AIMC Topic: Retrospective Studies
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Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration.
Technology in cancer research & treatment
Apr 17, 2025
IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metastasis using primary breast cancer and lymph node features derived from contrast-enhanced mammography...
An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.
Technology in cancer research & treatment
Apr 17, 2025
IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and...
Comparison of CNNs and Transformer Models in Diagnosing Bone Metastases in Bone Scans Using Grad-CAM.
Clinical nuclear medicine
Apr 16, 2025
PURPOSE: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and transformer models has not yet been explored. This study aims to evaluate the performance of various...
Quantitative Ischemic Lesions of Portable Low-Field Strength MRI Using Deep Learning-Based Super-Resolution.
Stroke
Apr 16, 2025
BACKGROUND: Deep learning-based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low-field strength magnetic resonance imaging (LF-MRI). The aim of this study is to evaluate ...
Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell carcinoma.
Oral oncology
Apr 16, 2025
OBJECTIVE: Despite growing interest in neoadjuvant therapies, there are no methods to predict radio- (RT) or chemoradiotherapy (CRT) response in head and neck squamous cell carcinoma (HNSCC). The aim of this research was to study the effect of neoadj...
Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis.
Scientific reports
Apr 16, 2025
Hip fractures in the elderly present a significant public health challenge globally, especially among patients with hypertension, who are at an increased risk of developing preoperative deep vein thrombosis (DVT). DVT not only heightens surgical risk...
Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods.
Scientific reports
Apr 16, 2025
The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, but the full extent of long-term risks remains uncertain. Addressing this knowledge deficit, a retros...
Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer.
Frontiers in endocrinology
Apr 16, 2025
RESEARCH QUESTION: Can machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?
Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.
Frontiers in public health
Apr 16, 2025
BACKGROUND: Exposure to heavy metals has been implicated in adverse auditory health outcomes, yet the precise relationships between heavy metal biomarkers and hearing status remain underexplored. This study leverages a machine learning framework to i...
The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians.
Journal of primary care & community health
Apr 16, 2025
BACKGROUND: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to...
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