1
|
Narayanan A, Kong F, Shadden S. LinFlo-Net: A Two-Stage Deep Learning Method to Generate Simulation Ready Meshes of the Heart. J Biomech Eng 2024; 146:071005. [PMID: 38258957 DOI: 10.1115/1.4064527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/19/2024] [Indexed: 01/24/2024]
Abstract
We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fit the cardiac structures to the given image. Compared with prior deep learning methods that adopted this approach, our framework is designed to minimize mesh self-penetration, which typically arises when deforming surface meshes separated by small distances. We achieve this by using a two-stage diffeomorphic deformation process along with a novel loss function derived from the kinematics of motion that penalizes surface contact and interpenetration. Our model demonstrates comparable accuracy with state-of-the-art methods while additionally producing meshes free of self-intersections. The resultant meshes are readily usable in physics based simulation, minimizing the need for postprocessing and cleanup.
Collapse
Affiliation(s)
- Arjun Narayanan
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94709
| | - Fanwei Kong
- Department of Pediatrics, Stanford University, Stanford, CA 94305
- Stanford University
| | - Shawn Shadden
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94709
| |
Collapse
|
2
|
Black D, Singh T, Molloi S. Coronary artery calcium quantification technique using dual energy material decomposition: a simulation study. Int J Cardiovasc Imaging 2024; 40:1465-1474. [PMID: 38904849 PMCID: PMC11258084 DOI: 10.1007/s10554-024-03124-9] [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: 01/29/2024] [Accepted: 04/28/2024] [Indexed: 06/22/2024]
Abstract
Coronary artery calcification is a significant predictor of cardiovascular disease, with current detection methods like Agatston scoring having limitations in sensitivity. This study aimed to evaluate the effectiveness of a novel CAC quantification method using dual-energy material decomposition, particularly its ability to detect low-density calcium and microcalcifications. A simulation study was conducted comparing the dual-energy material decomposition technique against the established Agatston scoring method and the newer volume fraction calcium mass technique. Detection accuracy and calcium mass measurement were the primary evaluation metrics. The dual-energy material decomposition technique demonstrated fewer false negatives than both Agatston scoring and volume fraction calcium mass, indicating higher sensitivity. In low-density phantom measurements, material decomposition resulted in only 7.41% false-negative (CAC = 0) measurements compared to 83.95% for Agatston scoring. For high-density phantoms, false negatives were removed (0.0%) compared to 20.99% in Agatston scoring. The dual-energy material decomposition technique presents a more sensitive and reliable method for CAC quantification.
Collapse
Affiliation(s)
- Dale Black
- Department of Radiological Sciences, University of California, Medical Sciences I, B-140, Irvine, CA, 92697, USA
| | - Tejus Singh
- Department of Radiological Sciences, University of California, Medical Sciences I, B-140, Irvine, CA, 92697, USA
| | - Sabee Molloi
- Department of Radiological Sciences, University of California, Medical Sciences I, B-140, Irvine, CA, 92697, USA.
| |
Collapse
|
3
|
Onnis C, van Assen M, Muscogiuri E, Muscogiuri G, Gershon G, Saba L, De Cecco CN. The Role of Artificial Intelligence in Cardiac Imaging. Radiol Clin North Am 2024; 62:473-488. [PMID: 38553181 DOI: 10.1016/j.rcl.2024.01.002] [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] [Indexed: 04/02/2024]
Abstract
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.
Collapse
Affiliation(s)
- Carlotta Onnis
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/CarlottaOnnis
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/marly_van_assen
| | - Emanuele Muscogiuri
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Thoracic Imaging, Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Giuseppe Muscogiuri
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Piazza OMS, 1, Bergamo BG 24127, Italy. https://twitter.com/GiuseppeMuscog
| | - Gabrielle Gershon
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/gabbygershon
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/lucasabaITA
| | - Carlo N De Cecco
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Emory University Hospital, 1365 Clifton Road Northeast, Suite AT503, Atlanta, GA 30322, USA.
