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Malik RF, Sun KJ, Azadi JR, Lau BD, Whelton S, Arbab-Zadeh A, Wilson RF, Johnson PT. Opportunistic Screening for Coronary Artery Disease: An Untapped Population Health Resource. J Am Coll Radiol 2024; 21:880-889. [PMID: 38382860 DOI: 10.1016/j.jacr.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/31/2024] [Accepted: 02/13/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Coronary artery disease is the leading cause of death in the United States. At-risk asymptomatic adults are eligible for screening with electrocardiogram-gated coronary artery calcium (CAC) CT, which aids in risk stratification and management decision-making. Incidental CAC (iCAC) is easily quantified on chest CT in patients imaged for noncardiac indications; however, radiologists do not routinely report the finding. OBJECTIVE To determine the clinical significance of CAC identified incidentally on routine chest CT performed for noncardiac indications. DESIGN An informationist developed search strategies in MEDLINE, Embase, and SCOPUS, and two reviewers independently screened results at both the abstract and full text levels. Data extracted from eligible articles included age, rate of iCAC identification, radiologist reporting frequency, impact on downstream medical management, and association of iCAC with patient outcomes. RESULTS From 359 unique citations, 83 research publications met inclusion criteria. The percentage of patients with iCAC ranged from 9% to 100%. Thirty-one investigations measured association(s) between iCAC and cardiovascular morbidity and mortality, and 29 identified significant correlations, including nonfatal myocardial infarction, fatal myocardial infarction, major adverse cardiovascular event, cardiovascular death, and all-cause death. iCAC was present in 20% to 100% of the patients in these cohorts, but when present, iCAC was reported by radiologists in only 31% to 44% of cases. Between 18% and 77% of patients with iCAC were not on preventive medications in studies that reported these data. Seven studies measured the effect of reporting on guideline directed medical therapy, and 5 (71%) reported an increase in medication prescriptions after diagnosis of iCAC, with one confirming reductions in low-density lipoprotein levels. Twelve investigations reported good concordance between CAC grade on noncardiac CT and Agatston score on electrocardiogram-gated cardiac CT, and 10 demonstrated that artificial intelligence tools can reliably calculate an Agatston score on noncardiac CT. CONCLUSION A body of evidence demonstrates that patients with iCAC on routine chest CT are at risk for cardiovascular disease events and death, but they are often undiagnosed. Uniform reporting of iCAC in the chest CT impression represents an opportunity for radiology to contribute to early identification of high-risk individuals and potentially reduce morbidity and mortality. AI tools have been validated to calculate Agatston score on routine chest CT and hold the best potential for facilitating broad adoption.
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Affiliation(s)
- Rubab F Malik
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kristie J Sun
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Javad R Azadi
- Assistant Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Brandyn D Lau
- Assistant Professor of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Seamus Whelton
- Associate Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Armin Arbab-Zadeh
- Director of Cardiac CT, Professor of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Renee F Wilson
- Evidence Based Practice Center, Johns Hopkins University School of Public Health, Baltimore, Maryland
| | - Pamela T Johnson
- Vice President of Care Transformation, Vice Chair of Quality and Safety in Radiology, and Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Parsa S, Somani S, Dudum R, Jain SS, Rodriguez F. Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time? Curr Atheroscler Rep 2024:10.1007/s11883-024-01210-w. [PMID: 38780665 DOI: 10.1007/s11883-024-01210-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE OF REVIEW This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. RECENT FINDINGS AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
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Affiliation(s)
- Shyon Parsa
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Ramzi Dudum
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sneha S Jain
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Center for Digital Health, Stanford University, Stanford, California, USA.
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3
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Ichikawa K, Wang R, McClelland RL, Manubolu VS, Susarla S, Lee D, Pourafkari L, Fazlalizadeh H, Bitar JA, Robin R, Kinninger A, Roy S, Post WS, Budoff M. Thoracic versus coronary calcification for atherosclerotic cardiovascular disease events prediction. Heart 2024:heartjnl-2023-323838. [PMID: 38627022 DOI: 10.1136/heartjnl-2023-323838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
This study compared the prognostic value of quantified thoracic artery calcium (TAC) including aortic arch on chest CT and coronary artery calcium (CAC) score on ECG-gated cardiac CT. METHODS A total of 2412 participants who underwent both chest CT and ECG-gated cardiac CT at the same period were included in the Multi-Ethnic Study of Atherosclerosis Exam 5. All participants were monitored for incident atherosclerotic cardiovascular disease (ASCVD) events. TAC is defined as calcification in the ascending aorta, aortic arch and descending aorta on chest CT. The quantification of TAC was measured using the Agatston method. Time-dependent receiver-operating characteristic (ROC) curves were used to compare the prognostic value of TAC and CAC scores. RESULTS Participants were 69±9 years of age and 47% were male. The Spearman correlation between TAC and CAC scores was 0.46 (p<0.001). During the median follow-up period of 8.8 years, 234 participants (9.7%) experienced ASCVD events. In multivariable Cox regression analysis, TAC score was independently associated with increased risk of ASCVD events (HR 1.31, 95% CI 1.09 to 1.58) as well as CAC score (HR 1.82, 95% CI 1.53 to 2.17). However, the area under the time-dependent ROC curve for CAC score was greater than that for TAC score in all participants (0.698 and 0.641, p=0.031). This was particularly pronounced in participants with borderline/intermediate and high 10-year ASCVD risk scores. CONCLUSION Our study demonstrated a significant association between TAC and CAC scores but a superior prognostic value of CAC score for ASCVD events. These findings suggest TAC on chest CT provides supplementary data to estimate ASCVD risk but does not replace CAC on ECG-gated cardiac CT.
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Affiliation(s)
| | - Rui Wang
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Robyn L McClelland
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | | | | | - Duo Lee
- The Lundquist Institute, Torrance, California, USA
| | | | | | | | - Rick Robin
- The Lundquist Institute, Torrance, California, USA
| | | | - Sion Roy
- The Lundquist Institute, Torrance, California, USA
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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Parsa S, Saleh A, Raygor V, Hoeting N, Rao A, Navar AM, Rohatgi A, Kay F, Abbara S, Khera A, Joshi PH. Measurement and Application of Incidentally Detected Coronary Calcium: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:1557-1567. [PMID: 38631775 DOI: 10.1016/j.jacc.2024.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 04/19/2024]
Abstract
Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.
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Affiliation(s)
- Shyon Parsa
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA; Department of Internal Medicine, Stanford University Hospital, Stanford, California, USA
| | - Adam Saleh
- Texas A&M University, Engineering Medicine, Houston, Texas, USA
| | - Viraj Raygor
- Sutter Health, Cardiovascular Health, Palo Alto, California, USA
| | - Natalie Hoeting
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Anjali Rao
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Ann Marie Navar
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Anand Rohatgi
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Fernando Kay
- Department of Radiology, Division of Cardiothoracic Imaging, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Suhny Abbara
- Department of Radiology, Division of Cardiothoracic Imaging, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Amit Khera
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA
| | - Parag H Joshi
- Department of Internal Medicine, Division of Cardiology, the UT Southwestern Medical Center, Dallas, Texas, USA.
