AIMC Topic: Retrospective Studies

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An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.

Scientific reports
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrosp...

The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.

JMIR research protocols
BACKGROUND: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research ...

Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.

Journal of medical Internet research
BACKGROUND: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accura...

A retrospective analysis of medical student authorship in highest impact radiology journal publications.

Current problems in diagnostic radiology
OBJECTIVES: To assess medical student authorship in radiology research, focusing on the prevalence and impact of student articles, demographic trends, and potential barriers and opportunities for involvement.

Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning.

Frontiers in immunology
BACKGROUND: Immune checkpoint inhibitors have proven efficacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related adverse reactions are still a major challenge faced by tumor immunotherapy, so it is urgent to establish n...

Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on co...

Machine Learning Model for Risk Stratification of Papillary Thyroid Carcinoma Based on Radiopathomics.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to develop a radiopathomics model based on preoperative ultrasound and fine-needle aspiration cytology (FNAC) images to enable accurate, non-invasive preoperative risk stratification for patients with papilla...

Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.

La Radiologia medica
PURPOSE: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.