| |
Collapse
|
4
|
Herten VRLM, Hampe N, Takx RAP, Franssen KJ, Wang Y, Sucha D, Henriques JP, Leiner T, Planken RN, Isgum I. Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction Using Mesh Priors. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1272-1283. [PMID: 37862273 DOI: 10.1109/tmi.2023.3326243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of cardiovascular events and determine the treatment. Clinical analysis of coronary arteries in CCTA comprises the identification of atherosclerotic plaque, as well as the grading of any coronary artery stenosis typically obtained through the CAD-Reporting and Data System (CAD-RADS). This requires analysis of the coronary lumen and plaque. While voxel-wise segmentation is a commonly used approach in various segmentation tasks, it does not guarantee topologically plausible shapes. To address this, in this work, we propose to directly infer surface meshes for coronary artery lumen and plaque based on a centerline prior and use it in the downstream task of CAD-RADS scoring. The method is developed and evaluated using a total of 2407 CCTA scans. Our method achieved lesion-wise volume intraclass correlation coefficients of 0.98, 0.79, and 0.85 for calcified, non-calcified, and total plaque volume respectively. Patient-level CAD-RADS categorization was evaluated on a representative hold-out test set of 300 scans, for which the achieved linearly weighted kappa ( κ ) was 0.75. CAD-RADS categorization on the set of 658 scans from another hospital and scanner led to a κ of 0.71. The results demonstrate that direct inference of coronary artery meshes for lumen and plaque is feasible, and allows for the automated prediction of routinely performed CAD-RADS categorization.
Collapse
|
5
|
Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [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: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
Collapse
Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
| |
Collapse
|
6
|
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.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
7
|
Black D, Xiao X, Molloi S. Integrated intensity-based technique for coronary artery calcium mass measurement: A phantom study. Med Phys 2023; 50:4930-4942. [PMID: 36852776 DOI: 10.1002/mp.16326] [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: 10/03/2022] [Revised: 01/13/2023] [Accepted: 01/30/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Agatston scoring, the traditional method for measuring coronary artery calcium, is limited in its ability to accurately quantify low-density calcifications, among other things. The inaccuracy of Agatston scoring is likely due partly to the arbitrary thresholding requirement of Agatston scoring. PURPOSE A calcium quantification technique that removes the need for arbitrary thresholding and is more accurate, sensitive, reproducible, and robust is needed. Improvements to calcium scoring will likely improve patient risk stratification and outcome. METHODS The integrated Hounsfield technique was adapted for calcium scoring (integrated calcium mass). Integrated calcium mass requires no thresholding and includes all calcium information within an image. This study utilized phantom images acquired by G van Praagh et al., with calcium hydroxyapatite (HA) densities in the range of 200-800 mgHAcm-3 to measure calcium according to integrated calcium mass and Agatston scoring. The calcium mass was known, which allowed for accuracy, reproducibility, sensitivity, and robustness comparisons between integrated calcium mass and Agatston scoring. Multiple CT vendors (Canon, GE, Philips, Siemens) were used during the image acquisition phase, which provided a more robust comparison between the two calcium scoring techniques. Three calcification inserts of different diameters (1, 3, and 5 mm) and different HA densities (200, 400, and 800 mgHAcm-3 ) were placed within the phantom. The effect of motion was also analyzed using a dynamic phantom. All dynamic phantom calcium inserts were 5.0 ± 0.1 mm in diameter with a length of 10.0 ± 0.1 mm. The four different densities were 196 ± 3, 380 ± 2, 408 ± 2, and 800 ± 2 mgHAcm-3 . RESULTS Integrated calcium mass was more accurate than Agatston scoring for stationary scans (R M S E I n t e g r a t e d = 2.87 $RMS{E}_{Integrated} = 2.87$ ,R M S E A g a t s o n = 4.07 $RMS{E}_{Agatson} = 4.07$ ) and motion affected scans (R M S E I n t e g r a t e d = 9.70 $RMS{E}_{Integrated} = 9.70$ ,R M S E A g a t s o n = 19.98 $RMS{E}_{Agatson} = 19.98$ ). On average, integrated calcium mass was more reproducible than Agatston scoring for two of the CT vendors. The percentage of false-negative and false-positive calcium scores were lower for integrated calcium mass (15.00%, 0.00%) than Agatston scoring (28.33%, 6.67%). Integrated calcium mass was more robust to changes in scan parameters than Agatston scoring. CONCLUSIONS The results of this study indicate that integrated calcium mass is more accurate, reproducible, and sensitive than Agatston scoring on a variety of different CT vendors. The substantial reduction in false-negative scores for integrated calcium mass is likely to improve risk-stratification for patients undergoing calcium scoring and their potential outcome.