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5
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Hu J, Hao G, Xu J, Wang X, Chen M. Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus. Heliyon 2024; 10:e27937. [PMID: 38496873 PMCID: PMC10944251 DOI: 10.1016/j.heliyon.2024.e27937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/03/2024] [Accepted: 03/08/2024] [Indexed: 03/19/2024] Open
Abstract
Background Coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) patients often presents diffuse lesions, with extensive calcification, and it is time-consuming to measure coronary artery calcium score (CACS). Objectives To explore the predictive ability of deep learning (DL)-based CACS for obstructive CAD and hemodynamically significant CAD in T2DM. Methods 469 T2DM patients suspected of CAD who accepted CACS scan and coronary CT angiography between January 2013 and December 2020 were enrolled. Obstructive CAD was defined as diameter stenosis ≥50%. Hemodynamically significant CAD was defined as CT-derived fractional flow reserve ≤0.8. CACS was calculated with a fully automated method based on DL algorithm. Logistic regression was applied to determine the independent predictors. The predictive performance was evaluated with area under receiver operating characteristic curve (AUC). Results DL-CACS (adjusted odds ratio (OR): 1.005; 95% CI: 1.003-1.006; P < 0.001) was significantly associated with obstructive CAD. DL-CACS (adjusted OR:1.003; 95% CI: 1.002-1.004; P < 0.001) was also an independent predictor for hemodynamically significant CAD. The AUCs, sensitivities, specificities, positive predictive values and negative predictive values of DL-CACS for obstructive CAD and hemodynamically significant CAD were 0.753 (95% CI: 0.712-0.792), 75.9%, 66.5%, 74.8%, 67.8% and 0.769 (95% CI: 0.728-0.806), 80.7%, 62.1%, 59.6% and 82.3% respectively. It took 1.17 min to perform automated measurement of DL-CACS in total, which was significantly less than manual measurement of 1.73 min (P < 0.001). Conclusions DL-CACS, with less time-consuming, can accurately and effectively predict obstructive CAD and hemodynamically significant CAD in T2DM.
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Affiliation(s)
- Jingcheng Hu
- Department of Endocrinology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Guangyu Hao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jialiang Xu
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Meng Chen
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Lin Y, Lin G, Peng MT, Kuo CT, Wan YL, Cherng WJ. The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels. J Thorac Imaging 2024; 39:111-118. [PMID: 37982516 DOI: 10.1097/rti.0000000000000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
PURPOSE To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. PATIENTS AND METHODS Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ). RESULTS The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively). CONCLUSIONS There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.
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Affiliation(s)
- Yenpo Lin
- Department of Medical Imaging and Intervention
| | - Gigin Lin
- Department of Medical Imaging and Intervention
| | | | - Chi-Tai Kuo
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | | | - Wen-Jin Cherng
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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Vrudhula A, Kwan AC, Ouyang D, Cheng S. Machine Learning and Bias in Medical Imaging: Opportunities and Challenges. Circ Cardiovasc Imaging 2024; 17:e015495. [PMID: 38377237 PMCID: PMC10883605 DOI: 10.1161/circimaging.123.015495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.
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Affiliation(s)
- Amey Vrudhula
- Icahn School of Medicine at Mount Sinai, New York
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
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10
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Wang Z, Zhu D, Hu G, Shi X. Enhanced CT imaging artificial neural network coronary artery calcification score assisted diagnosis. Technol Health Care 2024:THC231273. [PMID: 38427514 DOI: 10.3233/thc-231273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
BACKGROUND The study of coronary artery calcification (CAC) may assist in identifying additional coronary artery problem protective factors. On the contrary side, due to the wide variety of CAC as individuals, CAC research is difficult. Due to this, evaluating data for investigation is becoming complicated. OBJECTIVE To use a multi-layer perceptron, we investigated the accuracy and reliability of synthetic CAC coursework or hazard classification in pre or alors chest computerized tomography (CT) of arrangements resolutions in this analysis. method Photographs of the chest from similar individuals as well as calcium-just and non-gated pictures were incorporated. This cut thickness ordered CT pictures (bunch A: 1 mm; bunch B: 3 mm). The CAC rating was determined utilizing calcification score picture information, and became standard for tests. While the control treatment's machine learning program was created using 170 computed tomography pictures and evaluated using 144 scans, group A's machine learning algorithm was created using 150 chest CT diagnostic tests. RESULTS 334 external related pictures (100 μm: 117; 0.5 mm x: 117) of 117 individuals and 612 inside design organizing (1 mm: 294; mm3: 314) of 406 patients were surveyed. Pack B had 0.94, however, tests An and b had 0.90 (95% CI: 0.85-0.93) ICCs between significant learning and gold expenses (0.92-0.96). Dull Altman plots agreed well. A machine teaching approach successfully identified 71% of cases in category A is 81% of patients in section B again for cardiac risk class. CONCLUSION Regression risk evaluation algorithms could assist in categorizing cardiorespiratory individuals into distinct risk groups and conveniently personalize the treatments to the patient's circumstances. The models would be based on information gathered through CAC. On both 1 and 3-mm scanners, the automatic determination of a CAC value and cardiovascular events categorization that used a depth teaching approach was reliable and precise. The layer thickness of 0.5 mm on chest CT was slightly less accurate in CAC detection and risk evaluation.
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11
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Abdelrahman K, Shiyovich A, Huck DM, Berman AN, Weber B, Gupta S, Cardoso R, Blankstein R. Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification. Diagnostics (Basel) 2024; 14:125. [PMID: 38248002 PMCID: PMC10814920 DOI: 10.3390/diagnostics14020125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.
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Affiliation(s)
| | | | | | | | | | | | | | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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12
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Tang L, Wang X, Yang J, Wang Y, Qu M, Li H. DLFFNet: A new dynamical local feature fusion network for automatic aortic valve calcification recognition using echocardiography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107882. [PMID: 37972459 DOI: 10.1016/j.cmpb.2023.107882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/26/2023] [Accepted: 10/22/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND OBJECTIVE Aortic valve calcification (AVC) is a strong predictor of adverse cardiovascular events and is correlated with the degree of coronary artery stenosis. Generally, AVC is identified by echocardiography using visual "eyeballing", which results in huge differences between observers and requires a long time to learn. Therefore, accurately identifying AVC from echocardiographic images is a challenging task due to the interference of various factors. METHOD In this paper, we built a dynamical local feature fusion net capable of processing echocardiography to recognize AVC automatically. We proposed high-echo area which were segmented by a U-Net. Meanwhile, we fine-tuned the segmentation results by adding brightness in the mask tuning module in order to dynamically adjust the selection of local features. To better fuse multi-level and multi-scale information, we designed a pyramid-based two-branch feature fusion module in classification, which enables the network to better integrate global and local semantic representations. In addition, for the echocardiographic data collected by different devices and doctors, inconsistent aortic valve position with a small occupied area, a unified preprocessing algorithm was designed. RESULTS To highlight the effectiveness of the proposed approach, we compared several state-of-the-art methods on the same ultrasound dataset. The 231 patients with short-axis views of the aortic valve images were collected and labeled (masked) by experienced ultrasound doctors from The First Hospital of China Medical University. The accuracy, precision, sensitivity, specificity, and F1 score, micro-AUC, and macro-AUC of the model for the test dataset were, 82.40%, 82.50%, 82.50%, 91.23%, 82.47%, 92.39%, and 92.25%, respectively. CONCLUSIONS The results showed the possibility of using echocardiography to examine AVC automatically and verified by visualization methods that the Region of Interest of the model is consistent with the observed region of the experts.
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Affiliation(s)
- Lingzhi Tang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China; Computer Science and Engineering, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, China
| | - Xueqi Wang
- Department of Cardiovascular Ultrasound The First Hospital of China Medical University, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China; Computer Science and Engineering, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, China.
| | - Yonghuai Wang
- Department of Cardiovascular Ultrasound The First Hospital of China Medical University, China
| | - Mingjun Qu
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China; Computer Science and Engineering, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, China
| | - HongHe Li
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China; Computer Science and Engineering, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, China
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13
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van Assen M, Tariq A, Razavi AC, Yang C, Banerjee I, De Cecco CN. Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care. Circ Cardiovasc Imaging 2023; 16:e014533. [PMID: 38073535 PMCID: PMC10754220 DOI: 10.1161/circimaging.122.014533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.
