AIMC Topic: Prospective Studies
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A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.
Urologic oncology
Nov 7, 2024
OBJECTIVE: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data ...
PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.
Nature communications
Nov 7, 2024
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...
Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.
AJNR. American journal of neuroradiology
Nov 7, 2024
BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been ...
Utilizing artificial intelligence to determine bone mineral density using spectral CT.
Bone
Nov 6, 2024
BACKGROUND: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring dedicated software or calibration. Artificial intelligence (AI) has been utilized for diagnosising oste...
Artificial Intelligence Compared to Manual Selection of Prospective Surgical Residents.
Journal of surgical education
Nov 6, 2024
BACKGROUND: Artificial Intelligence (AI) in the selection of residency program applicants is a new tool that is gaining traction, with the aim of screening high numbers of applicants while introducing objectivity and mitigating bias in a traditionall...
The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.
World journal of urology
Nov 1, 2024
PURPOSE: To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis.
Exploring the feasibility of FOCUS DWI with deep learning reconstruction for breast cancer diagnosis: A comparative study with conventional DWI.
PloS one
Oct 31, 2024
PURPOSE: This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging.
Artificial Intelligence-Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography-Guided PCI: The FLASH Trial.
JACC. Cardiovascular interventions
Oct 30, 2024
BACKGROUND: Recently developed artificial intelligence-based coronary angiography (AI-QCA, fully automated) provides real-time, objective, and reproducible quantitative analysis of coronary angiography without requiring additional time or labor.
Time Factors
Female
Image Interpretation, Computer-Assisted
Artificial Intelligence
Treatment Outcome
Aged
Automation
Coronary Angiography
Humans
Male
Prospective Studies
Predictive Value of Tests
Coronary Vessels
Percutaneous Coronary Intervention
Middle Aged
Republic of Korea
Tomography, Optical Coherence
Coronary Artery Disease
Stents
Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.
ESC heart failure
Oct 27, 2024
AIMS: Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).
Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.
Obesity surgery
Oct 25, 2024
PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver...
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