Collapse
Affiliation(s)
- Dale Black
- Department of Radiological Sciences, University of California, Irvine, California, USA
| | - Xingshuo Xiao
- Department of Radiological Sciences, University of California, Irvine, California, USA
| | - Sabee Molloi
- Department of Radiological Sciences, University of California, Irvine, California, USA
| |
Collapse
|
8
|
Black D, Xiao X, Molloi S. Coronary artery calcium mass measurement based on integrated intensity and volume fraction techniques. J Med Imaging (Bellingham) 2023; 10:043502. [PMID: 37434664 PMCID: PMC10332802 DOI: 10.1117/1.jmi.10.4.043502] [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: 01/16/2023] [Revised: 05/11/2023] [Accepted: 06/26/2023] [Indexed: 07/13/2023] Open
Abstract
Purpose Agatston scoring does not detect all the calcium present in computed tomography scans of the heart. A technique that removes the need for thresholding and quantifies calcium mass more accurately and reproducibly is needed. Approach Integrated intensity and volume fraction techniques were evaluated for accurate quantification of calcium mass. Integrated intensity calcium mass, volume fraction calcium mass, Agatston scoring, and spatially weighted calcium scoring were compared with known calcium mass in simulated and physical phantoms. The simulation was created to match a 320-slice CT scanner. Fat rings were added to the simulated phantoms, which resulted in small (30 × 20 cm 2 ), medium (35 × 25 cm 2 ), and large (40 × 30 cm 2 ) phantoms. Three calcification inserts of different diameters and hydroxyapatite densities were placed within the phantoms. All the calcium mass measurements were repeated across different beam energies, patient sizes, insert sizes, and densities. Physical phantom images from a previously reported study were then used to evaluate the accuracy and reproducibility of the techniques. Results Both integrated intensity calcium mass and volume fraction calcium mass yielded lower root mean squared error (RMSE) and deviation (RMSD) values than Agatston scoring in all the measurements in the simulated phantoms. Specifically, integrated calcium mass (RMSE: 0.49 mg, RMSD: 0.49 mg) and volume fraction calcium mass (RMSE: 0.58 mg, RMSD: 0.57 mg) were more accurate for the low-density stationary calcium measurements than Agatston scoring (RMSE: 3.70 mg, RMSD: 2.30 mg). Similarly, integrated calcium mass (15.74%) and volume fraction calcium mass (20.37%) had fewer false-negative (CAC = 0) measurements than Agatston scoring (75.00%) and spatially weighted calcium scoring (26.85%), on the low-density stationary calcium measurements. Conclusion The integrated calcium mass and volume fraction calcium mass techniques can potentially improve risk stratification for patients undergoing calcium scoring and further improve risk assessment compared with Agatston scoring.
Collapse
Affiliation(s)
- Dale Black
- University of California, Irvine, Department of Radiological Sciences, Irvine, California, United States
| | - Xingshuo Xiao
- University of California, Irvine, Department of Radiological Sciences, Irvine, California, United States
| | - Sabee Molloi
- University of California, Irvine, Department of Radiological Sciences, Irvine, California, United States
| |
Collapse
|
9
|
Miller RJH, Pieszko K, Shanbhag A, Feher A, Lemley M, Killekar A, Kavanagh PB, Van Kriekinge SD, Liang JX, Huang C, Miller EJ, Bateman T, Berman DS, Dey D, Slomka PJ. Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events. J Nucl Med 2023; 64:652-658. [PMID: 36207138 PMCID: PMC10071789 DOI: 10.2967/jnumed.122.264423] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model, including correlation with expert annotations and associations with major adverse cardiovascular events (MACE). Methods: We trained a convolutional long short-term memory DL model to automatically quantify CAC on CTAC scans using 6,608 studies (2 centers) and evaluated the model in an external cohort of patients without known coronary artery disease (n = 2,271) obtained in a separate center. We assessed agreement between DL and expert annotated CAC scores. We also assessed associations between MACE (death, revascularization, myocardial infarction, or unstable angina) and CAC categories (0, 1-100, 101-400, or >400) for scores manually derived by experienced readers and scores obtained fully automatically by DL using multivariable Cox models (adjusted for age, sex, past medical history, perfusion, and ejection fraction) and net reclassification index. Results: In the external testing population, DL CAC was 0 in 908 patients (40.0%), 1-100 in 596 (26.2%), 100-400 in 354 (15.6%), and >400 in 413 (18.2%). Agreement in CAC category by DL CAC and expert annotation was excellent (linear weighted κ, 0.80), but DL CAC was obtained automatically in less than 2 s compared with about 2.5 min for expert CAC. DL CAC category was an independent risk factor for MACE with hazard ratios in comparison to a CAC of zero: CAC of 1-100 (2.20; 95% CI, 1.54-3.14; P < 0.001), CAC of 101-400 (4.58; 95% CI, 3.23-6.48; P < 0.001), and CAC of more than 400 (5.92; 95% CI, 4.27-8.22; P < 0.001). Overall, the net reclassification index was 0.494 for DL CAC, which was similar to expert annotated CAC (0.503). Conclusion: DL CAC from SPECT/CT attenuation maps agrees well with expert CAC annotations and provides a similar risk stratification but can be obtained automatically. DL CAC scores improved classification of a significant proportion of patients as compared with SPECT myocardial perfusion alone.