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Affiliation(s)
- Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Amara Tariq
- Machine Intelligence in Medicine and Imaging (MI-2) Lab, Mayo Clinic, AZ, USA
| | - Alexander C. Razavi
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
- Emory Clinical Cardiovascular Research Institute, Emory University, Atlanta, GA, USA
| | - Carl Yang
- Computer Science, Emory University, Atlanta, GA, USA
| | - Imon Banerjee
- Machine Intelligence in Medicine and Imaging (MI-2) Lab, Mayo Clinic, AZ, USA
| | - Carlo N. De Cecco
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
- Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA USA
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14
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Grant JK, Orringer CE. Coronary and Extra-coronary Subclinical Atherosclerosis to Guide Lipid-Lowering Therapy. Curr Atheroscler Rep 2023; 25:911-920. [PMID: 37971683 DOI: 10.1007/s11883-023-01161-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE OF REVIEW To discuss and review the technical considerations, fundamentals, and guideline-based indications for coronary artery calcium scoring, and the use of other non-invasive imaging modalities, such as extra-coronary calcification in cardiovascular risk prediction. RECENT FINDINGS The most robust evidence for the use of CAC scoring is in select individuals, 40-75 years of age, at borderline to intermediate 10-year ASCVD risk. Recent US recommendations support the use of CAC scoring in varying clinical scenarios. First, in adults with very high CAC scores (CAC ≥ 1000), the use of high-intensity statin therapy and, if necessary, guideline-based add-on LDL-C lowering therapies (ezetimibe, PCSK9-inhibitors) to achieve a ≥ 50% reduction in LDL-C and optimally an LDL-C < 70 mg/dL is recommended. In patients with a CAC score ≥ 100 at low risk of bleeding, the benefits of aspirin use may outweigh the risk of bleeding. Other applications of CAC scoring include risk estimation on non-contrast CT scans of the chest, risk prediction in younger patients (< 40 years of age), its value as a gatekeeper for the decision to perform nuclear stress testing, and to aid in risk stratification in patients presenting with low-risk chest pain. There is a correlation between extra-coronary calcification (e.g., breast arterial calcification, aortic calcification, and aortic valve calcification) and incident ASCVD events. However, its role in informing lipid management remains unclear. Identification of coronary calcium in selected patients is the single best non-invasive imaging modality to identify future ASCVD risk and inform lipid-lowering therapy decision-making.
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Affiliation(s)
- Jelani K Grant
- Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Baltimore, MD, USA
| | - Carl E Orringer
- NCH Rooney Heart Institute, 399 9th Street North, Suite 300, Naples, FL, 34102, USA.
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15
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Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:586-596. [PMID: 38223683 PMCID: PMC10781930 DOI: 10.1007/s43657-023-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.
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Affiliation(s)
- Junhao Li
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Guifen Yang
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
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16
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Adams SJ, Mikhael P, Wohlwend J, Barzilay R, Sequist LV, Fintelmann FJ. Artificial Intelligence and Machine Learning in Lung Cancer Screening. Thorac Surg Clin 2023; 33:401-409. [PMID: 37806742 DOI: 10.1016/j.thorsurg.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.
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Affiliation(s)
- Scott J Adams
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Peter Mikhael
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeremy Wohlwend
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Barzilay
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lecia V Sequist
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, Boston, MA, USA.
| | - Florian J Fintelmann
- Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
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17
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Tatsugami F, Nakaura T, Yanagawa M, Fujita S, Kamagata K, Ito R, Kawamura M, Fushimi Y, Ueda D, Matsui Y, Yamada A, Fujima N, Fujioka T, Nozaki T, Tsuboyama T, Hirata K, Naganawa S. Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagn Interv Imaging 2023; 104:521-528. [PMID: 37407346 DOI: 10.1016/j.diii.2023.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
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Affiliation(s)
- Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Shohei Fujita
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital N15, W5, Kita-Ku, Sapporo 060-8638, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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18
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Takahashi D, Fujimoto S, Nozaki YO, Kudo A, Kawaguchi YO, Takamura K, Hiki M, Sato E, Tomizawa N, Daida H, Minamino T. Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method. EUROPEAN HEART JOURNAL OPEN 2023; 3:oead113. [PMID: 38035036 PMCID: PMC10683040 DOI: 10.1093/ehjopen/oead113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/14/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Aims To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images. Methods and results Retrospectively, 560 gated CCT images (including 60 synthetic images) performed at our institution were used to train AI-model, which can automatically divide heart region into five areas belonging to left main (LM), left anterior descending (LAD), circumflex (LCX), right coronary artery (RCA), and another. Total and vessel-specific CAC score (CACS) in each scan were manually evaluated. AI-model was trained with novel Heart-labelling method via DL according to the manual-derived results. Then, another 409 gated CCT images obtained in our institution were used for model validation. The performance of present AI-model was tested using another external cohort of 400 gated CCT images of Stanford Center for Artificial Intelligence of Medical Imaging by comparing with the ground truth. The overall accuracy of the AI-model for total CACS classification was excellent with Cohen's kappa of k = 0.89 and 0.95 (validation and test, respectively), which surpasses previous research of k = 0.89. Bland-Altman analysis showed little difference in individual total and vessel-specific CACS between AI-derived CACS and ground truth in test cohort (mean difference [95% confidence interval] were 1.5 [-42.6, 45.6], -1.5 [-100.5, 97.5], 6.6 [-60.2, 73.5], 0.96 [-59.2, 61.1], and 7.6 [-134.1, 149.2] for LM, LAD, LCX, RCA, and total CACS, respectively). Conclusion Present Heart-labelling method provides a further improvement in fully automated, total, and vessel-specific CAC quantification on gated CCT.
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Affiliation(s)
- Daigo Takahashi
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Shinichiro Fujimoto
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Yui O Nozaki
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Ayako Kudo
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Yuko O Kawaguchi
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Kazuhisa Takamura
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Makoto Hiki
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Eisuke Sato
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Nobuo Tomizawa
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Hiroyuki Daida
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
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19
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Muscogiuri E, van Assen M, Tessarin G, Razavi A, Schwemmer C, Schoebinger M, Wels M, Rapaka S, Fung GSK, Stillman AE, De Cecco CN. Validation of a convolutional neural network algorithm for calcium score quantification using a multivendor dataset. J Cardiovasc Comput Tomogr 2023; 17:473-475. [PMID: 37945453 PMCID: PMC10908358 DOI: 10.1016/j.jcct.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/07/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Emanuele Muscogiuri
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA; Thoracic Imaging Division, Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Marly van Assen
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA
| | - Giovanni Tessarin
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA; Department of Medicine-DIMED, Institute of Radiology, University of Padova, Padua, Italy; Department of Radiology, Ca' Foncello General Hospital, Treviso, Italy
| | - Alexander Razavi
- Department of Cardiology, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA
| | - Chris Schwemmer
- Computed Tomography, Siemens Healthineers, Forchheim, Germany
| | - Max Schoebinger
- Computed Tomography, Siemens Healthineers, Forchheim, Germany
| | - Michael Wels
- Computed Tomography, Siemens Healthineers, Forchheim, Germany
| | | | | | - Arthur E Stillman
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA
| | - Carlo N De Cecco
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA.
