AIMC Topic: Observer Variation
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Comparative performance of PD-L1 scoring by pathologists and AI algorithms.
Histopathology
Feb 17, 2025
AIM: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD-L1 expression in non-small cell lung carcinoma (NSCLC). Immune-checkpoint inhibitors have revolutionized NSCLC treatm...
Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular Testing.
The American journal of pathology
Jan 30, 2025
Tumor cellularity (TC) in lung adenocarcinoma slides submitted for molecular testing is important in identifying actionable mutations, but lack of best practice guidelines results in high interobserver variability in TC assessments. An artificial int...
Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.
La Radiologia medica
Jan 26, 2025
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.
Intra- and interobserver agreement of proposed objective transvaginal ultrasound image-quality scoring system for use in artificial intelligence algorithm development.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Jan 24, 2025
OBJECTIVES: The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high-quality data. This study aimed to test the intra- and interobserver agreement of a proposed imag...
Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction.
BMC medical imaging
Jan 13, 2025
BACKGROUND: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. L...
Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.
Open heart
Jan 11, 2025
BACKGROUND: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis im...
Artificial Intelligence
Algorithms
Retrospective Studies
Female
Reproducibility of Results
Humans
Coronary Vessels
Computed Tomography Angiography
Coronary Stenosis
Predictive Value of Tests
Aged
Coronary Angiography
Male
Radiographic Image Interpretation, Computer-Assisted
Middle Aged
Coronary Artery Disease
Severity of Illness Index
Observer Variation
Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.
European radiology
Jan 10, 2025
OBJECTIVES: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagno...
Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.
The international journal of cardiovascular imaging
Dec 16, 2024
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients...
Computed Tomography Angiography
Cardiac-Gated Imaging Techniques
Severity of Illness Index
Coronary Angiography
Reproducibility of Results
Observer Variation
Time Factors
Radiologists
Humans
Aged
Coronary Vessels
Radiographic Image Interpretation, Computer-Assisted
Artificial Intelligence
Multidetector Computed Tomography
Male
Coronary Artery Disease
Retrospective Studies
Vascular Calcification
Middle Aged
Workflow
Female
Electrocardiography
Predictive Value of Tests
Evaluation of the clinical utility of lateral cephalometry reconstructed from computed tomography extracted by artificial intelligence.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Dec 9, 2024
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...
Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.
Journal of applied clinical medical physics
Dec 1, 2024
PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geome...
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