Citations

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care.

ABSTRACT Melasma is a chronic hyperpigmentation disorder that disproportionately affects women and individuals with darker skin types, leading to a significant psychosocial burden. This meta‐analysis aims to evaluate the safety and efficacy of the 755‐nm picosecond alexandrite laser (PSAL) compared with conventional therapies in the management of melasma. PubMed, Ovid Medline, Embase, Cochrane Library, and Google Scholar were searched from inception to April 10, 2025, in accordance with PRISMA guidelines. Randomised controlled trials (RCTs) assessing PSAL versus control comparators in adults with melasma and reporting Melasma Area and Severity Index (MASI) or Modified MASI outcomes were included. The review was registered prospectively on PROSPERO (CRD420251022381). Risk of bias was assessed using ROB2. Pooled effect sizes were calculated using a random‐effects model. Five RCTs ( n = 139 patients) were included. Change in MASI favoured triple combination cream (TCC) over PSAL therapy (MD = 1.82, 95% CI: 1.11, 2.52, I 2 = 0%). Post‐inflammatory hyperpigmentation was more common in the PSAL group compared to topical creams (OR = 6.86, 95% CI: 1.47, 32.07, I 2 = 0%) but had no difference with the Q ‐switched lasers (OR = 4.09, 95% CI: 0.62, 26.97, I 2 = 0%). There was no incidence of hypopigmentation or infection reported. PSAL appears inferior to the TCC in reducing MASI scores. Overall certainty is low due to small, heterogenous trials. Low rates of irreversible adverse events with PSAL may support trials in refractory cases, but long‐term RCTs with larger sample sizes are required to evaluate durability and recurrence outcomes.

ABSTRACT Background Cardiac amyloidosis (CA) is frequently complicated by atrial fibrillation (AF), yet outcomes after left atrial appendage occlusion (LAAO) in this population remain poorly defined. Methods We conducted a retrospective TriNetX study of adults with AF undergoing LAAO, comparing patients with and without CA after 1:1 propensity matching. Outcomes were assessed using Kaplan–Meier analyses and Cox regression. Results Among 532 matched pairs, mortality and major adverse cardiovascular events were similar between groups, whereas major bleeding was higher in CA (HR 1.90). Conclusions LAAO yields comparable ischemic outcomes in CA, though bleeding risk is increased.

Abstract Background Computed tomography (CT) is an essential imaging modality for disease diagnosis, treatment efficacy, and image‐based guidance of various medical procedures. The locally deposited radiation dose in tissues, as estimated by the computed tomography dose index (CTDI), can vary considerably across exposures delivered by CT scanners from different vendors, even if the scans are performed using similar technique factors, such as tube potential and tube current. The volumetric CTDI (CTDI vol ) is a common dose metric that reports an average radiation dose (in mGy) delivered to a specific volume within a test phantom. The CTDI vol is important in dosimetry applications as the organ absorbed dose within the patient has been shown to scale in near‐linear proportion, creating a basis for comparing organ doses across different scan protocols and scanner models. Purpose To develop a database of tube current‐time product (mAs) normalized CTDI vol values for currently utilized CT scanner models for each of the four primary CT vendors for use in the MIRDct organ dosimetry software available at MIRDsoft.org. This data forms the basis of the MIRDct code, which reports organ doses across a range of computational phantoms based upon axial organ dose coefficient libraries generated through Monte Carlo radiation transport for a reference CT scanner. Organ doses delivered by alternate CT scanner vendors and models may then be reported using ratios of normalized CTDI vol values under similar technique factors. Methods Scanners were selected from four major CT manufacturers: Philips Healthcare, GE Healthcare, Canon Medical Systems, and Siemens Healthineers. Technique parameters were also selected for each scanner that closely matched values used in the generation of an equivalent CT source term (small to large bowtie filters; 80–140‐kVp tube voltage; and 10‐mm to 40‐mm beam collimation). For each scanner chosen, the appropriate technique factors and protocols were selected, and the console‐reported CTDI vol values were recorded and normalized to a set value of 100 mAs. The normalized CTDI vol data collected for use within the MIRDct code were analyzed for noticeable patterns, features, and trends, and were compared to similar normalized CTDI vol datasets used within the National Cancer Institute NCICT software and the Virtual Phantoms, Inc. VirtualDose software. Results For all given CT scanner and technique factor combinations, there was strong agreement in normalized CTDI vol values across all three codes: between 0% and 12% difference for the compared scanners. Ratios of CTDI vol values for various CT scanner vendors and models to the corresponding CTDI vol values for the MIRDct reference scanner (Cannon Aquilion One Genesis) were also compared on the basis of either the 16‐cm head PMMA phantom or the 32‐cm body PMMA phantom. The mean quotient of these normalized CTDI vol ratios (head ratios to body ratios) was found to be approximately 1.06, and thus either ratio may be applied in reporting patient organ dose by MIRDct. Conclusions A database of normalized CTDI vol (mGy/100 mAs) was created for 17 models of CT scanners from four manufacturers at varying tube potentials, collimations, x‐ray bowtie filters, and phantom sizes for use in the MIRDct software.