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20
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Choi DY, Hayes D, Maidman SD, Dhaduk N, Jacobs JE, Shmukler A, Berger JS, Cuff G, Rehe D, Lee M, Donnino R, Smilowitz NR. Existing Nongated CT Coronary Calcium Predicts Operative Risk in Patients Undergoing Noncardiac Surgeries (ENCORES). Circulation 2023; 148:1154-1164. [PMID: 37732454 PMCID: PMC10592001 DOI: 10.1161/circulationaha.123.064398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND Preoperative cardiovascular risk stratification before noncardiac surgery is a common clinical challenge. Coronary artery calcium scores from ECG-gated chest computed tomography (CT) imaging are associated with perioperative events. At the time of preoperative evaluation, many patients will not have had ECG-gated CT imaging, but will have had nongated chest CT studies performed for a variety of noncardiac indications. We evaluated relationships between coronary calcium severity estimated from previous nongated chest CT imaging and perioperative major clinical events (MCE) after noncardiac surgery. METHODS We retrospectively identified consecutive adults age ≥45 years who underwent in-hospital, major noncardiac surgery from 2016 to 2020 at a large academic health system composed of 4 acute care centers. All patients had nongated (contrast or noncontrast) chest CT imaging performed within 1 year before surgery. Coronary calcium in each vessel was retrospectively graded from absent to severe using a 0 to 3 scale (absent, mild, moderate, severe) by physicians blinded to clinical data. The estimated coronary calcium burden (ECCB) was computed as the sum of scores for each coronary artery (0 to 9 scale). A Revised Cardiac Risk Index was calculated for each patient. Perioperative MCE was defined as all-cause death or myocardial infarction within 30 days of surgery. RESULTS A total of 2554 patients (median age, 68 years; 49.7% women; median Revised Cardiac Risk Index, 1) were included. The median time interval from nongated chest CT imaging to noncardiac surgery was 15 days (interquartile range, 3-106 days). The median ECCB was 1 (interquartile range, 0-3). Perioperative MCE occurred in 136 (5.2%) patients. Higher ECCB values were associated with stepwise increases in perioperative MCE (0: 2.9%, 1-2: 3.7%, 3-5: 8.0%; 6-9: 12.6%, P<0.001). Addition of ECCB to a model with the Revised Cardiac Risk Index improved the C-statistic for MCE (from 0.675 to 0.712, P=0.018), with a net reclassification improvement of 0.428 (95% CI, 0.254-0.601, P<0.0001). An ECCB ≥3 was associated with 2-fold higher adjusted odds of MCE versus an ECCB <3 (adjusted odds ratio, 2.11 [95% CI, 1.42-3.12]). CONCLUSIONS Prevalence and severity of coronary calcium obtained from existing nongated chest CT imaging improve preoperative clinical risk stratification before noncardiac surgery.
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Affiliation(s)
- Daniel Y Choi
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
| | - Dena Hayes
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
| | - Samuel D Maidman
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
| | - Nehal Dhaduk
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
| | - Jill E Jacobs
- Department of Radiology (J.E.J., A.S., R.D.), New York University Grossman School of Medicine, New York, NY
| | - Anna Shmukler
- Department of Radiology (J.E.J., A.S., R.D.), New York University Grossman School of Medicine, New York, NY
| | - Jeffrey S Berger
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
- Department of Surgery (J.S.B.), New York University Grossman School of Medicine, New York, NY
| | - Germaine Cuff
- Department of Anesthesiology, Perioperative Care and Pain Medicine (G.C., D.R., M.L.), New York University Grossman School of Medicine, New York, NY
| | - David Rehe
- Department of Anesthesiology, Perioperative Care and Pain Medicine (G.C., D.R., M.L.), New York University Grossman School of Medicine, New York, NY
| | - Mitchell Lee
- Department of Anesthesiology, Perioperative Care and Pain Medicine (G.C., D.R., M.L.), New York University Grossman School of Medicine, New York, NY
| | - Robert Donnino
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
- Department of Radiology (J.E.J., A.S., R.D.), New York University Grossman School of Medicine, New York, NY
- Cardiology Division, Department of Medicine, Veterans Affairs New York Harbor Healthcare System, New York, NY (R.D., N.R.S.)
| | - Nathaniel R Smilowitz
- Leon H. Charney Division of Cardiology (D.Y.C., D.H., S.D.M., N.D., J.S.B., R.D., N.R.S.), New York University Grossman School of Medicine, New York, NY
- Cardiology Division, Department of Medicine, Veterans Affairs New York Harbor Healthcare System, New York, NY (R.D., N.R.S.)
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21
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Langlotz CP. The Future of AI and Informatics in Radiology: 10 Predictions. Radiology 2023; 309:e231114. [PMID: 37874234 PMCID: PMC10623186 DOI: 10.1148/radiol.231114] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 10/25/2023]
Affiliation(s)
- Curtis P. Langlotz
- From the Departments of Radiology, Medicine, and Biomedical Data
Science, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA
94305
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22
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Peng AW, Dudum R, Jain SS, Maron DJ, Patel BN, Khandwala N, Eng D, Chaudhari AS, Sandhu AT, Rodriguez F. Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes. J Am Coll Cardiol 2023; 82:1192-1202. [PMID: 37704309 PMCID: PMC11009374 DOI: 10.1016/j.jacc.2023.06.040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/30/2023] [Accepted: 06/20/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Coronary artery calcium (CAC) is a strong predictor of cardiovascular events across all racial and ethnic groups. CAC can be quantified on nonelectrocardiography (ECG)-gated computed tomography (CT) performed for other reasons, allowing for opportunistic screening for subclinical atherosclerosis. OBJECTIVES The authors investigated whether incidental CAC quantified on routine non-ECG-gated CTs using a deep-learning (DL) algorithm provided cardiovascular risk stratification beyond traditional risk prediction methods. METHODS Incidental CAC was quantified using a DL algorithm (DL-CAC) on non-ECG-gated chest CTs performed for routine care in all settings at a large academic medical center from 2014 to 2019. We measured the association between DL-CAC (0, 1-99, or ≥100) with all-cause death (primary outcome), and the secondary composite outcomes of death/myocardial infarction (MI)/stroke and death/MI/stroke/revascularization using Cox regression. We adjusted for age, sex, race, ethnicity, comorbidities, systolic blood pressure, lipid levels, smoking status, and antihypertensive use. Ten-year atherosclerotic cardiovascular disease risk was calculated using the pooled cohort equations. RESULTS Of 5,678 adults without ASCVD (51% women, 18% Asian, 13% Hispanic/Latinx), 52% had DL-CAC >0. Those with DL-CAC ≥100 had an average 10-year ASCVD risk of 24%; yet, only 26% were on statins. After adjustment, patients with DL-CAC ≥100 had increased risk of death (HR: 1.51; 95% CI: 1.28-1.79), death/MI/stroke (HR: 1.57; 95% CI: 1.33-1.84), and death/MI/stroke/revascularization (HR: 1.69; 95% CI: 1.45-1.98) compared with DL-CAC = 0. CONCLUSIONS Incidental CAC ≥100 was associated with an increased risk of all-cause death and adverse cardiovascular outcomes, beyond traditional risk factors. DL-CAC from routine non-ECG-gated CTs identifies patients at increased cardiovascular risk and holds promise as a tool for opportunistic screening to facilitate earlier intervention.
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Affiliation(s)
- Allison W Peng
- Department of Medicine, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA. https://twitter.com/AllisonWPeng
| | - Ramzi Dudum
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - Sneha S Jain
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - David J Maron
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | | | - David Eng
- Bunkerhill Health, Palo Alto, California, USA
| | - Akshay S Chaudhari
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Department of Radiology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Alexander T Sandhu
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Veteran's Affairs Palo Alto Healthcare System, Palo Alto, California, USA. https://twitter.com/ATSandhu
| | - Fatima Rodriguez
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA.
