Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disci...
Background: Depression is a common mental disorder, and early and objective diagnosis of depression is challenging. New advances in deep learning show promise for processing audio and video content when screening for depression. Nevertheless, the maj...
Artificial intelligence (AI) has shown promising performance in brain tumor diagnosis and prognosis; however, most reported advances remain difficult to translate into clinical practice due to limited interpretability, inconsistent evaluation protoco...
The lack of clinical data for chronic kidney disease (CKD) prediction frequently results in model overfitting and inadequate generalization to novel samples. This research mitigates this constraint by utilizing a Conditional Tabular Generative Advers...
Evaluating the difficulty of endotracheal intubation during pre-anesthesia assessment has consistently posed a challenge for clinicians. Accurate prediction of intubation difficulty is crucial for subsequent treatment planning. However, existing diag...
Background: Sepsis-associated liver injury (SALI) is a serious complication of sepsis that increases the risk of organ dysfunction and mortality; however, early identification of high-risk patients remains difficult due to nonspecific clinical featur...
Chest X-ray radiology report generation is a challenging task that involves techniques from medical natural language processing and computer vision. This paper provides a comprehensive overview of recent progress. The annotation protocols, structure,...
Background/Objectives: Brain metastases (BM) are a major challenge in the treatment of non-small cell lung cancer (NSCLC), particularly among patients with anaplastic lymphoma kinase rearrangements (ALK+ NSCLC), where incidence can reach up to 60% du...
Acne vulgaris (AV) is a common dermatological disorder in adolescents, encompassing both non-inflammatory and inflammatory lesions, with growing evidence implicating the skin microbiome in its pathogenesis. This study analyzed skin lesion samples fro...
Background: Due to the complexity of venous ulcer treatment, the role of nurses is critical, and artificial intelligence, particularly artificial neural networks of the Multilayer Perceptron type, can be effective tools that support professionals wit...
Vancomycin remains a cornerstone for severe Gram-positive infections in the ICU, yet creatinine elevation—a sensitive marker of early renal stress—occurs frequently and complicates therapy. We developed a machine learning model to predict...
Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models gen...
Artificial intelligence (AI) is rapidly emerging as a transformative tool capable of addressing critical challenges and improving outcomes in tissue engineering and regenerative medicine. This paper demonstrates how machine learning and data fusion p...
Modern diagnostic systems face computational challenges when processing exponential disease-symptom combinations, with traditional approaches requiring up to
Background/Objectives: This review paper summarizes and critically analyzes different Machine Learning (ML) and Artificial Intelligence (AI)-based predictive modeling techniques in early detection and personalized treatment for Kidney diseases, speci...
Background: Salivary gland tumors pose a diagnostic challenge due to their histological heterogeneity and overlapping features. While immunohistochemistry (IHC) is critical for accurate classification, selecting appropriate markers can be subjective...
Artificial Intelligence (AI) is reshaping pharmacy practice by enhancing decision-making, personalizing therapy, and improving medication safety. AI applications now span drug discovery, clinical decision support, and adherence monitoring. This narra...
Background: Dopamine (DA) is a key biomarker for neurodegenerative diseases such as Parkinson’s. However, detailed insights into how DA release in the brain changes with aging remain challenging. Integrating machine learning with DA sensing pla...
Background/Objectives: Endometriosis is a chronic inflammatory condition that often requires laparoscopic examination for definitive diagnosis. Automated analysis of laparoscopic images using Deep Learning (DL) may support clinicians by improving dia...
Introduction: The digital era has provided the development of innovative health devices that enable the precise characterization of health and disease, facilitating diagnoses and interventions. This study aimed to systematically review and verify the...
Background: The growing demand for automated microorganism classification in the context of Laboratory 4.0 highlights the potential of convolutional neural networks (CNNs) for accurate and efficient image analysis. However, their effectiveness remain...
Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized primarily by motor impairments due to the loss of dopaminergic neurons. Despite extensive research, the precise causes of PD remain unknown, and reliab...
Background: Reconstructing gene regulatory networks (GRNs) from gene expression data remains a major challenge in systems biology due to the inherent complexity of biological systems and the limitations of existing reconstruction methods, which often...
Extensive research is being carried out on the application of
Background: Urinary tract infections (UTIs) remain among the most common bacterial infections, yet reliable risk stratification remains challenging. Serum vitamin D has been linked to immune regulation, but its predictive role in UTI subtypes is uncl...
