Bernardo Canedo Bizzo

Automated artificial intelligence–enabled measurement of cardiac structures on point-of-care ultrasonography: a prospective multicenter validation study

Sarah F. Mercaldo, Bernardo Canedo Bizzo, Tsion Sadore, Madeleine A Halle et autres

Introduction Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside, especially in resource-limited settings. This information may include 2D cardiac quantitative data, although measuring the data manually can be time-consuming and subject to user experience. Artificial intelligence (AI) …

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AI Monitoring AI with LLMs: The American College of Radiology's Imaging AI Registry

Kendall Schmidt, Laura Brink, Neil Bhatia, Jason Yao et autres

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Performance-based frailty tools capture distinct risk phenotypes in kidney transplant candidates: A comparative analysis of the FRAIL scale and short physical performance battery

Rucháma Verhoeff, Frank Hullekes, Emily Jaffe, Jennie Cataldo et autres

Frailty predicts adverse outcomes in kidney transplant candidates, but it remains unclear whether different frailty tools identify the same or distinct at-risk patients. In this retrospective cohort study, 953 kidney transplant candidates listed between 2018 and 2022 underwent frailty assessment using both …

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ACR’s Assess-AI: A Registry for Real-World Performance Monitoring of Clinical Imaging Artificial Intelligence

Laura P. Coombs, Laura Brink, Woojin Kim, Christoph Wald et autres

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Evaluation of an artificial intelligence model for the identification of obstructive hydrocephalus on computed tomography of the head

Ankita Ghatak, Isabella Newbury‐Chaet, Sarah Mercaldo, John K. Chin et autres

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Assessing Change in Stone Burden on Baseline and Follow-Up CT: Radiologist and Radiomics Evaluations

Parisa Kaviani, Matthias Frank Froelich, Bernardo Canedo Bizzo, Andrew N. Primak et autres

This retrospective diagnostic accuracy study compared radiologist-based qualitative assessments and radiomics-based analyses with an automated artificial intelligence (AI)–based volumetric approach for evaluating changes in kidney stone burden on follow-up CT examinations. With institutional review board approval, 157 patients (mean age, 61 ± …

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Large language models as cost-conscious decision aids in emergency medicine: protocol support for imaging in lower back pain

Siona Prasad, Arya S Rao, Mario V. Russo, Soham Ghoshal et autres

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Evaluation of an artificial intelligence model for opportunistic Agatston scoring on non-gated chest computed tomography

Suzannah E. McKinney, Sarah Mercaldo, John K. Chin, Ankita Ghatak et autres

The Agatston score is a measure of cardiovascular disease traditionally calculated on cardiac gated computed tomography (CT) of the chest. Cardiac gated CT is resource-intensive, can be hard to access, and involves extra radiation exposure. Artificial intelligence (AI) can be used to …

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Artificial Intelligence in Adrenal Imaging

Daniel I. Glazer, Bernardo Canedo Bizzo, David Fuentes, William W. Mayo-Smith

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Cardiac Measurement Calculation on Point-of-Care Ultrasonography with Artificial Intelligence

Sarah Mercaldo, Bernardo Canedo Bizzo, Tsion Sadore, Madeleine A Halle et autres

Abstract Introduction Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited settings. This information may include 2D cardiac quantitative data, although measuring the data manually can be time-consuming and subject to user experience. Artificial …

us (code pays fourni par la source)

Regulating Generative AI in Radiology Practice: A Trilaminar Approach to Balancing Risk with Innovation

Vrushab Gowda, Bernardo Canedo Bizzo, Keith J. Dreyer

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Detection of Hypertrophic Cardiomyopathy on Electrocardiogram Using Artificial Intelligence

James Michael Hillis, Bernardo Canedo Bizzo, Sarah F. Mercaldo, Ankita Ghatak et autres

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is associated with significant morbidity and mortality, including sudden cardiac death in the young. Its prevalence is estimated to be 1 in 500, although many people are undiagnosed. The ability to screen electrocardiograms for its presence could improve …

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