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Dossabhoy SS, Ho VT, Ross EG, Rodriguez F, Arya S. Artificial intelligence in clinical workflow processes in vascular surgery and beyond. Semin Vasc Surg 2023; 36:401-412. [PMID: 37863612 PMCID: PMC10956485 DOI: 10.1053/j.semvascsurg.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 10/22/2023]
Abstract
In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.
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Affiliation(s)
- Shernaz S Dossabhoy
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304
| | - Vy T Ho
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304
| | - Elsie G Ross
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, CA
| | - Shipra Arya
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304.
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24
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Canan A, Ghandour AAH, Saboo SS, Rajiah PS. Opportunistic screening at chest computed tomography: literature review of cardiovascular significance of incidental findings. Cardiovasc Diagn Ther 2023; 13:743-761. [PMID: 37675086 PMCID: PMC10478026 DOI: 10.21037/cdt-23-79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/14/2023] [Indexed: 09/08/2023]
Abstract
Background and Objective Several incidental cardiovascular findings are present in a routine chest computed tomography (CT) scan, many of which do not make it to the final radiology report. However, these findings have important clinical implications, particularly providing prognosis and risk-stratification for future cardiovascular events. The purpose of this article is to review the literature on these incidental cardiovascular findings in a routine chest CT and inform the radiologist on their clinical relevance. Methods A time unlimited review of PubMed and Web of Science was performed by using relevant keywords. Articles in English that involved adults were included. Key Content and Findings Coronary artery calcification (CAC) is the most common incidental cardiac finding detected in a routine chest CT and is a significant predictor of cardiovascular events. Noncoronary vascular calcifications in chest CT include aortic valve, mitral annulus, and thoracic aortic calcifications (TAC). Among these, aortic valve calcification (AVC) has the strongest association with coronary artery disease and cardiovascular events. Additional cardiac findings such as myocardial scar and left ventricular size and noncardiac findings such as thoracic fat, bone density, hepatic steatosis, and breast artery calcifications can also help in risk stratification and patient management. Conclusions The radiologist interpreting a routine chest CT should be cognizant of the incidental cardiovascular findings, which helps in the diagnosis and risk-stratification of cardiovascular disease. This will guide appropriate referral and management.
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Affiliation(s)
- Arzu Canan
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
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25
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Pyrros A, Borstelmann SM, Mantravadi R, Zaiman Z, Thomas K, Price B, Greenstein E, Siddiqui N, Willis M, Shulhan I, Hines-Shah J, Horowitz JM, Nikolaidis P, Lungren MP, Rodríguez-Fernández JM, Gichoya JW, Koyejo S, Flanders AE, Khandwala N, Gupta A, Garrett JW, Cohen JP, Layden BT, Pickhardt PJ, Galanter W. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nat Commun 2023; 14:4039. [PMID: 37419921 PMCID: PMC10328953 DOI: 10.1038/s41467-023-39631-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/19/2023] [Indexed: 07/09/2023] Open
Abstract
Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.
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Affiliation(s)
- Ayis Pyrros
- Duly Health and Care, Department of Radiology, Downers Grove, IL, USA.
- Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, USA.
| | | | | | - Zachary Zaiman
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Kaesha Thomas
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Brandon Price
- Department of Radiology, Florida State University, Tallahassee, FL, USA
| | - Eugene Greenstein
- Department of Cardiology, Duly Health and Care, Downers Grove, IL, USA
| | - Nasir Siddiqui
- Duly Health and Care, Department of Radiology, Downers Grove, IL, USA
| | - Melinda Willis
- Duly Health and Care, Department of Radiology, Downers Grove, IL, USA
| | | | - John Hines-Shah
- Duly Health and Care, Department of Radiology, Downers Grove, IL, USA
| | | | - Paul Nikolaidis
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Matthew P Lungren
- Department of Biomedical and Health Information Sciences, UCSF, San Francisco, CA, USA
- Center for Artificial Intelligence in Medicine, Stanford University, Stanford, CA, USA
- Microsoft, Microsoft Corporation, Redmond, USA
| | | | | | - Sanmi Koyejo
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Adam E Flanders
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | | | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - Joseph Paul Cohen
- Center for Artificial Intelligence in Medicine, Stanford University, Stanford, CA, USA
| | - Brian T Layden
- Department of Medicine, University of Illinois Chicago, Chicago, IL, USA
| | | | - William Galanter
- Department of Medicine, University of Illinois Chicago, Chicago, IL, USA
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Tong A, Magudia K. One Step Forward in Opportunistic Screening for Body Composition. Radiology 2023; 307:e231003. [PMID: 37191482 DOI: 10.1148/radiol.231003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Affiliation(s)
- Angela Tong
- From the NYU Grossman School of Medicine, 660 1st Ave, 3rd Floor, New York, NY 10016 (A.T.); and Duke University School of Medicine, Durham, NC (K.M.)
| | - Kirti Magudia
- From the NYU Grossman School of Medicine, 660 1st Ave, 3rd Floor, New York, NY 10016 (A.T.); and Duke University School of Medicine, Durham, NC (K.M.)
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27
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Rajpurkar P, Lungren MP. The Current and Future State of AI Interpretation of Medical Images. N Engl J Med 2023; 388:1981-1990. [PMID: 37224199 DOI: 10.1056/nejmra2301725] [Citation(s) in RCA: 72] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Pranav Rajpurkar
- From the Department of Biomedical Informatics, Harvard Medical School, Boston (P.R.); the Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, and the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco - both in California (M.P.L.); and Microsoft, Redmond, Washington (M.P.L.)
| | - Matthew P Lungren
- From the Department of Biomedical Informatics, Harvard Medical School, Boston (P.R.); the Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, and the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco - both in California (M.P.L.); and Microsoft, Redmond, Washington (M.P.L.)
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28
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Danilov A, Aronow WS. Artificial Intelligence in Cardiology: Applications and Obstacles. Curr Probl Cardiol 2023; 48:101750. [PMID: 37088174 DOI: 10.1016/j.cpcardiol.2023.101750] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023]
Abstract
Artificial intelligence (AI) technology is poised to alter the flow of daily life, and in particular, medicine, where it may eventually complement the physician's work in diagnosing and treating disease. Despite the recent frenzy and uptick in AI research over the past decade, the integration of AI into medical practice is in its early stages. Cardiology stands to benefit due to its many diagnostic modalities and diverse treatments. AI methods have been applied to various domains within cardiology: imaging, electrocardiography, wearable devices, risk prediction, and disease classification. While many AI-based approaches have been developed that perform equal to or better than the state-of-the-art, few prospective randomized studies have evaluated their use. Furthermore, obstacles at the intersection of medicine and AI remain unsolved, including model understanding, bias, model evaluation, relevance and reproducibility, and legal and ethical dilemmas. We summarize recent and current applications of AI in cardiology, followed by a discussion of the aforementioned complications.
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Affiliation(s)
| | - Wilbert S Aronow
- New York Medical College, School of Medicine, Valhalla, New York; Department of Cardiology, Westchester Medical Center, Valhalla, NY
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29
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Harmon DM, Sehrawat O, Maanja M, Wight J, Noseworthy PA. Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation. Arrhythm Electrophysiol Rev 2023; 12:e12. [PMID: 37427304 PMCID: PMC10326669 DOI: 10.15420/aer.2022.31] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/22/2022] [Indexed: 07/11/2023] Open
Abstract
AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.
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Affiliation(s)
- David M Harmon
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - John Wight
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
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30
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Sandhu AT, Rodriguez F, Ngo S, Patel BN, Mastrodicasa D, Eng D, Khandwala N, Balla S, Sousa D, Maron DJ. Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project). Circulation 2023; 147:703-714. [PMID: 36342823 PMCID: PMC10108579 DOI: 10.1161/circulationaha.122.062746] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 10/26/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation. METHODS NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. RESULTS Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (P<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; P=0.008). CONCLUSIONS Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events. REGISTRATION URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT04789278.
