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Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.
BMC medical informatics and decision making
Feb 5, 2025
BACKGROUND: Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patient information as key diagnostic factors. However, these techniques have either been time-consuming ...
Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.
Scientific reports
Feb 5, 2025
Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different ...
Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.
Scientific reports
Feb 5, 2025
This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and p...
High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies.
European radiology
Feb 4, 2025
OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary compu...
Age-stratified deep learning model for thyroid tumor classification: a multicenter diagnostic study.
European radiology
Feb 4, 2025
OBJECTIVES: Thyroid cancer, the only cancer that uses age as a specific predictor of survival, is increasing in incidence, yet it has a low mortality rate, which can lead to overdiagnosis and overtreatment. We developed an age-stratified deep learnin...
Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Clinical breast cancer
Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.
Medical & biological engineering & computing
Feb 4, 2025
Diagnosing Parkinson's disease (PD) via speech is crucial for its non-invasive and convenient data collection. However, the small sample size of PD speech data impedes accurate recognition of PD speech. Therefore, we propose a novel multi-source spar...
A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial Nephrectomy.
Urology
Feb 4, 2025
OBJECTIVE: To test if our artificial intelligence (AI)-postoperative glomerular filtration rate (GFR) prediction is as accurate as a validated clinical model. The American Urologic Association recommends estimating postoperative GFR in patients with ...
Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease.
Parkinsonism & related disorders
Feb 4, 2025
INTRODUCTION: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons with Parkinson's disease (PD). The purpose of this study was to develop a short form of the WHODAS 2.0...
Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 4, 2025
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...
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