Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.

Insufficient oral ridge tissue presents a challenge in the treatment of dental implants. One method to enhance alveolar crest dimensions is guided bone regeneration (GBR); however, existing membranes have certain limitations. To address this issue, we aimed to compare the effectiveness of a resorbable bilayer membrane composed of poly(l-lactic acid) and poly(-caprolactone) （PLACL）with that of a collagen membrane（COL） in a rat GBR model. The rat calvaria was used as an experimental model by placing a plastic cylinder. Forty male Fisher rats underwent surgery, and micro-computed tomography and histomorphometric analyses were performed to assess bone regeneration. The results showed that bone regeneration was similar across all the groups. However, after 24 weeks, the PLACL membrane demonstrated significant resilience, occasional partial degradation, and intermittent air bubble formation. This extended preservation of the barrier effect has great potential to facilitate optimal bone regeneration. In conclusion, this study shows that the PLACL membrane is a promising alternative to GBR. By providing a durable barrier and supporting bone regeneration over an extended period, this resorbable bilayer membrane may address the limitations of the current membranes. Further studies and clinical trials are warranted to validate the efficacy and safety of this drug in humans.