Predicting the risk of dental implant loss using deep learning.

AIM: To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography. MATERIALS AND METHODS: Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) between January 2012 and January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5-year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan-Meier curves were created and compared by the log-rank test. RESULTS: The IM exhibited the best performance in predicting implant loss risk (AUC = 0.90, 95% confidence interval [CI] 0.84-0.95), followed by the DL model (AUC = 0.87, 95% CI 0.80-0.92) and the CM (AUC = 0.72, 95% CI 0.63-0.79). CONCLUSIONS: Our study offers preliminary evidence that both the DL model and the IM performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks.

pH and lipase-responsive nanocarrier-mediated dual drug delivery system to treat periodontitis in diabetic rats.

Precise and controlled drug delivery to treat periodontitis in patients with diabetes remains a significant clinical challenge. Nanoparticle-based drug delivery systems offer a potential therapeutic strategy; however, the low loading efficiency, non-responsiveness, and single effect of conventional nanoparticles hinder their clinical application. In this study, we designed a novel self-assembled, dual responsive, and dual drug-loading nanocarrier system, which comprised two parts: the hydrophobic lipid core formed by 1, 2-Distearoyl-sn-glycero-3-phosphoethanolamine-Poly (ethylene glycol) (DSPE-PEG) loaded with alpha-lipoic acid (ALA); and a hydrophilic shell comprising a poly (amidoamine) dendrimer (PAMAM) that electrostatically adsorbed minocycline hydrochloride (Mino). This unique design allows the controlled release of antioxidant/ALA under lipase stimulation from periodontal pathogens and antimicrobial/Mino under the low pH of the inflammatory microenvironment. In vivo and in vitro studies confirmed that this dual nanocarrier could inhibit the formation of subgingival microbial colonies while promoting osteogenic differentiation of cells under diabetic pathological conditions, and ameliorated periodontal bone resorption. This effective and versatile drug-delivery strategy has good potential applications to inhibit diabetes-associated periodontal bone loss.

Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation.

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.