Collapse
Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Góra, Zielona Góra, Poland
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Timothy Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, Missouri
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
| |
Collapse
|
10
|
Zair AM, Bouzouad Cherfa A, Cherfa Y, Belkhamsa N. An automated segmentation of coronary artery calcification using deep learning in specific region limitation. Med Biol Eng Comput 2023:10.1007/s11517-023-02797-z. [PMID: 36871109 DOI: 10.1007/s11517-023-02797-z] [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: 08/20/2021] [Accepted: 01/31/2023] [Indexed: 03/06/2023]
Abstract
Coronary artery calcification (CAC) is a frequent disease of the arteries that supply the surface of the heart muscle. Leaving a severe disease untreated can make it permanent. Computer tomography (CT), which is well known for its ability to quantify the Agatston score, is used to visualize high-resolution CACs. CAC segmentation is still an important topic. Our goal is to automatically segment CAC in a specific area and measure the Agatston score in 2D images. The heart region is limited using a threshold, unused structures are removed using 2D connectivity (muscle, lung, ribcage), the heart cavity is extracted using the convex hull of the lungs, and the CAC is then segmented in 2D using a convolutional neural network (U-Net models/SegNet-VGG16 with transfer learning). The Agatston score prediction is calculated for CAC quantification. The proposed strategy is tested through experiments, which yield encouraging outcomes. Graphical Abstract Deep learning for CAC segmentation in CT images.
Collapse
Affiliation(s)
- Asmae Mama Zair
- University of Blida 01, B.P 270, Soumaa road, Blida, Algeria.
| | | | - Yazid Cherfa
- University of Blida 01, B.P 270, Soumaa road, Blida, Algeria
| | | |
Collapse
|
11
|
Schwarz EL, Pegolotti L, Pfaller MR, Marsden AL. Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease. BIOPHYSICS REVIEWS 2023; 4:011301. [PMID: 36686891 PMCID: PMC9846834 DOI: 10.1063/5.0109400] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/12/2022] [Indexed: 01/15/2023]
Abstract
Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.
Collapse
Affiliation(s)
- Erica L. Schwarz
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Luca Pegolotti
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Martin R. Pfaller
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Alison L. Marsden
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
12
|
Kong F, Shadden SC. Learning Whole Heart Mesh Generation From Patient Images for Computational Simulations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:533-545. [PMID: 36327186 DOI: 10.1109/tmi.2022.3219284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Patient-specific cardiac modeling combines geometries of the heart derived from medical images and biophysical simulations to predict various aspects of cardiac function. However, generating simulation-suitable models of the heart from patient image data often requires complicated procedures and significant human effort. We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template. For both 3D CT and MR data, this method achieves promising accuracy for whole heart reconstruction, consistently outperforming prior methods in constructing simulation-suitable meshes of the heart. When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations. Our source code and pretrained networks are available at https://github.com/fkong7/HeartDeformNets.
Collapse
|
13
|
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.
Collapse
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.
