sévoflurane, isoflurane ou propofol chez des patients ayant une obésité morbide. Au moyen d’un modèle d’effets aléatoires, nous avons réalisé des méta-analyses pour évaluer les temps de récupération (ouverture des yeux, serrement de main, extubation trachéale, indication du nom ou de la date de naissance), le temps écoulé jusqu’au congé de l’unité de soins post anesthésie (salle de réveil) et l’incidence et la sévérité des nausées et vomissements postopératoires. Nous avons analysé les résultats de 11 études et avons constaté que les patients ayant reçu du desflurane prenaient moins de temps à: répondre à l’ordre d’ouvrir les yeux (différence moyenne pondérée [DMP] −3,10 min; intervalle de confiance [IC] à 95 %: −5,13 à −1,08), à serrer la main de l’investigateur (DMP −7,83 min; IC à 95 %: −8,81 à −6,84), à être prêt pour l’extubation trachéale (DMP −3,88 min; IC à 95 %: −7,42 à −0,34) et à donner leur nom (DMP −7,15 min; IC à 95 %: −11,00 à −3,30). Nous n’avons pas trouvé de différences significatives dans les délais de congé de la salle de réveil, ni sur les plans des nausées et vomissements post opératoires ou des besoins en analgésiques en salle de réveil. La récupération postopératoire a été significativement plus rapide après desflurane qu’après une anesthésie par sévoflurane, isoflurane ou propofol chez des patients obèses. Aucune différence cliniquement pertinente n’a été observée concernant les délais de congé de la salle de réveil, l’incidence des nausées et vomissements postopératoires, ou des scores de douleur postopératoire. La revue systématique a été enregistrée dans PROSPERO (CRD42014009480).