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Affiliation(s)
- Alexander T Sandhu
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA
- Center for Digital Health, Department of Medicine, Stanford University, Stanford, CA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA
- Center for Digital Health, Department of Medicine, Stanford University, Stanford, CA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA
| | - Summer Ngo
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ
| | - Domenico Mastrodicasa
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, US
| | - David Eng
- Department of Computer Science, Stanford University School of Medicine, Stanford, CA
- Bunkerhill Health, Palo Alto, CA, US
| | - Nishith Khandwala
- Department of Computer Science, Stanford University School of Medicine, Stanford, CA
- Bunkerhill Health, Palo Alto, CA, US
| | - Sujana Balla
- Department of Internal Medicine, University of California San Francisco-Fresno, Fresno, CA
| | | | - David J. Maron
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA
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31
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Joshi PH, Nasir K, Navar AM. When Opportunity Knocks: Capitalizing on Incidental Coronary Arterial Calcification. Circulation 2023; 147:715-717. [PMID: 36848409 DOI: 10.1161/circulationaha.122.063207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- Parag H Joshi
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas (P.H.J., A.M.N.)
| | - Khurram Nasir
- Houston Methodist DeBakey Heart and Vascular Center, TX (K.N.).,Center for Cardiovascular Computational and Precision Health (C3-PH), Houston Methodist, TX (K.N.)
| | - Ann Marie Navar
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas (P.H.J., A.M.N.)
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32
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Sartoretti T, Gennari AG, Sartoretti E, Skawran S, Maurer A, Buechel RR, Messerli M. Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging. J Nucl Cardiol 2023; 30:313-320. [PMID: 35301677 PMCID: PMC9984313 DOI: 10.1007/s12350-022-02940-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/12/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND RESULTS Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores. CONCLUSION A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.
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Affiliation(s)
- Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands
| | - Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Elisabeth Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Ronny R Buechel
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
- University of Zurich, Zurich, Switzerland.
- Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands.
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Zhong Z, Yang W, Zhu C, Wang Z. Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:131. [PMID: 36819510 PMCID: PMC9929846 DOI: 10.21037/atm-22-6333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Background and Objective Vascular calcification has important clinical significance due to its vital prognostic value for cardiovascular diseases, chronic kidney disease (CKD), diabetes, fracture, and other multisystem diseases. Radiology is the main diagnostic method of it, but facing great pressure such as the increasing workload and decreasing working accuracy rate. Therefore, radiology needs to find a way out to better realize the clinical value of vascular calcification. Artificial intelligence (AI) encompasses any algorithm imitating human intelligence. AI has shown great potential in image analysis, such as its high speed and accuracy, becoming the savior of the current situation. In order to promote more rational utilization, the role and progress of AI in this field were reviewed. Methods A search was conducted in PubMed and Web of Science. The key words included "artificial intelligence", "machine learning", "deep learning", and "vascular calcification". The qualitative analysis of literature was achieved through repeated deliberation after refining valuable content. The theme is the role and progress of AI in the diagnostic radiology of vascular calcification. Key Content and Findings Sixty-two articles were included. AI has been applied to the diagnostic radiology of 5 types of vascular calcification, including coronary artery calcification (CAC), thoracic aortic calcification (TAC), abdominal aortic calcification (AAC), carotid artery calcification, and breast artery calcification (BAC). Deep learning (DL), the latest technology in this field has been well applied and satisfactorily performed. Radiologists have been able to achieve efficient diagnosis of 5 types of vascular calcification through AI, with reliable accuracy. Conclusions Increasingly, advanced AI has achieved an accuracy comparable to that of human experts, with a faster speed. Moreover, the ability to reduce noise and artifacts enables more imaging equipment to obtain reliable quantification. AI has acquired the ability to cooperate with radiology departments in future work. However, the research in AAC and carotid artery calcification can be more in-depth, and more types of vascular calcification and more fields of radiology should be expanded to. The interpretation of results made by AI and the promotion of existing achievements to the development of other disciplines are also the focus in future.
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Affiliation(s)
- Zhiqi Zhong
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Wenjun Yang
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Chengcheng Zhu
- Digestive Endoscopy Center, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Zhongqun Wang
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
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Ihdayhid AR, Lan NSR, Williams M, Newby D, Flack J, Kwok S, Joyner J, Gera S, Dembo L, Adler B, Ko B, Chow BJW, Dwivedi G. Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography. Eur Radiol 2023; 33:321-329. [PMID: 35986771 PMCID: PMC9755106 DOI: 10.1007/s00330-022-09028-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/07/2022] [Accepted: 07/13/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVES Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC. METHODS Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers). RESULTS Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan. CONCLUSIONS This artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. KEY POINTS • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.
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Affiliation(s)
- Abdul Rahman Ihdayhid
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia.
- Harry Perkins Institute of Medical Research, Curtin University, Perth, Australia.
| | - Nick S R Lan
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
- Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia
| | - Michelle Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, UK
| | - David Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, UK
| | | | | | | | - Sahil Gera
- Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia
| | - Lawrence Dembo
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
- Envision Medical Imaging, Perth, Australia
| | | | - Brian Ko
- Monash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Melbourne, Australia
| | | | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia.
- Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia.
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Gupta K, Hirsch JR, Kalsi J, Patel V, Gad MM, Virani SS. Highlights of Cardiovascular Disease Prevention Studies Presented at the 2022 American Heart Association Scientific Sessions. Curr Atheroscler Rep 2023; 25:31-41. [PMID: 36602752 DOI: 10.1007/s11883-022-01079-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW Summarize selected late-breaking science on cardiovascular (CV) disease prevention presented at the 2022 scientific session of the American Heart Association (AHA). RECENT FINDINGS The PROMINENT trial compared pemafibrate to a placebo in patients with type 2 diabetes mellitus (DM) and mild-to-moderate hypertriglyceridemia and high-density lipoprotein cholesterol (HDL-C)<40 mg/dL who were already on guideline-directed statin therapy. The RESPECT-EPA trial compared purified eicosapentaenoic acid (EPA) and statin therapy to statin therapy alone for secondary prevention of atherosclerotic CV disease (ASCVD). SPORT compared the efficacy of low-dose statin therapy with a placebo and six commonly used dietary supplements on lipid and inflammatory markers. Data from long-term follow-up of the FOURIER-OLE study was presented to evaluate the efficacy of very low low-density lipoprotein cholesterol (LDL-C) levels with proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Patient-level meta-analyses evaluated the association of statin therapy with new-onset DM and worse glycemic control. PROMPT-LIPID evaluated if automated electronic alerts to physicians with guideline-based recommendations improved the management of hyperlipidemia in patients at very high risk. NOTIFY-1 trial evaluated if notifying physicians and patients about coronary artery calcium (CAC) scores in non-ECG gated computed tomography scans led to increased prescription of statin therapy for primary ASCVD prevention. The DCP trial compared hydrochlorothiazide and chlorthalidone for blood pressure control and CV outcomes in hypertension. The CRHCP study compared the effectiveness of a village doctor for hypertension management and CV outcomes in rural areas of China. The QUARTET USA trial compared the effectiveness and safety of 4 antihypertensive medications in ultra-low doses with angiotensin-receptor blocker monotherapy. The late-breaking science presented at the 2022 scientific session of the AHA paves the way for future pragmatic trials and provides meaningful information to guide management strategies in cardiovascular disease prevention.
