Background/Objectives: Dental implant technology has become the primary treatment modality for managing tooth loss in current clinical practice. Accurate identification of implant brands and their clinical exposure status have increasingly emerged as a critical issue in implant-related research and clinical diagnosis. Conventional approaches that rely on clinicians’ experience are time-consuming and may lead to misclassification due to variations in image quality or subtle differences in implant design features across brands. Methods: This single-center study classified six categories combining two implant brands (3i and Xive) and three exposure statuses. A retrospective cohort of 497 patients (708 periapical radiographs, 928 implant instances) was partitioned using an 8:1:1 stratified ratio with strict patient-level independence. Following YOLOv8-based implant localization and ROI extraction, classification was evaluated across deep learning backbones using 5-fold cross-validation and compared against a panel of three dental specialists on a test set of 8 complex cases. Results: YOLOv8 achieved a precision of 99.39% and a sensitivity of 98.63% in the implant detection task. For the classification of implant brand and exposure status, EfficientNet-B3 achieved the best performance with an accuracy of 99.18 ± 0.85% (95% CI: [94.20%, 100.00%]) across 5-fold cross-validation, demonstrating competitive diagnostic performance alongside existing implant classification benchmarks in the literature. In a preliminary clinical feasibility assessment on representative cases, the framework demonstrated rapid algorithmic inference (mean inference time of 36.18 ms per image), providing instant decision-support potential alongside participating clinicians. Conclusions: This study demonstrates the technical feasibility and proof-of-concept of a deep learning framework for identifying dental implant brands and exposure status under robust internal validation. While offering preliminary decision-support potential, external validation on multi-center cohorts and prospective clinical trials across expanded implant systems remain necessary before clinical implementation.

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. Experimental results demonstrate that, when using three physiological parameters as input, the ANN achieved a classification accuracy of 97.78%, substantially outperforming traditional approaches. Even under two-parameter input conditions, the model maintained strong predictive performance. These findings confirm the effectiveness and practical potential of the proposed framework for real-time, non-invasive CI assessment. Moreover, this research has received rigorous assessment and approval from the Institutional Review Board (IRB) under application number 202501987B0.