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The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.
Journal of medical Internet research
May 28, 2024
BACKGROUND: Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of th...
Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.
Frontiers in public health
May 28, 2024
INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to p...
Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.
Frontiers in endocrinology
May 28, 2024
INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve pati...
Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm.
Academic radiology
May 27, 2024
RATIONALE AND OBJECTIVES: We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm.
Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis.
Clinical radiology
May 27, 2024
AIM: The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features.
A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.
Radiation oncology (London, England)
May 27, 2024
BACKGROUND: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surg...
Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.
BMC medical informatics and decision making
May 27, 2024
BACKGROUND: Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) tech...
Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.
BMC neurology
May 27, 2024
BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the e...
Proximal femur fracture detection on plain radiography via feature pyramid networks.
Scientific reports
May 27, 2024
Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a de...
Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.
Abdominal radiology (New York)
May 26, 2024
PURPOSE: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.
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