Objectives: The aim of this study was to develop AI-based predictive models to assess the risk of osteoporosis in postmenopausal women using panoramic radiographs (OPTs). Methods: A total of 301 panoramic radiographs (OPTs) from postmenopausal women were collected and labeled based on DXA-assessed bone mineral density. Of these, 245 OPTs from the Hospital of San Giovanni Rotondo were used for model training and internal testing, while 56 OPTs from the University of Parma served as an external validation set. A mandibular region of interest (ROI) was defined on each image. Predictive models were developed using classical radiomics, deep radiomics, and convolutional neural networks (CNNs), evaluated based on AUC, accuracy, sensitivity, and specificity. Results: Among the tested approaches, classical radiomics showed limited predictive ability (AUC = 0.514), whereas deep radiomics using DenseNet-121 features combined with logistic regression achieved the best performance in this group (AUC = 0.722). For end-to-end CNNs, ResNet-50 using a hybrid feature extraction strategy achieved the highest AUC in external validation (AUC = 0.786), with a sensitivity of 90.5%. While internal testing yielded high performance metrics, external validation revealed reduced generalizability, highlighting the challenges of translating AI models into clinical practice. Conclusions: AI-based models show potential for opportunistic osteoporosis screening from OPT images. Although the results are promising, particularly those obtained with deep radiomics and transfer learning strategies, further refinement and validation in larger and more diverse populations are essential before clinical application. These models could support the early, non-invasive identification of at-risk patients, complementing current diagnostic pathways.

Objectives: Given the growing use of resonance frequency analysis in clinical implantology and the inconsistent evidence regarding its prognostic value, this systematic review aimed to clarify the association between implant stability quotient (ISQ) with insertion torque (IT), marginal bone loss (MBL), and implant survival/success rate. Material and Methods: Following PRISMA guidelines, a comprehensive literature search was conducted across MEDLINE, Scopus, and Web of Science to answer the PIO question: “In systemically healthy patients undergoing implant placement, are ISQ values associated with intraoperative and postoperative surrogate parameters (IT and MBL) and clinical outcomes such as implant survival and success?” Risk of bias was assessed using design‐specific methodological tools. Results: Forty‐eight studies were included, and 20 were eligible for quantitative analysis of ISQ‐IT correlation. A moderate and statistically significant association was observed (pooled r = 0.44; 95% CI: 0.32–0.55, p < 0.001), with substantial heterogeneity ( I ² > 90%). Three studies assessed baseline ISQ values and implant survival. Although surviving implants showed higher ISQ values (mean difference = 10.22), the pooled estimate was not statistically significant and remained highly heterogeneous. Quantitative synthesis for MBL and implant success was not feasible due to inconsistent reporting and methodological variability. Conclusions: ISQ shows a moderate correlation with IT, supporting its role as a complementary indicator of primary implant stability. However, high heterogeneity and limited certainty of evidence restrict its clinical interpretability. Current evidence does not support baseline ISQ values as independent predictors of MBL or implant survival.

Background/Objectives: Peri-implantitis is a major complication in implant dentistry, and its radiographic diagnosis remains challenging because conventional assessment is operator-dependent and bone loss is often detected only after measurable changes occur. Artificial intelligence (AI) may support the detection, quantification, and prognostic assessment of peri-implant bone conditions. This review aimed to synthesize evidence on AI-based radiographic approaches for peri-implantitis detection, marginal bone loss measurement, and risk stratification. Methods: PubMed and Scopus were searched for original studies published between 2013 and 2025 that applied artificial intelligence (AI), including machine learning and deep learning, to peri-implantitis. Eligible studies focused on peri-implant bone assessment and reported quantitative performance metrics. Extracted data included imaging modality, AI model, task, dataset, reference standard, validation strategy, performance, and clinical relevance. A qualitative synthesis was performed. Results: Eleven studies met the eligibility criteria; however, one full text could not be retrieved, and ten studies were included. In most of the studies, peri-implant marginal bone loss detection or measurement was performed using periapical/intraoral radiographs, while only few studies used panoramic or combined imaging. Common architectures included YOLO variants, Faster R-CNN, Mask R-CNN, U-Net, ResNet, and AlexNet. Performance was generally encouraging for implant localization, bone loss detection, keypoint identification, and severity classification. Only one study addressed outcome prediction. All studies were retrospective and internally validated. Conclusions: AI may support radiographic detection and quantification of peri-implant bone loss as an adjunctive diagnostic tool. However, evidence is limited by retrospective designs, heterogeneous reference standards, lack of external validation, and limited clinical-data integration. Future studies should prioritize prospective multicenter validation, longitudinal imaging, and multimodal models.

