Citations

Accurate teeth segmentation and orientation are fundamental in modern oral healthcare, enabling precise diagnosis, treatment planning, and dental implant design. In this study, we present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep-learning techniques. We built an end-to-end instance segmentation network that uses an encoder–decoder architecture reinforced with grid-aware attention gates along the skip connections. We introduce oriented bounding box (OBB) generation through principal component analysis (PCA) for precise tooth orientation estimation. Evaluating our approach on the publicly available DNS dataset, comprising 543 panoramic X-ray images, we achieve the highest Intersection-over-Union (IoU) score of 82.43% and a Dice Similarity Coefficient (DSC) score of 90.37% among compared models in teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU) score of 82.82%. We also conduct detailed analyses of individual tooth labels and categorical performance, shedding light on strengths and weaknesses. The proposed model’s accuracy and versatility offer promising prospects for improving dental diagnoses, treatment planning, and personalized healthcare in the oral domain.

Abstract Purpose Cenobamate is approved for treating focal epilepsy and has potential for treating tumor‐related epilepsy (TRE) based on potential anti‐tumor effects, though there is little data on efficacy and safety in this patient population. Methods This was a retrospective, multi‐center study of patients with TRE who were prescribed cenobamate as adjunctive treatment between January 2020 and November 2024 at Mayo Clinic in Florida, Minnesota, and Arizona. Drug efficacy was assessed, and factors that were predictive of response to treatment were analyzed. Results A total of 17 patients were included, with 12 being drug resistant. The median seizure frequency for the entire group before starting cenobamate treatment was 12 (IQR = 4–64) per month, and after treatment at last follow‐up was 3 (IQR = 1–15) per month, with an overall 56.3% reduction. Four patients (23.5%) were seizure‐free, and 7 (41.1%) patients were responders (at least 50% seizure reduction). No adverse events were reported for patients on concurrent chemotherapy. Responders had significantly fewer seizures per month compared to non‐responders (5.72 ± 10.29 vs. 27.33 ± 30.17; p = .030). No clinical factors were predictive of responsiveness (or lack thereof) to cenobamate. Conclusions This study demonstrates that adjunctive therapy with cenobamate treatment for TRE is a feasible and potentially effective option. Clinical history, including drug resistance, did not predict drug efficacy. No adverse effects were reported in those treated with chemotherapy. Further studies are needed to validate treatment response and safety.

Background Patients with advanced cancer experience a substantial amount of existential and emotional distress. Dignity therapy (DT) is a brief, individualized psychotherapy that aims to relieve existential distress and improve the experiences of individuals impacted by serious illness. Aim To evaluate the feasibility and acceptability of virtual DT (vDT) among patients with advanced cancer. Methods This pilot study recruited adult patients with advanced cancer and an expected prognosis >6 months. The vDT intervention consisted of 2 sessions of guided interviews focused on life review, discussion, and development of a generativity document (GD). Feasibility and acceptability were assessed quantitatively and with semi-structured interviews. Effectiveness measures (distress, dignity impact scale, quality of life [QOL]) were administered pre/post intervention. Results Thirty-one patients referred from a palliative care clinic were eligible. Twenty-one consented and 15 received a final GD. Patients who completed final assessments (N = 9) reported satisfaction with their experience and would recommend the therapy to others. Patients found the virtual component of the intervention acceptable, and the majority noted an improvement in their dignity. Qualitative analysis revealed themes of study team effectiveness, perspectives on intervention processes, benefits to self, and benefits to loved ones. Conclusion Participation in vDT intervention was feasible and acceptable for patients. Patients appreciated the connection with the interviewer, the process, and the benefit of having a document to share with loved ones. Preliminary effectiveness data suggest vDT improves sense of dignity.

Abstract Objectives Large language models (LLMs) offer potential in natural language processing tasks in healthcare. Due to the need for high accuracy, understanding their limitations is essential. The purpose of this study was to evaluate the performance of LLMs in classifying radiology reports for the presence of pulmonary embolism (PE) under various conditions, including different prompt designs and data perturbations. Materials and Methods In this retrospective, institutional review board approved study, we evaluated 3 Google’s LLMs including Gemini-1.5-Pro, Gemini-1.5-Flash-001, and Gemini-1.5-Flash-002, in classifying 11 999 pulmonary CT angiography radiology reports for PE. Ground truth labels were determined by concordance between a computer vision-based PE detection (CVPED) algorithm and multiple LLM runs under various configurations. Discrepancies between algorithms’ classifications were aggregated and manually reviewed. We evaluated the effects of prompt design, data perturbations, and repeated analyses across geographic cloud regions. Performance metrics were calculated. Results Of 11 999 reports, 1296 (10.8%) were PE-positive. Accuracy across LLMs ranged between 0.953 and 0.996. The highest recall rate for a prompt modified after a review of the misclassified cases (up to 0.997). Few-shot prompting improved recall (up to 0.99), while chain-of-thought generally degraded performance. Gemini-1.5-Flash-002 demonstrated the highest robustness against data perturbations. Geographic cloud region variability was minimal for Gemini-1.5+-Pro, while the Flash models showed stable performance. Discussion and Conclusion LLMs demonstrated high performance in classifying radiology reports, though results varied with prompt design and data quality. These findings underscore the need for systematic evaluation and validation of LLMs for clinical applications, particularly in high-stakes scenarios.

Abstract The link between regional tau load and clinical manifestation of Alzheimer’s disease (AD) highlights the importance of characterizing spatial tau distribution across disease variants. In typical (memory-predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicentres. However, given the limited spatial heterogeneity of tau in typical AD, atypical (non-amnestic-predominant) AD variants with distinct tau patterns provide a key opportunity to investigate the universality of connectivity as a scaffold for tau progression. In this large-scale, multicentre study across 14 international sites, we included cross-sectional tau-PET data from 320 individuals with atypical AD (n = 139 posterior cortical atrophy/PCA-AD; n = 103 logopenic variant primary progressive aphasia/lvPPA-AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS-AD), with a subset of individuals (n = 78) having longitudinal tau-PET data. Additionally, as an independent sample, we included regional post-mortem tau stainings from 93 atypical AD patients from two sites (n = 19 PCA-AD, n = 32 lvPPA-AD, n = 23 bvAD, n = 19 CBS-AD). Gaussian mixture modelling was used to harmonize different tau-PET tracers by transforming tau-PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether brain regions with stronger resting-state functional MRI-based functional connectivity, derived from healthy elderly controls in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), showed greater covariance in cross-sectional and longitudinal tau-PET and post-mortem tau pathology. Furthermore, we examined whether functional connectivity of tau-PET epicentres (i.e. the top 5% of regions with the highest baseline tau load) and tau-PET accumulation epicentres (i.e. the top 5% of regions with the highest tau accumulation rates) was associated with cross-sectional and longitudinal tau patterns. Our findings show that tau-PET epicentres aligned with clinical variants, e.g. a visual network predominant pattern in PCA-AD (‘visual AD’) and left-hemispheric temporal predominance, particularly within the language network, in lvPPA-AD (‘language AD’). Moreover, more strongly functionally connected regions showed correlated concurrent tau-PET levels (confirmed with post-mortem data) and tau-PET accumulation rates. The functional connectivity profile of tau-PET epicentres and accumulation epicentres corresponded to tau-PET progression patterns, with higher tau-PET levels and accumulation rates in functionally close regions, and lower tau-PET levels and accumulation rates in functionally distant regions. Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicentres, across all AD clinical variants. Since tau proteinopathy is a major driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant-specific end points in clinical trials.