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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.
Essential competencies of nurses working with AI-driven lifestyle monitoring in long-term care: A modified Delphi study.
Nurse education today
Mar 1, 2025
BACKGROUND: As more and more older adults prefer to stay in their homes as they age, there's a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed ...
Deep learning for tibial plateau fracture detection and classification.
The Knee
Mar 1, 2025
BACKGROUND: Deep learning (DL) has been shown to be successful in interpreting radiographs and aiding in fracture detection and classification. However, no study has aimed to develop a computer vision model for tibia plateau fractures using the Schat...
Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 1, 2025
AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).
Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.
Neuroscience
Mar 1, 2025
This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and extracranial plaques, which is crucial for stroke treatment and prevention. We retrospectively analy...
Predicting 30-day mortality in hemophagocytic lymphohistiocytosis: clinical features, biochemical parameters, and machine learning insights.
Annals of hematology
Mar 1, 2025
This study aims to evaluate the clinical characteristics and biochemical parameters of hemophagocytic lymphohistiocytosis (HLH) patients to predict 30-day mortality. Parameters analyzed include lymphocyte count (L), platelet count (PLT), total protei...
An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.
Annals of the rheumatic diseases
Mar 1, 2025
OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis patients and identify important clinical indicators of PsA in primary care.
Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility.
Neuroradiology
Mar 1, 2025
PURPOSE: Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoperative nerve injury. However, it is often difficult to evaluate them with preoperative imaging. We developed a software that automatically generates th...
Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.
Asian Pacific journal of cancer prevention : APJCP
Mar 1, 2025
OBJECTIVE: Ovarian cancer's complexity and heterogeneity pose significant challenges in treatment, often resulting in suboptimal chemotherapy outcomes. This study aimed to leverage machine learning algorithms, gene selection, and gene expression dat...
Diffusion-Weighted Imaging-Based Radiomics Features and Machine Learning Method to Predict the 90-Day Prognosis in Patients With Acute Ischemic Stroke.
The neurologist
Mar 1, 2025
OBJECTIVES: The evaluation of the prognosis of patients with acute ischemic stroke (AIS) is of great significance in clinical practice. We aim to evaluate the feasibility and effectiveness of diffusion-weighted imaging (DWI) image-based radiomics fea...
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