Most Recent

Background: Prospective bedside data collection in perioperative research is labour-intensive and error-prone. Methods: We describe a workflow for prospective data capture in a 10-bed cardiac surgical intensive care unit: four Google Forms modules wr...

Background: Accurate determination of CYP2D6 copy number variation (CNV) is essential for reliable phenotype assignment in pharmacogenetics. Although digital PCR (dPCR) is widely used for CNV analysis, standardized approaches for selecting and probab...

Calculating the optimal cut-point of a predictor variable for a binary classification task is a crucial step in clinical practice, directly influencing diagnostic accuracy and patient management. This tutorial offers a practical, step-by-step guide f...

Background: Artificial intelligence (AI)-assisted imaging may support recognition of endometriosis, but perspectives on its use in adolescent care remain unclear. This study explored young people, parents, and health professionals’ views on its...

Background: Multivariable early-warning models can integrate physiological signals and missingness patterns, but added complexity may not provide meaningful value over simple predictors. We evaluated whether a single prespecified L2-regularized logis...

Invasive fungal infections cause approximately 1.5 billion cases each year, with an estimated 1.6 million deaths. Classic diagnostic methods, such as culture, direct microscopy, and antigen testing, largely rely on professional experience and are oft...

(1) Background: Modeling signaling cascades is helpful in deciphering the structure and interactions within and between cascades, and testing our understanding of the cascades by using the models to make predictions and suggesting experiments to eval...

Analyzing physiological signals with artificial intelligence (AI) has become a strategic tool for clinical diagnosis, event prediction, risk stratification, monitoring, and functional analysis, among other tasks. However, the growing use of AI in hea...

Automated breast ultrasound (ABUS) acquires whole-breast volumes that can be reviewed independently of acquisition, but tumor segmentation in these volumes remains difficult. This scoping review maps deep learning methods for tumor segmentation in AB...

Background: Preprocessing pipelines developed for retinal fundus photography are routinely transferred to other vascular imaging modalities without validation. Methods: Seven preprocessing stages from the retinal literature were assembled into a rule...

Background: Surgical instrument segmentation is a relevant computer vision task in minimally invasive surgery, where binary masks can be used to separate surgical instruments from the background. This study compares selected U-Net architectural confi...

Brain tumor classification from magnetic resonance imaging (MRI) can support computer-aided diagnosis by enabling automated differentiation of tumor and non-tumor categories. This study presents DualXBrNet, a hybrid dual-level fusion framework for mu...

Background: Recent research has highlighted the role of gut microbiota dysfunctions in Parkinson’s disease (PD), which may occur decades before the onset of motor symptoms. However, methodological variability across studies and complex host&nda...

Postoperative atrial fibrillation (POAF) is a leading cause of stroke and mortality after cardiac surgery, motivating continuous long-term ECG monitoring.This paper presents Cardiac-Net, a wearable ECG system (ESP32-S3/AD8232 BLE device with a Flutte...

Background: Diagnosing suspected abusive head trauma (AHT) requires the integration of clinical, imaging, ophthalmologic, and contextual information. The interpretation of these findings is often challenging because they differ in diagnostic relevanc...

Background: Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are progressive neurodegenerative disorders affecting over 100 million people worldwide. Effective connectivity (EC), which quantifies directional influence between brain re...

Glioma is the most common primary malignant brain tumor, with glioblastoma (GBM) carrying a 5-year survival rate below 10%. We analyzed 693 CGGA expression profiles, including 692 samples with available WHO grade (249 GBM and 443 lower-grade glioma [...

Breast cancer is a heterogeneous disease whose molecular complexity complicates therapeutic stratification. Although PAM50 is a widely used molecular subtyping framework, discrete subtype assignments may underrepresent transitional molecular states a...

Background/Objectives: Rabies remains a fatal viral disease, and current conventional vaccine strategies face significant challenges regarding structural stability and large-scale manufacturing. This study aimed to design a novel messenger ribonuclei...

(1) Background: Simultaneous evaluation of acute and chronic lesions is critical for staging renal disease, yet manual assessment suffers from high inter-observer variability and heavy workloads. While deep learning models excel at structural segment...

Artificial intelligence (AI) is changing the pharmaceutical industry at every stage of drug development. This review examines how AI is currently used and the recent progress in areas such as manufacturing, drug discovery, formulation, clinical trial...

Background: Healthcare information systems are effective at documenting encounters but remain less capable of representing evolving patient states, coordinating cross-disciplinary decisions, and reconnecting decisions with longitudinal outcomes and v...

Background: Accurate mapping of microarray probes to gene loci is essential for a reliable interpretation of gene expression data, yet available probe annotations are often incomplete or outdated. In this study, we aimed to develop and apply an exper...

Background: Monitoring unconsciousness under general anaesthesia typically relies on indirect physiological indicators rather than direct measures of brain activity. This study investigates a compact, real-time electroencephalography (EEG)-based moni...

