11 results match your criteria: "Imaging and Biomedical Sciences Cedars-Sinai Medical Center[Affiliation]"
AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis.
medRxiv
September 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Article Synopsis
- - The study focuses on developing an automated system to quantify [18F]-fluorodeoxyglucose (FDG) PET activity in diagnosing cardiac sarcoidosis using deep learning for segmenting cardiac chambers from CT scans.
- - The analysis included 69 patients, revealing that the cardiometabolic activity (CMA) showed the best predictive accuracy for cardiac sarcoidosis, followed by volume of inflammation (VOI) and target to background ratio (TBR).
- - The findings indicate that this automated method provides rapid, objective measurements of cardiac inflammation, showing high sensitivity and specificity for diagnosing cardiac sarcoidosis.
AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR.
JACC Adv
October 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA.
Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR).
Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR.
Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR.
Evaluating AI Proficiency in Nuclear Cardiology: Large Language Models take on the Board Preparation Exam.
medRxiv
July 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology.
Objectives: This study assesses four LLMs - GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.) - in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination.
Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.
medRxiv
April 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Article Synopsis
- Low-dose computed tomography (CT) scans, used in hybrid myocardial perfusion imaging, provide valuable anatomical and pathological insights beyond just attenuation correction, which may be enhanced through AI-driven frameworks.
- This study analyzed data from over 10,000 patients, segmenting various structures and utilizing deep learning to assess coronary artery health, leading to improved all-cause mortality predictions.
- The comprehensive model integrating data from CT attenuation correction, myocardial perfusion imaging, and clinical factors outperformed other AI models in predicting mortality risk, particularly among patients with normal perfusion.
Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.
Nat Commun
March 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Article Synopsis
- Chest CT scans are widely used in the U.S., with 15 million performed yearly, primarily for diagnosing various conditions, including cardiac risks.
- A new automated AI system can quickly and accurately assess coronary calcium and various heart chamber volumes from these scans, processing data in about 18 seconds and only missing 0.1% of cases.
- The AI-generated measurements of coronary calcium and heart volumes are effective in predicting overall and cardiovascular mortality, offering a better risk assessment method than traditional evaluations by radiologists.
Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.
Eur Heart J Cardiovasc Imaging
June 2024
Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Floor 4, Los Angeles 90048 CA, USA.
Article Synopsis
- The study examines the effectiveness of using automated deep learning techniques to analyze vessel-specific coronary artery calcification (CAC) for better prognostic assessments of heart health in patients.
- A large dataset comprised of electrocardiogram-gated and attenuation-corrected CT scans was used to evaluate the accuracy of this analysis, showing strong agreement with expert assessments across various artery segments.
- The findings indicate that significant CAC levels, especially in the left main/left anterior descending artery, correlate with a higher risk of major adverse cardiovascular events, suggesting that vessel-specific assessment can enhance risk stratification in cardiovascular health.
AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.
NPJ Digit Med
February 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Article Synopsis
- Epicardial adipose tissue (EAT) volume and attenuation can indicate cardiovascular risk, but measuring them manually is time-consuming; the study explored using deep learning to automate this process using CT scans.
- Researchers trained a deep learning model on data from 500 patients to accurately identify EAT, achieving results in under 2 seconds compared to 15 minutes for manual analysis.
- After analyzing 8781 patients, results showed that higher EAT measurements were linked to an increased risk of death or myocardial infarction over a median follow-up of 2.7 years, indicating that automated EAT assessments could enhance cardiovascular risk prediction.
Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI.
medRxiv
January 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Non-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI.
Methods: We enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI.
Background: The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD.
View Article and Find Full Text PDFDiagnostic accuracy of bone scintigraphy imaging for transthyretin cardiac amyloidosis: systematic review and meta-analysis.
J Nucl Cardiol
December 2023
Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada.
Article Synopsis
- A systematic review and meta-analysis were conducted to assess the accuracy of different bone scintigraphy imaging techniques for diagnosing transthyretin cardiac amyloidosis (ATTR-CM), revealing shifts in accuracy over time.* -
- Out of 428 studies, 23 were selected for analysis, encompassing 3,954 patients, with 39.6% diagnosed with ATTR-CM; visual planar grading and quantitative analysis showed higher accuracy than the heart-to-contralateral (HCL) ratio.* -
- The findings indicate that bone scintigraphy is highly effective for diagnosing ATTR-CM, with variability in study results largely due to differing disease prevalence, and highlight the need for precise methods in low-risk populations.*
Improved myocardial blood flow estimation with residual activity correction and motion correction in F-flurpiridaz PET myocardial perfusion imaging.
Eur J Nucl Med Mol Imaging
May 2022
Department of Medicine (Division of Artificial Intelligence)- Imaging- and Biomedical Sciences- Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
Article Synopsis
- The study evaluates the effectiveness of using F-flurpiridaz PET imaging, along with motion correction (MC) and residual activity correction (RAC), in diagnosing coronary artery disease (CAD) in 231 patients.
- It finds that measuring myocardial blood flow (MBF) and myocardial flow reserve (MFR) in smaller segments of the heart (minimal segment) yields better diagnostic accuracy than broader measurements (global or minimal vessel).
- The results suggest that incorporating MC and RAC significantly enhances the sensitivity of MBF and MFR estimates for detecting obstructive CAD.