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Affiliation(s)
- Kartik Gupta
- Department of Medicine, Henry Ford Hospital, Detroit, MI, USA
| | - Josh R Hirsch
- Section of Cardiology and Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jasmeet Kalsi
- Department of Medicine, Henry Ford Hospital, Detroit, MI, USA
| | - Vaidahi Patel
- Heart & Vascular Institute, Division of Cardiovascular Diseases, Henry Ford Hospital, Detroit, MI, USA
| | - Mohamed Medhat Gad
- Section of Cardiology and Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Salim S Virani
- Section of Cardiology and Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
- Health Policy, Quality & Informatics Program, Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
- Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
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36
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Javaid A, Zghyer F, Kim C, Spaulding EM, Isakadze N, Ding J, Kargillis D, Gao Y, Rahman F, Brown DE, Saria S, Martin SS, Kramer CM, Blumenthal RS, Marvel FA. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. Am J Prev Cardiol 2022; 12:100379. [PMID: 36090536 PMCID: PMC9460561 DOI: 10.1016/j.ajpc.2022.100379] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/21/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
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37
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Sartoretti E, Gennari AG, Maurer A, Sartoretti T, Skawran S, Schwyzer M, Rossi A, Giannopoulos AA, Buechel RR, Gebhard C, Huellner MW, Messerli M. Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT. Sci Rep 2022; 12:19191. [PMID: 36357446 PMCID: PMC9649723 DOI: 10.1038/s41598-022-20005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/07/2022] [Indexed: 11/11/2022] Open
Abstract
Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool's capability of correctly assigning Agatston scores ≥ 1000 (p = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives.
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Affiliation(s)
- Elisabeth Sartoretti
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Antonio G. Gennari
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Alexander Maurer
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Thomas Sartoretti
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Stephan Skawran
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Moritz Schwyzer
- grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.412004.30000 0004 0478 9977Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland ,grid.5801.c0000 0001 2156 2780Health Sciences and Technology, Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Alexia Rossi
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Andreas A. Giannopoulos
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Ronny R. Buechel
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Catherine Gebhard
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.7400.30000 0004 1937 0650Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Martin W. Huellner
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- grid.412004.30000 0004 0478 9977Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland
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Föllmer B, Biavati F, Wald C, Stober S, Ma J, Dewey M, Samek W. Active multitask learning with uncertainty-weighted loss for coronary calcium scoring. Med Phys 2022; 49:7262-7277. [PMID: 35861655 DOI: 10.1002/mp.15870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 06/01/2021] [Accepted: 06/20/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE The coronary artery calcification (CAC) score is an independent marker for the risk of cardiovascular events. Automatic methods for quantifying CAC could reduce workload and assist radiologists in clinical decision-making. However, large annotated datasets are needed for training to achieve very good model performance, which is an expensive process and requires expert knowledge. The number of training data required can be reduced in an active learning scenario, which requires only the most informative samples to be labeled. Multitask learning techniques can improve model performance by joint learning of multiple related tasks and extraction of shared informative features. METHODS We propose an uncertainty-weighted multitask learning model for coronary calcium scoring in electrocardiogram-gated (ECG-gated), noncontrast-enhanced cardiac calcium scoring CT. The model was trained to solve the two tasks of coronary artery region segmentation (weak labels) and coronary artery calcification segmentation (strong labels) simultaneously in an active learning scenario to improve model performance and reduce the number of samples needed for training. We compared our model with a single-task U-Net and a sequential-task model as well as other state-of-the-art methods. The model was evaluated on 1275 individual patients in three different datasets (DISCHARGE, CADMAN, orCaScore), and the relationship between model performance and various influencing factors (image noise, metal artifacts, motion artifacts, image quality) was analyzed. RESULTS Joint learning of multiclass coronary artery region segmentation and binary coronary calcium segmentation improved calcium scoring performance. Since shared information can be learned from both tasks for complementary purposes, the model reached optimal performance with only 12% of the training data and one-third of the labeling time in an active learning scenario. We identified image noise as one of the most important factors influencing model performance along with anatomical abnormalities and metal artifacts. CONCLUSIONS Our multitask learning approach with uncertainty-weighted loss improves calcium scoring performance by joint learning of shared features and reduces labeling costs when trained in an active learning scenario.
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Affiliation(s)
- Bernhard Föllmer
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Federico Biavati
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Wald
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Stober
- Artificial Intelligence Lab, Otto-von-Guericke-Universität, Magdeburg, Germany
| | - Jackie Ma
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Marc Dewey
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health and DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Wojciech Samek
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
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39
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Palm V, Norajitra T, von Stackelberg O, Heussel CP, Skornitzke S, Weinheimer O, Kopytova T, Klein A, Almeida SD, Baumgartner M, Bounias D, Scherer J, Kades K, Gao H, Jäger P, Nolden M, Tong E, Eckl K, Nattenmüller J, Nonnenmacher T, Naas O, Reuter J, Bischoff A, Kroschke J, Rengier F, Schlamp K, Debic M, Kauczor HU, Maier-Hein K, Wielpütz MO. AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine. Healthcare (Basel) 2022; 10:2166. [PMID: 36360507 PMCID: PMC9690402 DOI: 10.3390/healthcare10112166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 08/12/2023] Open
Abstract
Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.
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Affiliation(s)
- Viktoria Palm
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Tobias Norajitra
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Claus P. Heussel
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Stephan Skornitzke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Taisiya Kopytova
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Andre Klein
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Silvia D. Almeida
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Dimitrios Bounias
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Jonas Scherer
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Klaus Kades
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Hanno Gao
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Paul Jäger
- Interactive Machine Learning Research Group, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Elizabeth Tong
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kira Eckl
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Omar Naas
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Julia Reuter
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Arved Bischoff
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Jonas Kroschke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Fabian Rengier
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Manuel Debic
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Klaus Maier-Hein
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Mark O. Wielpütz
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
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Choi JH, Cha MJ, Cho I, Kim WD, Ha Y, Choi H, Lee SH, You SC, Chang JS. Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer. Front Oncol 2022; 12:989250. [PMID: 36203468 PMCID: PMC9530804 DOI: 10.3389/fonc.2022.989250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/30/2022] [Indexed: 11/20/2022] Open
Abstract
This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer who underwent non-contrast-enhanced chest CT between 2013 and 2015 were included. Agatston scores obtained by manual segmentation of CAC on chest CT were used as reference. Reliability of automated CAC score acquisition was evaluated using intraclass correlation coefficients (ICCs). The agreement for cardiovascular disease (CVD) risk stratification was assessed with linearly weighted k statistics. ICCs between the manual and automated CAC scores were 0.992 (95% CI, 0.991 and 0.993, p<0.001) for total Agatston scores, 0.863 (95% CI, 0.844 and 0.880, p<0.001) for the left main, 0.964 (95% CI, 0.959 and 0.968, p<0.001) for the left anterior descending, 0.962 (95% CI, 0.956 and 0.966, p<0.001) for the left circumflex, and 0.980 (95% CI, 0.978 and 0.983, p<0.001) for the right coronary arteries. The agreement for cardiovascular risk was excellent (k=0.946, p<0.001). Current DL-based automated CAC software showed excellent reliability for Agatston score and CVD risk stratification using non-ECG gated CT scans and might allow the identification of high-risk cancer patients for CVD.
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Affiliation(s)
- Joo Hyeok Choi
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Min Jae Cha
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
- *Correspondence: Min Jae Cha, ; Iksung Cho,
| | - Iksung Cho
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
- *Correspondence: Min Jae Cha, ; Iksung Cho,
| | - William D. Kim
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Yera Ha
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Hyewon Choi
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Sun Hwa Lee
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
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Morf C, Sartoretti T, Gennari AG, Maurer A, Skawran S, Giannopoulos AA, Sartoretti E, Schwyzer M, Curioni-Fontecedro A, Gebhard C, Buechel RR, Kaufmann PA, Huellner MW, Messerli M. Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT. Diagnostics (Basel) 2022; 12:diagnostics12081876. [PMID: 36010226 PMCID: PMC9406755 DOI: 10.3390/diagnostics12081876] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/24/2022] [Accepted: 07/27/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives: The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. Methods: A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. Results: On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. Conclusions: Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.
