Purpose
This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95.
Methods
We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance.
Results
MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation.
Conclusion
MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

This review examines the bidirectional relationship between periodontitis and systemic health conditions, offering an integrated perspective based on current evidence. It synthesizes epidemiological data, biological mechanisms, and clinical implications to support collaborative care strategies recognizing oral health as a key component of overall wellness. Periodontitis affects 7.4% to 11.2% of adults worldwide, and its prevalence increases with age. Beyond its local effects, including gingival inflammation, periodontal pocket formation, and alveolar bone loss, periodontitis is associated with various systemic conditions. Emerging evidence has established links with obesity, diabetes mellitus, cardiovascular disease, chronic kidney disease, inflammatory bowel disease, rheumatoid arthritis, respiratory diseases, adverse pregnancy outcomes, certain malignancies, neurodegenerative diseases, psychological disorders, and autoimmune conditions. These associations are mediated by 3 primary mechanisms: dysbiotic oral biofilms, chronic low-grade systemic inflammation, and the dissemination of periodontal pathogens throughout the body. The pathophysiology involves elevated levels of pro-inflammatory cytokines (including interleukin 6, tumor necrosis factor alpha, and C-reactive protein), impaired immune function, oxidative stress, and molecular mimicry. Periodontal pathogens, particularly Porphyromonas gingivalis, are crucial in initiating and sustaining systemic inflammatory responses. Treatment of periodontitis has demonstrated measurable improvements in numerous systemic conditions, emphasizing the clinical significance of these interconnections. Periodontitis should be understood as more than just a localized oral disease; it significantly contributes to the overall systemic inflammatory burden, with implications for general health. An integrated, multidisciplinary approach to prevention, early detection, and comprehensive treatment is vital for optimal patient outcomes. Healthcare providers should acknowledge oral health as an essential element of systemic well-being.

ABSTRACT Objectives:

This study investigated the 24-hour ambulatory blood pressure monitoring (ABPM) and Holter parameters for evaluating their prognostic significance of cardiovascular events including stroke in population without atrial fibrillation (AF).

Methods:

Among 3,199 patients that underwent ABPM, 335 who also underwent Holter recordings were selected in a tertiary hospital. Seventeen patients who had been documented with AF on Holter monitoring or diagnosed with AF were excluded, and finally 318 patients were analyzed. The association between cardiovascular events and ABPM/Holter parameters was analyzed by a logistic regression model, and the risk factors were estimated by a Cox hazard model. Age, sex, and histories of cardiovascular disease were adjusted by a multivariable analysis, and the cut-off values were suggested by a Kaplan-Meyer analysis.

Results:

During the total follow-up (28.5±1.7 months), 13 (4.1%) stroke, 6 (1.9%) heart failure, and 12 (3.8%) acute coronary syndrome incidences were observed. In the univariate analysis of the ABPM parameters, an increment in the night systolic BP (hazard ratio=1.034, P=0.020) and night diastolic BP (hazard ratio=1.063, P=0.031) significantly elevated the risk of a stroke occurrence. According to the Kaplan-Meyer analysis, there was a significant difference in the stroke incidence between the groups divided by a cut-off value of the night systolic BP of 120 mmHg (P=0.014) and night diastolic BP of 75 mmHg (P=0.023).

Conclusion:

In a population without AF, the nocturnal BP was a significant predictor of a stroke incidence. At this point, the cut-off value of mean 120/75 mmHg in 24 ABPM was advisable.