Objectives: To evaluate the effectiveness of spatial superimposition of cone‐beam computed tomography (CBCT) scans acquired at baseline and 6‐month follow‐up for monitoring bone healing in mandibular cystic lesions, aiming to reduce variability in conventional volumetric comparisons and enhance accuracy, reproducibility, and spatial fidelity of radiographic assessments. Materials and Methods: Two CBCT scans of a single patient (baseline, T0; follow‐up, T1) were imported into 3D Slicer v5.8.0. The mandible was isolated by manually cropping a region of interest, and the cystic lesion was segmented semi‐automatically using the Grow from Seeds tool. A rigid six‐degree‐of‐freedom registration aligned T1 to T0; the resulting transformation matrix was applied to the T1 segmentation to enable direct voxel‐wise comparison. Lesion volumes were measured, and spatial subtraction analysis quantified the resorbed area. Results: Lesion volume decreased from 1841.64 mm ³ at T0 to 1362.62 mm ³ at T1, corresponding to an absolute reduction of 479.02 mm ³ (26.0%). The subtraction mask accurately localized regressed voxels, confirming both the magnitude and spatial distribution of bone healing. Conclusions: Potential limitations related to CBCT artifacts and the need for operator‐dependent manual steps should be considered when interpreting the results. Overall, CBCT superimposition with semi‐automatic segmentation provides an objective, consistent, and anatomically precise approach for monitoring mandibular cyst regression and may represent a useful tool to support conservative management strategies.

Background: The growing application of artificial intelligence in dentistry has opened new opportunities for predicting peri-implantitis (PI) using convolutional neural networks (CNNs). Transfer learning, where knowledge from a model pre-trained on a large dataset is adapted to a new, domain-specific task, may enhance performance, particularly when medical datasets are limited. This study evaluated the ability of several pre-trained CNN architectures applied to baseline 2D intraoral radiographs to predict peri-implantitis onset. Methods: A retrospective multi-center study was conducted at the University of Michigan and King’s College London. A total of 195 implants (146 from Michigan and 49 from London) placed in 108 maintained patients were included. For each implant, a baseline periapical radiograph taken up to one year after prosthetic restoration was analyzed. Five pre-trained CNN architectures (ResNet50, DenseNet, VGG16, InceptionV3, EfficientNet) were combined with four transfer learning strategies: feature extraction (hybrid and non-hybrid), partial fine-tuning (50% of layers), and full fine-tuning. Models were trained using patient-wise splits to avoid leakage, and performance was evaluated on an independent test set using accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Results: All CNN architectures showed variable performance across transfer learning strategies. The non-hybrid feature extraction approach provided the most consistent discrimination, with the best models achieving AUC values between 0.74 and 0.83. InceptionV3 reached the highest AUC (0.83). Fine-tuning strategies yielded modest or low performance, with AUCs generally ranging from 0.42 to 0.69 across architectures. Conclusion: Non-hybrid feature extraction applied to pre-trained CNNs showed the most promising, though moderate, performance for predicting PI onset from baseline intraoral radiographs. These findings indicate that transfer learning may assist early risk stratification in implant dentistry, but larger prospective studies are needed before clinical adoption. Clinical relevance: Deep learning models may serve as adjunctive tools to identify implants at higher risk for future PI based on routine baseline radiographs, supporting more individualized maintenance protocols and earlier preventive interventions.