ABSTRACT Background Ventricular tachycardia (VT) is a life‐threatening arrhythmia that requires both relatively rapid and accurate detection in intensive care units (ICUs). Continuous monitoring systems play a crucial role in detecting them. However, previous studies have reported that nearly 9 out of 10 arrhythmia alarms in ICUs tend to be false positives, which usually transpire to a well‐documented phenomenon called “alarm fatigue” that leads to desensitization, delayed responses, and increased cognitive burden on healthcare providers. Methods We developed a deep learning based, one‐dimensional convolutional neural network (1D‐CNN) to classify VT alarms using multiple raw waveform inputs, including two electrocardiogram (ECG) leads, photoplethysmogram (PPG) and arterial blood pressure (ABP) signals. The model was trained using the publicly available VTaC Arrhythmia Benchmark Dataset. We used the 10‐second waveform segments that preceded each VT alarm, pre‐processed and then used them to train the machine learning model to correctly classify the VT alarm. Results On the test set, the model achieved an area under the receiver operating characteristic curve of 0.901, overall accuracy of 83.22%, F1‐score of 73.3%, sensitivity of 77.53%, specificity of 85.63%, and positive predictive value of 69.57%. The model successfully detected over three‐quarters of them while significantly reducing false positive rates for the detection of VT. Conclusions This study demonstrates that a deep learning based 1D‐CNN model using short segments of raw waveform data can achieve robust performance in distinguishing true and false VT alarms.

Abstract Background Diabetes mellitus is a prevalent global health issue, affecting approximately 14% of individuals as latest reported. Among its complications, diabetic peripheral polyneuropathy (DPN) is a major cause of morbidity, impacting up to half of patients with long-standing diabetes. Given the limitations of current diagnostic methods, there is an urgent need for reliable biomarkers for early detection and management of DPN. Neurofilament light chain (NFL), a structural neuronal protein, has been proposed as a potential biomarker due to its release into the blood and cerebrospinal fluid following axonal damage. This systematic review and meta-analysis aim to evaluate serum NFL levels in diabetic patients with and without polyneuropathy compared to healthy controls. Methods A systematic search was conducted in four databases: Web of Science, PubMed, Scopus, and Embase. Studies were included if they measured serum NFL levels in diabetic patients, with or without polyneuropathy, and compared these levels to healthy controls. The Newcastle-Ottawa Scale (NOS) and JBI Critical Appraisal tools were used for quality assessment. We performed a meta-analysis using Review Manager 5.4 version, and assessed heterogeneity through chi-square tests and I² statistics. Results Eight studies (n = 2980 participants, including 950 diabetic patients and 2030 healthy controls) met the inclusion criteria. Three studies specifically evaluated serum NFL levels in diabetic patients with polyneuropathy. Meta-analysis revealed significantly elevated serum NFL levels in diabetic patients compared to healthy controls. Similarly, diabetic patients with polyneuropathy exhibited higher NFL levels than healthy controls. However, due to the limited number of studies comparing diabetic patients with and without neuropathy, a definitive conclusion on NFL's specificity to DPN remains uncertain. Heterogeneity analysis showed a moderate to high variation among studies, with a slight reduction after sensitivity analysis. Conclusion This study highlights elevated serum NFL levels in diabetic patients, irrespective of polyneuropathy status, suggesting that diabetes itself may contribute to increased NFL release. However, due to limited studies assessing NFL in DPN, further research is required to clarify its role as a specific biomarker for DPN.