Background: Rigid alignment of multimodal MRI is a prerequisite for neurological post-processing, yet classical iterative optimisers are accurate only when allowed to converge, at tens of seconds per volume, while learning-based predictors are fast b...

Atrial fibrillation (AF) detection from short single-lead electrocardiogram (ECG) recordings requires a balance between rhythm discrimination and computational efficiency. This study compares a one-dimensional ResNet-34 (1D-ResNet34), a Temporal Conv...

Background: U-Net-style encoder–decoder architectures are widely used for 3D medical image segmentation, but their conventional skip connections usually transfer encoder features to the decoder through static concatenation. Such direct skip fus...

Rapid selection of appropriate antimicrobial therapy is important for improving patient outcomes and supporting antimicrobial stewardship, particularly in the context of increasing antimicrobial resistance (AMR). Conventional antimicrobial susceptibi...

Artificial intelligence in cervical cytology requires controlled comparisons to distinguish the contribution of visual representations within the classification pipeline. This study evaluated six deep feature representation architectures for Bethesda...

Background: Breast imaging ends in a management decision, from routine return to biopsy, yet most models are evaluated by accuracy alone and say nothing about when they may be wrong. We present a trust-aware multimodal model that reads paired mammogr...

Background: Understanding transcriptomic state-transition dynamics is important for elucidating cancer progression and therapeutic response, yet existing single-cell transcriptomic approaches primarily characterize gene-expression changes or pseudote...

Background/Objectives: ATAC-seq footprinting can infer transcription-factor (TF) occupancy across the genome at near-base-pair resolution. However, its broad adoption is limited by high computational demands, complex command-line workflows, and fragm...

Background: The growing volume of DNA sequence data demands efficient metagenomic identification. It provides the possibility of constructing tools with sustainability performance to monitor environmental conditions, including risks related to organi...

Background: Early and accurate detection of depression from multimodal data is a critical yet challenging task. Many existing models either rely on a single modality or exhibit limited robustness across datasets, while publicly available datasets are...

Cardiovascular risk assessment remains a central challenge in both population-based prevention and high-risk clinical settings. We benchmarked a previously developed mechanistic model of cardiovascular ageing against machine-learning baselines across...

Background: Biomedical spectroscopy offers a non-invasive way to characterise disease, but the large number of spectral variables, redundancy, and limited interpretability can hinder the creation of compact diagnostic models. This study presents the...

Rapid and accurate diagnosis is crucial for the early detection of malaria caused by Plasmodium parasites. This study primarily focuses on identifying a suitable deep learning model for classifying malaria parasites. In an ablation process, we starte...

Skin cancer is one of the most common diseases worldwide and, if left untreated, it can be life threatening. In this work, we propose a dual-branch deep learning framework that integrates a CNN–BiLSTM module for local spatial–sequential f...

This review consists of three parts related to the AT(N) framework, providing a comprehensive overview of biomarker-based diagnosis of Alzheimer’s disease (AD). In recent years, management of AD has shifted from a symptom-based approach toward...

Background: One-lung ventilation (OLV) is routinely used during thoracic surgery. Static respiratory risk scores may not capture the physiological changes that occur after lung isolation. This study evaluated whether an early intraoperative risk asse...

Early identification of individuals with abnormal glucose metabolism is essential for timely intervention and prevention of diabetes-related complications. Routine laboratory testing generates large amounts of clinical data that may support automated...

Background: Accurate prognosis in diffuse large B-cell lymphoma (DLBCL) is limited by biological heterogeneity and the absence of formal per-patient uncertainty quantification for treatment-response prediction. This study introduces a multi-layer evi...

Reproducible high-performance computing pipelines for transcriptomic analysis are essential for population-scale precision oncology, yet most published workflows address only a single sequencing modality or lack benchmarking on underrepresented popul...

Background: Near-perfect machine-learning performance on public clinical datasets can arise from data leakage or predictors unavailable at the intended prediction time. We audited a breast cancer vital-status workflow and established a censoring-awar...

Background: Deep learning models, particularly convolutional neural networks (CNNs), have shown promising performance for pneumonia detection using chest X-ray images. However, the impact of preprocessing, architecture selection, data augmentation, a...

Digital health observatories require predictive frameworks that transform administrative health data into reliable territorial risk indicators. We developed a biomedical informatics framework for machine-learning-based epidemiological risk stratifica...

Diabetic foot ulcers (DFUs) are a major cause of lower-extremity amputation, and image-based decision support may assist screening and preliminary triage where specialist access is limited. We propose DFU-MambaKAN, a hybrid architecture combining a p...

Accurate prediction of drug–target interactions (DTIs) is a cornerstone of computational drug discovery, with the potential to reduce experimental costs and accelerate therapeutic development. Although deep learning methods such as graph neural...

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, driving the need for reliable tools for early risk prediction. Artificial intelligence (AI) applied to electrocardiograms (ECGs) has shown strong predictive performance f...

The LW-9 peptide was previously identified through screening of purified molecules from the venom of the spider Phoneutria nigriventer and has been described as an immunomodulatory compound with potential application in cancer therapy. The molecule w...