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MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.
Scientific reports
Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.
Journal for immunotherapy of cancer
Mar 3, 2025
BACKGROUND: Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifyin...
Does the alternating timing of rTMS combined with soft-hand rehabilitation robot affect the recovery of hand function in patients after stroke? A study protocol for a multicentre randomised controlled trial.
BMJ open
Mar 3, 2025
INTRODUCTION: Combining repetitive transcranial magnetic stimulation (rTMS) with robotic training could result in more significant improvements in motor function than either treatment alone. The efficacy of this combination may depend on the sequenci...
Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.
Cardiovascular ultrasound
Mar 3, 2025
BACKGROUND: The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-lines present on lung ultrasound (LUS) serve as a useful tool for the assessment of pulmonary congest...
Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.
Frontiers in immunology
Mar 3, 2025
BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.
Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.
International journal of cardiology
Mar 2, 2025
BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE imag...
Efficacy and Safety of a Medical Robot for Non-Face-to-Face Nasopharyngeal Swab Specimen Collection: Nonclinical and Clinical Trial Findings for COVID-19 Testing.
American journal of rhinology & allergy
Mar 2, 2025
ObjectivesTo meet the high demand for polymerase chain reaction (PCR) tests to diagnose COVID-19 and rapidly control the outbreak, an efficient and safe molecular diagnostic protocol is necessary. In this study, we evaluated the efficacy and safety o...
Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.
Expert review of respiratory medicine
Mar 2, 2025
BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise that is often not available. We aim to evaluate the utility of ChatGPT (GPT) in interpreting CPET res...
The Extent to Which Artificial Intelligence Can Help Fulfill Metastatic Breast Cancer Patient Healthcare Needs: A Mixed-Methods Study.
Current oncology (Toronto, Ont.)
Mar 2, 2025
The Artificial Intelligence Patient Librarian (AIPL) was designed to meet the psychosocial and supportive care needs of Metastatic Breast Cancer (MBC) patients with HR+/HER2- subtypes. AIPL provides conversational patient education, answers user ques...
Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.
Biomedical engineering online
Mar 2, 2025
BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.
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