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Artificial intelligence in neurovascular decision-making: a comparative analysis of ChatGPT-4 and multidisciplinary expert recommendations for unruptured intracranial aneurysms.

Neurosurgical review Feb 21, 2025
In the multidisciplinary treatment of cerebrovascular diseases, specialists from different disciplines strive to develop patient-specific treatment recommendations. ChatGPT is a natural language processing chatbot with increasing applicability in med...
Decision Making Natural Language Processing Intracranial Aneurysm Humans Artificial Intelligence Clinical Decision-Making Female Aged Adult Male Middle Aged Generative Artificial Intelligence
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Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.

Frontiers in public health Feb 21, 2025
BACKGROUND: It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed f...
Middle Aged Risk Factors Humans Aged Machine Learning Adult Algorithms Male Female Depressive Disorder, Major Nutrition Surveys United States
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Early prediction of 30-day mortality in patients with surgical wound infections following cardiothoracic surgery: Development and validation of the SWICS-30 score utilizing conventional logistic regression and artificial neural network.

The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases Feb 21, 2025
INTRODUCTION: We aimed to create and validate the 30-day prognostic score for mortality in patients with surgical wound infection (SWICS-30) after cardiothoracic surgery.
Humans Neural Networks, Computer Prognosis Retrospective Studies Aged Aged, 80 and over Young Adult Female Risk Factors Adolescent Hospital Mortality Logistic Models Cardiac Surgical Procedures Surgical Wound Infection Adult Male Middle Aged Risk Assessment
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Artificial intelligence-based model to predict recurrence after local excision in T1 rectal cancer.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Feb 20, 2025
BACKGROUND: According to current guideline, patients with resected specimens showing high-risk features are recommended additional surgery after local excision (LE) of T1 colorectal cancer, despite the low incidence of recurrence. However, surgical r...
Humans Neural Networks, Computer Neoplasm Staging Neoplasm Recurrence, Local Rectal Neoplasms Proctectomy Male Artificial Intelligence Middle Aged Retrospective Studies Risk Assessment Female ROC Curve Aged
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System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method.

Neurocritical care Feb 20, 2025
BACKGROUND: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests usin...
Heart Arrest Glasgow Coma Scale Male Middle Aged Electroencephalography Outcome Assessment, Health Care Prognosis Humans Female Machine Learning Aged
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Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.

Annals of surgical oncology Feb 20, 2025
BACKGROUND: Postdischarge venous thromboembolism (pdVTE) is a life-threatening complication following resection for pancreatic cancer (PC). While national guidelines recommend extended chemoprophylaxis for all, adherence is low and ranges from 1.5 to...
Venous Thromboembolism Pancreatectomy Risk Factors Aged Male Middle Aged Decision Trees Postoperative Complications Pancreatic Neoplasms Machine Learning Follow-Up Studies Humans Patient Discharge Prognosis Retrospective Studies Female
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Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training.

Endocrine Feb 20, 2025
PURPOSE: This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thyroid nodules.
Thyroid Neoplasms Humans Adult Thyroid Nodule Artificial Intelligence Male Radiology Female Diagnosis, Computer-Assisted Internal Medicine Clinical Competence Internship and Residency
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Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.

American journal of ophthalmology Feb 20, 2025
PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection of anti-vascular endothelial growth factor (anti-VEGF) with data from optical coherence tomography ...
Vascular Endothelial Growth Factor A Intravitreal Injections Ranibizumab Bevacizumab Follow-Up Studies Humans Visual Acuity Diabetic Retinopathy Middle Aged Tomography, Optical Coherence Neural Networks, Computer Macular Edema Convolutional Neural Networks Retrospective Studies Pilot Projects Female Angiogenesis Inhibitors Aged Treatment Outcome Male
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T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

Journal of applied clinical medical physics Feb 20, 2025
PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).
Female Aged Aged, 80 and over Adult Humans Retrospective Studies Image Processing, Computer-Assisted Magnetic Resonance Imaging Rectal Neoplasms Male Middle Aged Deep Learning Imaging, Three-Dimensional
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AI in medical diagnosis: A contextualised study of patient motivations and concerns.

Social science & medicine (1982) Feb 20, 2025
Patients' reactions to the implementation of Artificial Intelligence (AI) in healthcare range from adverse to favourable. While AI holds the promise of revolutionising healthcare by enhancing, accelerating, and improving the precision of care service...
Patients Middle Aged Qualitative Research Surveys and Questionnaires Motivation Female Humans Adult Artificial Intelligence Male
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