The complicated interplay of clinical, demographic, and procedural factors makes it difficult to predict the success of in vitro fertilization (IVF), a commonly used assisted reproductive technology. The goal of this research was to create an artificial intelligence (AI) pipeline that could predict live birth outcomes in IVF treatments with high accuracy. We evaluated prediction performance by integrating different feature selection methods, such as principal component analysis (PCA) and particle swarm optimization (PSO), with different machine learning-based classifiers, including random forest (RF) and decision tree, as well as deep learning-based classifiers, including a custom transformer-based model and a Tab_transformer model with an attention mechanism. Additionally, this study analyzes confounding factors like patient age and previous IVF cycles and explores the influence of different perturbation and preprocessing techniques and validates the model's robustness under varied scenarios. In addition, Shapley Additive Explanations (SHAP) analysis was performed to enhance interpretability of methods. This research demonstrated that the best performance was achieved by combining PSO for feature selection with the Tab_transformer-based deep learning model, yielding an accuracy of 97 % and an AUC of 98.4 %, highlighting its significant performance in prediction live births. By identifying the most significant predictors of infertility and guaranteeing clinical significance, SHAP analysis significantly improved interpretability. With the accuracy and interpretability, this study develops a strong AI pipeline for predicting live birth outcomes in IVF. This study establishes a highly accurate AI pipeline for predicting live birth outcomes in IVF, demonstrating its potential to enhance personalized fertility treatments.

Background: Maxillary sinus floor augmentation (MSFA) is commonly used to increase posterior maxillary bone volume prior to implant placement. Although generally successful, late complications can impact long-term outcomes. The purpose of the study was to estimate the incidence and timing of atypical late complications following (MSFA) using bovine xenohybrid bone grafts. The study also aimed to evaluate whether preoperative bone volume is associated with the risk of complications. Methods: This retrospective cohort study was conducted at the Center of Oral and Maxillofacial Surgery, Danube Private University, Krems-Stein, Austria, and included patients who underwent MSFA with bovine xenohybrid bone grafts and either simultaneous or staged implant placement between January 2020 and December 2023. Preoperative bone volume of the posterior maxilla measured via cone beam computed tomography (CBCT) in the planned implant insertion position. The primary endpoint was the time (days) from MSFA to the occurrence of a graft-related complication (defined as atypical if occurring more than 6 months after MSFA and not related to peri-implantitis) The covariates included subjects’ age, sex, the quantity of graft used for MSFA, timing of dental implant insertion (simultaneous vs. staged) and implant dimensions. Kaplan–Meier analysis and Cox proportional hazards regression were used to evaluate time-to-event data. Only one graft site per patient was analyzed. Results: Atypical complications occurred in 9 out of 47 patients (19.1%), with an average time to onset of 645 days. In a multivariable analysis, a lower preoperative bone volume was found to be an independent predictor of an increased risk of complications (hazard ratio [HR]: 0.972; 95% confidence interval [CI]: 0.925–1.021; p = 0.252). However, the quantity of graft used for MSFA was not found to be a predictor (p = 0.46). Conclusions: Within the limitations of a retrospective study, reduced native bone volume appears to increase the risk of atypical late complications following MSFA with bovine xenohybrid grafts. This makes closer clinical and radiologic follow-up of patients over a longer period very necessary.

This work introduces a new efficient iterative solver for the reconstruction of real-time cone-beam computed tomography (CBCT), which is based on the Prior Image Constrained Compressed Sensing (PICCS) regularization and leverages the efficiency of Krylov subspace methods. In particular, we focus on the setting where a sequence of under-sampled CT scans are taken on the same object with only local changes (e.g. changes in a tumour size or the introduction of a surgical tool). This is very common, for example, in image-guided surgery, where the amount of measurements is limited to ensure the safety of the patient. In this case, we can also typically assume that a (good) initial reconstruction for the solution exists, coming from a previously over-sampled scan, so we can use this information to aid the subsequent reconstructions. The effectiveness of this method is demonstrated in both a synthetic scan and using real CT data, where it can be observed that the PICCS framework is very effective for the reduction of artifacts, and that the new method is faster than other common alternatives used in the same setting.

In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

This study introduces a novel optimization approach for source-detector trajectories, designed to simultaneously extend the small dental CBCT field of view (FOV) at specific regions of interest (ROIs) while reducing radiation dose. The proposed algorithm automatically customizes and produces a mechanically feasible arc-based scan trajectory to target predefined ROIs, expanding the FOV beyond the standard range while utilizing only the necessary projections to reconstruct the region of interest, rather than those required for reconstruction of the entire volume. This approach enables a significant reduction in the number of projections. Both qualitative and quantitative analyses, including peak signal-to-noise ratio (PSNR) values greater than 30 dB and structural similarity index measure (SSIM) scores above 0.9, demonstrate that the method successfully enhances the FOV while maintaining high image quality. Notably, the approach achieves a significant reduction in the number of projections up to 47% compared to the conventional limited-angle circular trajectory. This is the first study to explore the simultaneous extension of the FOV and radiation dose reduction in dental CBCT imaging, providing a promising solution for improving clinical outcomes and treatment planning in next-generation CBCT systems.

Objective: This retrospective study investigated the relationship between the timing of single-tooth implant-supported restorations—including the interval from tooth extraction and socket preservation to implant placement and final prosthetic restoration—and the severity of periapical inflammation, as classified by the Dental Apical Inflammation Score (DAIS). Methods: A total of 87 patients were included (DAIS 1: 8; DAIS 2: 14; DAIS 3: 1; DAIS 4: 64). Procedural intervals (extraction, socket preservation, implant placement, and prosthetic restoration) were analyzed alongside histological assessment of periapical inflammation. Clinical parameters such as tooth location, endodontic treatment status, patient age, and sex were examined using ANOVA, chi-square tests, and Pearson’s correlation analysis. Results: An effective sample size of N = 86 (excluding the single DAIS 3 case) was included in the parametric analysis. No significant differences in procedural timing were found across DAIS groups for the intervals between extraction and implant placement (F(2, 83) = 0.338, p = 0.714) or restoration (F(2, 83) = 1.016, p = 0.367). Tukey’s HSD post hoc analysis showed no pairwise group differences. Histological diagnosis was not significantly associated with DAIS (χ²(6) = 7.00, p = 0.321), though small subgroup sizes warrant interpretive caution. A significant association was identified between DAIS score and tooth location (χ²(3) = 11.79, p = 0.008). Patient age showed a weak but significant positive correlation with DAIS (r = 0.222, p = 0.039). No significant associations were found for endodontic status (χ²(3) = 2.54, p = 0.468) or sex (χ²(3) = 2.63, p = 0.452). Histological assessment revealed that most specimens represented radicular cysts with varying proportions of acute and chronic inflammatory infiltrates, consistent with the DAIS classification. Conclusions: Procedural timing did not significantly differ between DAIS groups. However, the observed associations with tooth location and patient age may warrant further investigation into their potential relevance for treatment planning. These findings suggest that implant timing may not need to be substantially modified according to DAIS severity alone, but that anatomical site and patient age should be considered during clinical decision-making.