AIMC Topic: Observer Variation

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Using a large language model (ChatGPT) to assess risk of bias in randomized controlled trials of medical interventions: protocol for a pilot study of interrater agreement with human reviewers.

BMC medical research methodology Jul 31, 2025
BACKGROUND: Risk of bias (RoB) assessment is an essential part of systematic reviews that requires reading and understanding each eligible trial and RoB tools. RoB assessment is subject to human error and is time-consuming. Machine learning-based too...
Humans Machine Learning Randomized Controlled Trials as Topic Bias Systematic Reviews as Topic Observer Variation Large Language Models Research Design Language Risk Assessment Pilot Projects Generative Artificial Intelligence
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Nancy histological index in ulcerative colitis: an interobserver study.

Journal of clinical pathology Jul 18, 2025
The Nancy histological index (NHI) is a measure of disease activity within colorectal biopsies and a predictor of clinical outcome in ulcerative colitis (UC). We measured interobserver agreement (IOA) during NHI scoring of 20 colorectal biopsies in U...
Colitis, Ulcerative Biopsy Observer Variation Reproducibility of Results Male Colon Humans Severity of Illness Index Female
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A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images.

BMC medical imaging May 1, 2025
BACKGROUND: The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-con...
Aged, 80 and over Young Adult Adult Male Middle Aged Deep Learning Adolescent Radiographic Image Interpretation, Computer-Assisted Observer Variation Adrenal Glands Humans Algorithms Tomography, X-Ray Computed Female Aged
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Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.

The international journal of cardiovascular imaging Apr 29, 2025
Real time 2D phase contrast (RTPC) MRI is useful for flow quantification in atrial fibrillation (AF) patients, but data analysis requires time-consuming anatomical contouring for many cardiac time frames. Our goal was to develop a convolutional neura...
Predictive Value of Tests Atrial Fibrillation Heart Atria Heart Rate Image Interpretation, Computer-Assisted Observer Variation Automation Blood Flow Velocity Atrial Function, Left Magnetic Resonance Imaging, Cine Male Middle Aged Deep Learning Time Factors Magnetic Resonance Imaging Humans Female Reproducibility of Results Aged
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Artificial intelligence-assisted left ventricular global longitudinal strain assessment in patients with acute myocardial infarction: a RESUS-AMI trial sub-analysis.

The international journal of cardiovascular imaging Apr 29, 2025
PURPOSE: The aim of this sub-analysis of the RESUS-AMI trial was to evaluate the correlation of artificial intelligence (AI)-assisted echocardiographic global longitudinal strain (GLS) assessments with infarct size, left ventricular ejection fraction...
Aged Male Ventricular Function, Left Stroke Volume Echocardiography Humans Artificial Intelligence Female Reproducibility of Results Image Interpretation, Computer-Assisted Observer Variation Magnetic Resonance Imaging, Cine Myocardial Contraction Global Longitudinal Strain Middle Aged Treatment Outcome Predictive Value of Tests ST Elevation Myocardial Infarction Percutaneous Coronary Intervention
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Fast and automatic coronary artery segmentation using nnU-Net for non-contrast enhanced magnetic resonance coronary angiography.

The international journal of cardiovascular imaging Apr 26, 2025
Non-contrast enhanced magnetic resonance coronary angiography (MRCA) is a promising coronary heart disease screening modality. However, its clinical application is hindered by inherent limitations, including low spatial resolution and insufficient co...
Coronary Angiography Coronary Artery Disease Image Interpretation, Computer-Assisted Observer Variation Automation Coronary Vessels Magnetic Resonance Angiography Computed Tomography Angiography Male Humans Middle Aged Female Deep Learning Reproducibility of Results Time Factors Aged Predictive Value of Tests
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Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.

Tomography (Ann Arbor, Mich.) Apr 17, 2025
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...
Observer Variation Deep Learning Humans Aged Shoulder Magnetic Resonance Imaging Prospective Studies Adult Edema Female Rotator Cuff Injuries Sensitivity and Specificity Shoulder Joint Reproducibility of Results Male Image Interpretation, Computer-Assisted Middle Aged
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Towards clinical implementation of automated segmentation of vestibular schwannomas: a reliability study comparing AI and human performance.

Neuroradiology Apr 4, 2025
PURPOSE: To evaluate the clinimetric reliability of automated vestibular schwannoma (VS) segmentations by a comparison with human inter-observer variability on T1-weighted contrast-enhanced MRI scans.
Tumor Burden Humans Image Interpretation, Computer-Assisted Observer Variation Adult Neuroma, Acoustic Male Artificial Intelligence Middle Aged Retrospective Studies Contrast Media Female Magnetic Resonance Imaging Reproducibility of Results Aged
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Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.

AJNR. American journal of neuroradiology Mar 4, 2025
BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well-documented selection criterion for acute ischemic stroke treatment; however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with notable interobserver ...
Humans Deep Learning Algorithms Ischemic Stroke Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Observer Variation Computed Tomography Angiography
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Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.

The international journal of cardiovascular imaging Feb 28, 2025
Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting treatment decisions due to the extremely low coronary artery event risk associated with the former. ...
Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Vessels Coronary Artery Disease Humans Reproducibility of Results Plaque, Atherosclerotic Observer Variation Artificial Intelligence Aged Vascular Calcification Retrospective Studies Male Asymptomatic Diseases Middle Aged Computed Tomography Angiography Female Severity of Illness Index
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