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Pieszko K, Shanbhag A, Killekar A, Lemley M, Otaki Y, Kriekinge SV, Kavanagh P, Miller RJ, Miller EJ, Bateman T, Dey D, Berman D, Slomka P. Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:120323A. [PMID: 36277935 PMCID: PMC9585987 DOI: 10.1117/12.2613147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We aimed to develop a novel deep-learning based method for automatic coronary artery calcium (CAC) quantification in low-dose ungated computed tomography attenuation correction maps (CTAC). In this study, we used convolutional long-short -term memory deep neural network (conv-LSTM) to automatically derive coronary artery calcium score (CAC) from both standard CAC scans and low-dose ungated scans (CT-attenuation correction maps). We trained convLSTM to segment CAC using 9543 scans. A U-Net model was trained as a reference method. Both models were validated in the OrCaCs dataset (n=32) and in the held-out cohort (n=507) without prior coronary interventions who had CTAC standard CAC scan acquired contemporarily. Cohen's kappa coefficients and concordance matrices were used to assess agreement in four CAC score categories (very low: <10, low:10-100; moderate:101-400 and high >400). The median time to derive results on a central processing unit (CPU) was significantly shorter for the conv-LSTM model- 6.18s (inter quartile range [IQR]: 5.99, 6.3) than for UNet (10.1s, IQR: 9.82, 15.9s, p<0.0001). The memory consumption during training was much lower for our model (13.11Gb) in comparison with UNet (22.31 Gb). Conv-LSTM performed comparably to UNet in terms of agreement with expert annotations, but with significantly shorter inference times and lower memory consumption.
Collapse
Affiliation(s)
- K Pieszko
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - A Shanbhag
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - A Killekar
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - M Lemley
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Y Otaki
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Serge Van Kriekinge
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Kavanagh
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Jh Miller
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Tim Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA
| | - D Dey
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - D Berman
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - P Slomka
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| |
Collapse
|
16
|
Winkelmann MT, Jacoby J, Schwemmer C, Faby S, Krumm P, Artzner C, Bongers MN. Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. ROFO-FORTSCHR RONTG 2022; 194:763-770. [PMID: 35081651 DOI: 10.1055/a-1717-2703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
PURPOSE Evaluation of machine learning-based fully automated artery-specific coronary artery calcium (CAC) scoring software, using semi-automated software as a reference. METHODS A total of 505 patients underwent non-contrast-enhanced calcium scoring computed tomography (CSCT). Automated, machine learning-based software quantified the Agatston score (AS), volume score (VS), and mass score (MS) of each coronary artery [right coronary artery (RCA), left main (LM), circumflex (CX) and left anterior descending (LAD)]. Identified CAC of readers who annotated the data with semi-automated software served as a reference standard. Statistics included comparisons of evaluation time, agreement of identified CAC, and comparisons of the AS, VS, and MS of the reference standard and the fully automated algorithm. RESULTS The machine learning-based software correlated strongly with the reference standard for the AS, VS, and MS (Spearman's rho > 0.969) (p < 0.001), with excellent agreement (ICC > 0.919) (p < 0.001). The mean assessment time of the reference standard was 59 seconds (IQR 39-140) and that of the automated algorithm was 5.9 seconds (IQR 3.9-16) (p < 0.001). The Bland-Altman plots mean difference and 1.96 upper and lower limits of agreement for all arteries combined were: AS 0.996 (1.33 to 0.74), VS 0.995 (1.40 to 0.71), and MS 0.995 (1.35 to 0.74). The mean bias was minimal: 0.964-1.0429. Risk class assignment showed high accuracy for the AS in total (weighed κ = 0.99) and for each individual artery (κ = 0.96-0.99) with corresponding correct risk group assignment in 497 of 505 patients (98.4 %). CONCLUSION The fully automated artery-specific coronary calcium scoring algorithm is a time-saving procedure and shows excellent correlation and agreement compared with the clinically established semi-automated approach. KEY POINTS · Very high correlation and agreement between fully automatic and semi-automatic calcium scoring software.. · Less time-consuming than conventional semi-automatic methods.. · Excellent tool for artery-specific calcium scoring in a clinical setting.. CITATION FORMAT · Winkelmann MT, Jacoby J, Schwemmer C et al. Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1717-2703.
