AIMC Topic: Middle Aged

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Enhancing Work Efficiency With Generative Artificial Intelligence: Experience and Training Insights From School Nurses Through Focus Groups and Surveys.

International nursing review
AIM: This study examined the use of generative artificial intelligence (GAI), factors related to its application, and training expectations among school nurses who enrolled as seed instructors in a GAI training program.

Simultaneous T and ADC Mapping of Acute-to-Chronic Ischemic Stroke With Multiple Overlapping-Echo Detachment Imaging.

NMR in biomedicine
Multiparametric quantitative MRI based on multiple overlapping-echo detachment imaging (MQMOLED) can simultaneously quantify T and ADC with whole brain coverage within 40 s. T and ADC play an important role in the assessment and management of ischemi...

A preliminary exploration of surgical strategies for solitary papillary thyroid carcinoma on the isthmus.

Oral oncology
BACKGROUND: For solitary papillary thyroid carcinoma on the isthmic (SPTCI), there are currently no specific guidelines for the extent of resection and lymph node dissection. This study aims to explore the surgical strategies suitable for patients wi...

`Probabilistic ensemble learning for prediction of stroke thrombectomy outcomes from the NeuroVascular Quality Initiative-Quality Outcomes Database (NVQI-QOD) Acute Ischemic Stroke Registry.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Mechanical Thrombectomy (MT) is the standard of care in the interventional management of Acute Ischemic Stroke (AIS). The NVQI-QOD registry records detailed patient characteristics, pre-operative imaging, procedure metrics, and post-ope...

Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease.

Radiology. Artificial intelligence
Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to perform parametric response mapping (PRM) and predict functional small airways disease (fSAD). Materials and Methods In this retrospective study, predictive and ...

A Deep Learning Model for Comprehensive Automated Bone Lesion Detection and Classification on Staging Computed Tomography Scans.

Academic radiology
RATIONALE AND OBJECTIVES: A common site of metastases for a variety of cancers is the bone, which is challenging and time consuming to review and important for cancer staging. Here, we developed a deep learning approach for detection and classificati...

Prediction model for postoperative urinary retention in patients undergoing totally extraperitoneal groin hernia repair.

Surgery
BACKGROUND: Postoperative urinary retention remains a common complication after totally extraperitoneal groin hernia repair, often prolonging hospitalization and increasing patient discomfort. This study aimed to develop a prediction model using mach...

Deep learning for giant cell arteritis diagnosis on temporal artery biopsy.

Computers in biology and medicine
OBJECTIVES: Giant Cell Arteritis (GCA) is a vasculitis affecting large and medium-caliber arteries, requiring early and accurate diagnosis to prevent serious complications. Temporal artery biopsy (TAB) is the gold standard for histopathological diagn...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Radiology. Artificial intelligence
Purpose To improve the generalizability of pathologic complete response prediction following neoadjuvant chemotherapy using deep learning-based retrospective pharmacokinetic quantification of early treatment dynamic contrast-enhanced MRI. Materials a...

Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

Neurourology and urodynamics
INTRODUCTION: Individuals with spinal cord injury (SCI) have varying bladder health trajectories after their injury. We explored whether a predictive machine learning model could identify which variables impact urinary symptoms.