Accurate spleen segmentation in abdominal CT scans remains a critical challenge in medical image analysis due to variable morphology, low tissue contrast, and proximity to similar anatomical structures. This paper presents an enhanced U-Net architect...
17 September 2025
Background: The selection of machine learning (ML) models in the biomedical sciences often relies on global performance metrics. When these metrics are closely clustered among candidate models, identifying the most suitable model for real-world deplo...
11 September 2025
This paper investigates the state of substance use disorder (SUD) and the frequency of substance use by utilizing three unsupervised machine learning techniques, based on the Diagnostic and Statistical Manual 5 (DSM-5) of mental health disorders. We...
Background: To propose an automatic liver and hepatic vessel segmentation solution based on a stacking model and decision fusion. This model combines the decisions of multiple models to achieve increased accuracy. It exhibits improved robustness due...
Background: Pediatric Intensive Care Unit (PICU) outcome prediction is challenging, and machine learning (ML) can enhance it by leveraging large datasets. Methods: We built an ML model to predict PICU outcomes (“Death vs. Survival”, &ldqu...
Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inhere...
Background: Virtual coaching can help people adopt new healthful behaviors by encouraging them to set specific goals and helping them review their progress. One challenge in creating such systems is analyzing clients’ statements about their act...
Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in under...
Virtual-reality exposure therapy (VRET) is an emerging treatment for psychiatric disorders that enables immersive and controlled exposure to anxiety-provoking stimuli. Recent developments integrate real-time physiological monitoring, including heart...
Background: Human Metapneumovirus (HMPV) is a respiratory virus in the Pneumoviridae family. HMPV is an enveloped, negative-sense RNA virus encoding three surface proteins: SH, G, and F. The highly immunogenic fusion (F) protein is essential for vira...
Breast cancer remains a critical global health challenge, with over 2.1 million new cases annually. This review systematically evaluates recent advancements (2022–2024) in machine and deep learning approaches for breast cancer detection and ris...
Background: The amount of data produced from biological experiments has increased geometrically, posing a challenge for the development of new methodologies that could enable their interpretation. We propose a novel approach for the analysis of trans...
Accurate segmentation of kidney microstructures in whole slide images (WSIs) is essential for the diagnosis and monitoring of renal diseases. In this study, an end-to-end instance segmentation pipeline was developed for the detection of glomeruli and...
Skin cancer is the most common cancer worldwide, for which early detection is crucial to improve survival rates. Visual inspection and biopsies have limitations, including being error-prone, costly, and time-consuming. Although several deep learning...
Background: Prostate cancer (PC) is the second leading cause of cancer-related death in men in the United States. A subset of patients develops non-metastatic, castration-resistant PC (nmCRPC), for which management requires a personalized considerati...
This paper proposes a hybrid method for skin lesion classification combining deep learning features with conventional descriptors such as HOG, Gabor, SIFT, and LBP. Feature extraction was performed by extracting features of interest within the tumor...
Background: Social media represents a unique opportunity to investigate the perspectives of people with eating disorders at scale. One forum alone, r/EatingDisorders, now has 113,000 members worldwide. In less than a day, where a manual analysis migh...
Lung-cancer incidence is projected to rise by 50% by 2035, underscoring the need for accurate yet accessible risk-stratification tools. We trained a Bayesian neural network on 300 annotated chest-CT scans from the public LIDC–IDRI cohort, integ...
Technological advancements and AI-based research have significantly influenced our daily lives. Human activity recognition (HAR) is a key area at the intersection of various AI technologies and application domains. In this study, we present our novel...
Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (...
Food pattern recognition plays a crucial role in modern healthcare by enabling automated dietary monitoring and personalised nutritional interventions, particularly for vulnerable populations with complex dietary needs. Current food recognition syste...
Background: Anemia, a common health disorder affecting populations globally, demands timely and accurate diagnosis for treatment to be effective. The aim of this paper is to detect and classify four types of anemia: hgb, iron-deficiency, folate-defic...
Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-dr...
Background: Chronic diseases significantly burden healthcare systems due to the need for long-term treatment. Early diagnosis is critical for effective management and minimizing risk. The current traditional diagnostic approaches face various challen...
Background: Lung adenocarcinoma is one of the major subtype of non-Small Cell Lung Cancer and biomarkers are essential to be identified for early diagnosis. The study aims to find in silico and preliminary in vitro analysis of potential biomarkers fo...
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