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Affiliation(s)
- Claudia Morf
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Antonio G. Gennari
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Andreas A. Giannopoulos
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Elisabeth Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Moritz Schwyzer
- University of Zurich, 8006 Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Alessandra Curioni-Fontecedro
- University of Zurich, 8006 Zurich, Switzerland
- Department of Medical Oncology and Hematology, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Catherine Gebhard
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, 8006 Zurich, Switzerland
| | - Ronny R. Buechel
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Philipp A. Kaufmann
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Martin W. Huellner
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland; (C.M.); (T.S.); (A.G.G.); (A.M.); (S.S.); (A.A.G.); (E.S.); (C.G.); (R.R.B.); (P.A.K.); (M.W.H.)
- University of Zurich, 8006 Zurich, Switzerland
- Correspondence:
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Improving Quality in Cardiothoracic Surgery: Exploiting the Untapped Potential of Machine Learning. Ann Thorac Surg 2022; 114:1995-2000. [DOI: 10.1016/j.athoracsur.2022.06.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/12/2022] [Accepted: 06/17/2022] [Indexed: 11/17/2022]
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Chen H, Ouyang D, Baykaner T, Jamal F, Cheng P, Rhee JW. Artificial intelligence applications in cardio-oncology: Leveraging high dimensional cardiovascular data. Front Cardiovasc Med 2022; 9:941148. [PMID: 35958422 PMCID: PMC9360492 DOI: 10.3389/fcvm.2022.941148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022] Open
Abstract
Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment. As many cancer patients undergo frequent surveillance through imaging as well as other diagnostic testing, there is a wealth of information that can be utilized to assess one's risk for cardiovascular complications of cancer therapies. Over the past decade, there have been remarkable advances in applying artificial intelligence (AI) to analyze cardiovascular data obtained from electrocardiograms, echocardiograms, computed tomography, and cardiac magnetic resonance imaging to detect early signs or future risk of cardiovascular diseases. Studies have shown AI-guided cardiovascular image analysis can accurately, reliably and inexpensively identify and quantify cardiovascular risk, leading to better detection of at-risk or disease features, which may open preventive and therapeutic opportunities in cardio-oncology. In this perspective, we discuss the potential for the use of AI in analyzing cardiovascular data to identify cancer patients at risk for cardiovascular complications early in treatment which would allow for rapid intervention to prevent adverse cardiovascular outcomes.
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Affiliation(s)
- Haidee Chen
- City of Hope National Medical Center, Duarte, CA, United States
| | - David Ouyang
- Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Tina Baykaner
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States
| | - Faizi Jamal
- City of Hope National Medical Center, Duarte, CA, United States
| | - Paul Cheng
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States
- Paul Cheng
| | - June-Wha Rhee
- City of Hope National Medical Center, Duarte, CA, United States
- *Correspondence: June-Wha Rhee
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44
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Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics (Basel) 2022; 12:diagnostics12071660. [PMID: 35885564 PMCID: PMC9318450 DOI: 10.3390/diagnostics12071660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.
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45
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Gupta MD, Kunal S, Girish M, Gupta A, Yadav R. Artificial intelligence in Cardiology: the past, present and future. Indian Heart J 2022; 74:265-269. [PMID: 35917970 PMCID: PMC9453051 DOI: 10.1016/j.ihj.2022.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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46
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Pedersen LN, Khoobchandani M, Brenneman R, Mitchell JD, Bergom C. Radiation-Induced Cardiac Dysfunction: Optimizing Radiation Delivery and Postradiation Care. Heart Fail Clin 2022; 18:403-413. [PMID: 35718415 DOI: 10.1016/j.hfc.2022.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Radiation therapy (RT) is part of standard-of-care treatment of many thoracic cancers. More than 60% of patients receiving thoracic RT may eventually develop radiation-induced cardiac dysfunction (RICD) secondary to collateral heart dose. This article reviews factors contributing to a thoracic cancer patient's risk for RICD, including RT dose to the heart and/or cardiac substructures, other anticancer treatments, and a patient's cardiometabolic health. It is also discussed how automated tracking of these factors within electronic medical record environments may aid radiation oncologists and other treating physicians in their ability to prevent, detect, and/or treat RICD in this expanding patient population.
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Affiliation(s)
- Lauren N Pedersen
- Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, St. Louis, MO 63110, USA
| | - Menka Khoobchandani
- Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, St. Louis, MO 63110, USA
| | - Randall Brenneman
- Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, St. Louis, MO 63110, USA; Alvin J. Siteman Center, Washington University in St. Louis, St Louis, MO, USA
| | - Joshua D Mitchell
- Cardio-Oncology Center of Excellence, Washington University in St. Louis, St Louis, MO, USA; Alvin J. Siteman Center, Washington University in St. Louis, St Louis, MO, USA; Division of Cardiology, Department of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - Carmen Bergom
- Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, St. Louis, MO 63110, USA; Cardio-Oncology Center of Excellence, Washington University in St. Louis, St Louis, MO, USA; Alvin J. Siteman Center, Washington University in St. Louis, St Louis, MO, USA.
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47
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Al-Dasuqi K, Johnson MH, Cavallo JJ. Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities. Clin Imaging 2022; 89:61-67. [PMID: 35716432 DOI: 10.1016/j.clinimag.2022.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022]
Abstract
The value of artificial intelligence (AI) in healthcare has become evident, especially in the field of medical imaging. The accelerated pace and acuity of care in the Emergency Department (ED) has made it a popular target for artificial intelligence-driven solutions. Software that helps better detect, report, and appropriately guide management can ensure high quality patient care while enabling emergency radiologists to better meet the demands of quick turnaround times. Beyond diagnostic applications, AI-based algorithms also have the potential to optimize other important steps within the ED imaging workflow. This review will highlight the different types of AI-based applications currently available for use in the ED, as well as the challenges and opportunities associated with their implementation.
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Affiliation(s)
- Khalid Al-Dasuqi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America.
| | - Michele H Johnson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America.
| | - Joseph J Cavallo
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America.
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48
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Pickhardt PJ. Value-added Opportunistic CT Screening: State of the Art. Radiology 2022; 303:241-254. [PMID: 35289661 PMCID: PMC9083232 DOI: 10.1148/radiol.211561] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 12/13/2022]
Abstract
Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.
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Affiliation(s)
- Perry J. Pickhardt
- From the Department of Radiology, The University of Wisconsin School
of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave,
Madison, WI 53792-3252
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49
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Revival and Revision of Right Ventricular Assessment by Machine Learning. JACC. CARDIOVASCULAR IMAGING 2022; 15:780-782. [PMID: 35512950 PMCID: PMC9839432 DOI: 10.1016/j.jcmg.2022.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/13/2022] [Indexed: 01/17/2023]
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50
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Langlais ÉL, Thériault-Lauzier P, Marquis-Gravel G, Kulbay M, So DY, Tanguay JF, Ly HQ, Gallo R, Lesage F, Avram R. Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10260-x. [PMID: 35460017 DOI: 10.1007/s12265-022-10260-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022]
Abstract
Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.
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Affiliation(s)
- Élodie Labrecque Langlais
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada
| | - Pascal Thériault-Lauzier
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Guillaume Marquis-Gravel
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Merve Kulbay
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Derek Y So
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Jean-François Tanguay
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
| | - Hung Q Ly
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
| | - Richard Gallo
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Frédéric Lesage
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada
| | - Robert Avram
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada.
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