Collapse
Affiliation(s)
- Moritz T Winkelmann
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Johann Jacoby
- Institute of Clinical Epidemiology and Applied Biometry, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Chris Schwemmer
- Siemens Healthcare GmbH, Forchheim, Siemens Healthcare GmbH, Forchheim, Germany
| | - Sebastian Faby
- Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany
| | - Patrick Krumm
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Christoph Artzner
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Malte N Bongers
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| |
Collapse
|
17
|
Kong F, Wilson N, Shadden S. A deep-learning approach for direct whole-heart mesh reconstruction. Med Image Anal 2021; 74:102222. [PMID: 34543913 PMCID: PMC9503710 DOI: 10.1016/j.media.2021.102222] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/14/2021] [Accepted: 08/31/2021] [Indexed: 01/16/2023]
Abstract
Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision, these approaches have mostly focused on voxel-wise segmentation followed by surface reconstruction and post-processing techniques. However, such approaches suffer from a number of limitations including disconnected regions or incorrect surface topology due to erroneous segmentation and stair-case artifacts due to limited segmentation resolution. We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our approach leverages a graph convolutional neural network to predict deformation on mesh vertices from a pre-defined mesh template to reconstruct multiple anatomical structures in a 3D image volume. Our method demonstrated promising performance of generating whole heart reconstructions with as good or better accuracy than prior deep-learning-based methods on both CT and MR data. Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data. Our method was also able to produce temporally-consistent surface mesh predictions for heart motion from CT or MR cine sequences, and therefore can potentially be applied for efficiently constructing 4D whole heart dynamics. Our code and pre-trained networks are available at https://github.com/fkong7/MeshDeformNet.
Collapse
Affiliation(s)
- Fanwei Kong
- Mechanical Engineering Department, University of California, Berkeley, Berkeley, CA 94709, United States.
| | - Nathan Wilson
- Open Source Medical Software Corporation, Santa Monica, CA, United States.
| | - Shawn Shadden
- Mechanical Engineering Department, University of California, Berkeley, Berkeley, CA 94709, United States.
| |
Collapse
|
18
|
Westerlund AM, Hawe JS, Heinig M, Schunkert H. Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence. Int J Mol Sci 2021; 22:10291. [PMID: 34638627 PMCID: PMC8508897 DOI: 10.3390/ijms221910291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
Collapse
Affiliation(s)
- Annie M. Westerlund
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
| | - Johann S. Hawe
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
| | - Matthias Heinig
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
- Department of Informatics, Technical University Munich, Boltzmannstrasse 3, 85748 Garching, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Biedersteiner Strasse 29, 80802 Munich, Germany
| |
Collapse
|
19
|
Zhang ZZ, Guo Y, Hou Y. Artificial intelligence in coronary computed tomography angiography. Artif Intell Med Imaging 2021; 2:73-85. [DOI: 10.35711/aimi.v2.i3.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/20/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is recommended as a frontline diagnostic tool in the non-invasive assessment of patients with suspected coronary artery disease (CAD) and cardiovascular risk stratification. To date, artificial intelligence (AI) techniques have brought major changes in the way that we make individualized decisions for patients with CAD. Applications of AI in CCTA have produced improvements in many aspects, including assessment of stenosis degree, determination of plaque type, identification of high-risk plaque, quantification of coronary artery calcium score, diagnosis of myocardial infarction, estimation of computed tomography-derived fractional flow reserve, left ventricular myocardium analysis, perivascular adipose tissue analysis, prognosis of CAD, and so on. The purpose of this review is to provide a comprehensive overview of current status of AI in CCTA.
Collapse
Affiliation(s)
- Zhe-Zhe Zhang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Yan Guo
- GE Healthcare, Beijing 100176, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| |
Collapse
|
20
|
Gogin N, Viti M, Nicodème L, Ohana M, Talbot H, Gencer U, Mekukosokeng M, Caramella T, Diascorn Y, Airaud JY, Guillot MS, Bensalah Z, Dam Hieu C, Abdallah B, Bousaid I, Lassau N, Mousseaux E. Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning. Diagn Interv Imaging 2021; 102:683-690. [PMID: 34099435 DOI: 10.1016/j.diii.2021.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/23/2022]
Abstract
PURPOSE The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN). MATERIALS AND METHODS The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations. RESULTS The final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. CONCLUSION The deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.
