Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning.

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (Dsys-Ddia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R2 = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.

Nipple-Sparing Gigantomastia Breast Reduction: A Systematic Review.

INTRODUCTION: There remains an unclear definition of the term "gigantomastia," with many studies using different parameters and measurements. Currently, the operative management and patient education for gigantomastia are outdated. The historical teaching that a free nipple graft is necessary in elongated pedicles to avoid nipple necrosis may not be factual. The principal goal of our review aims to determine the safety of nipple-sparing breast reductions on large ptotic breasts via complication rate analysis. METHODS: The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines of conduct for systematic review and meta-analysis. In October 2021, PubMed was used to search the US National Library of Medicine database. Rayyan Intelligent Systematic Review aided in screening studies by title then abstract. If inclusion criteria were met, the entire article was reviewed. RESULTS: Twenty-two articles satisfied the inclusion and exclusion criteria. The study was composed of 1689 total patients with a mean body mass index of 32.9 (±3.4). Mean midclavicle-to-nipple distance and resection weight per breast was 39 cm (±3.8) and 1423.8 g (±268.9), respectively. A Wise pattern was preferred in 77.3% of the studies, with an inferior (45.5%) and superomedial (45.5%) pedicle used most commonly. Complete nipple areolar complex necrosis (1.7%) was found in 4 studies, whereas partial (5.9%) was observed in 11. More common complications included delayed wound healing (17.4%), surgical site infection (14.3%), seroma (10.5%), scar hypertrophy (9.9%), and wound dehiscence (9.2%). CONCLUSION: Nipple-sparing breast reduction surgery can be safely performed on hypertrophic and severely ptotic breasts with nipple areolar complications, such as partial or complete nipple areolar complex loss, at a rate less than previously believed.