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Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.
BMC pregnancy and childbirth
Jul 2, 2025
OBJECTIVE: To assess the methodological quality and the risk of bias, of studies that developed prediction models using Machine Learning (ML) techniques to estimate prenatal birthweight.
Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.
BMC pregnancy and childbirth
Jul 2, 2025
BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robus...
Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China.
BMC geriatrics
Jul 2, 2025
OBJECTIVE: Depression in older adults is a growing public health concern, yet there is still a lack of convenient and real-time methods for depressive symptoms identification. This study aims to develop a gait-based depression recognition method for ...
Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.
Scientific reports
Jul 2, 2025
Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive manual interpretation as well as unavoidable interpreter-dependent variabilities. We propose artific...
Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.
Scientific reports
Jul 2, 2025
Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates numerous cellular protein receptors such as estrogen receptors (ER), progesterone receptors (PR), and hu...
A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX).
Scientific reports
Jul 2, 2025
Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to understanding how AI models interpret electrocardiograms (ECGs). Traditional explanation methods focus on highlighting important ECG segments but often fai...
The effectiveness of explainable AI on human factors in trust models.
Scientific reports
Jul 2, 2025
Explainable AI has garnered significant traction in science communication research. Prior empirical studies have firmly established that explainable AI communication could improve trust in AI and that trust in AI engineers was argued to be an under-e...
Listening deeper: neural networks unravel acoustic features in preterm infant crying.
Scientific reports
Jul 2, 2025
Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more compr...
A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.
Scientific reports
Jul 2, 2025
Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team prop...
Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.
Nature communications
Jul 2, 2025
Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the...
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