Collapse
Affiliation(s)
| | - Mario Viti
- General Electric Healthcare, 78530 Buc, France; CentraleSupélec, Université Paris-Saclay, CentraleSupélec, Inria, 91192 Gif-sur-Yvette, France
| | | | - Mickaël Ohana
- Service de Radiologie, CHU de Strasbourg, 67000 Strasbourg, France
| | - Hugues Talbot
- CentraleSupélec, Université Paris-Saclay, CentraleSupélec, Inria, 91192 Gif-sur-Yvette, France
| | - Umit Gencer
- Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
| | | | | | - Yann Diascorn
- Institut Arnault Tzanck, 06123 Saint-Laurent-du-Var, France
| | - Jean-Yves Airaud
- Department of Radiology, Polyclinique Inkermann, 79000 Niort, France
| | - Marc-Samir Guillot
- Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
| | - Zoubir Bensalah
- Department of Radiology, Centre Hospitalier de Perpignan, 66000 Perpignan, France
| | | | | | - Imad Bousaid
- Imaging Department, Gustave-Roussy, Université Paris-Saclay, 94076 Villejuif, France
| | - Nathalie Lassau
- Imaging Department, Gustave-Roussy, Université Paris-Saclay, 94076 Villejuif, France; Biomaps, UMR 1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94076 Villejuif, France
| | - Elie Mousseaux
- Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
| |
Collapse
|
21
|
Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, Gimelli A, Georgoulias P, Gheysens O, Gaemperli O, Habib G, Hustinx R, Cosyns B, Verberne HJ, Hyafil F, Erba PA, Lubberink M, Slomka P, Išgum I, Visvikis D, Kolossváry M, Saraste A. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 2021; 48:1399-1413. [PMID: 33864509 PMCID: PMC8113178 DOI: 10.1007/s00259-021-05341-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 12/18/2022]
Abstract
In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
Collapse
Affiliation(s)
- Riemer H J A Slart
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
- Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands.
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Andor W J M Glaudemans
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | | | - Panagiotis Georgoulias
- Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece
| | - Olivier Gheysens
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium
| | | | - Gilbert Habib
- APHM, Cardiology Department, La Timone Hospital, Marseille, France
- IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium
| | - Bernard Cosyns
- Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium
| | - Hein J Verberne
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fabien Hyafil
- Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France
- University of Paris, PARCC, INSERM, F-75006, Paris, France
| | - Paola A Erba
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy
- Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy
| | - Mark Lubberink
- Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
| | - Piotr Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ivana Išgum
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands
| | | | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
| |
Collapse
|
22
|
Schirmer MD, Venkataraman A, Rekik I, Kim M, Mostofsky SH, Nebel MB, Rosch K, Seymour K, Crocetti D, Irzan H, Hütel M, Ourselin S, Marlow N, Melbourne A, Levchenko E, Zhou S, Kunda M, Lu H, Dvornek NC, Zhuang J, Pinto G, Samal S, Zhang J, Bernal-Rusiel JL, Pienaar R, Chung AW. Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge. Med Image Anal 2021; 70:101972. [PMID: 33677261 PMCID: PMC9115580 DOI: 10.1016/j.media.2021.101972] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 11/25/2020] [Accepted: 01/11/2021] [Indexed: 01/26/2023]
Abstract
Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.
Collapse
Affiliation(s)
- Markus D Schirmer
- Massachusetts General Hospital, Harvard Medical School, Boston, USA; German Center for Neurodegenerative Diseases, Bonn, Germany; Clinic for Neuroradiology, University Hospital Bonn, Germany; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA.
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Keri Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA; Department of Radiology, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA
| | - Karen Seymour
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Hassna Irzan
- Department of Medical Physics and Biomedical Engineering, University College London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Michael Hütel
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Neil Marlow
- Institute for Women's Health, University College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Medical Physics and Biomedical Engineering, University College London, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Egor Levchenko
- Institute for Cognitive Neuroscience, Higher School of Economics, Moscow, Russia; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Shuo Zhou
- Department of Computer Science, The University of Sheffield, Sheffield, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Mwiza Kunda
- Department of Computer Science, The University of Sheffield, Sheffield, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Haiping Lu
- Department of Computer Science, The University of Sheffield, Sheffield, UK; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Nicha C Dvornek
- Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Juntang Zhuang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Gideon Pinto
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Sandip Samal
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Jennings Zhang
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Jorge L Bernal-Rusiel
- Teradyte LLC, Coral Gables, FL, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA
| | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, USA.
| |
Collapse
|
23
|
Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications. Diagnostics (Basel) 2021; 11:diagnostics11030551. [PMID: 33808677 PMCID: PMC8003459 DOI: 10.3390/diagnostics11030551] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 01/10/2023] Open
Abstract
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.
Collapse
|
24
|
Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography. J Thorac Imaging 2021; 35 Suppl 1:S49-S57. [PMID: 32168163 DOI: 10.1097/rti.0000000000000491] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. MATERIALS AND METHODS Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. RESULTS CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. CONCLUSION The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.
Collapse
|
25
|
Zhang N, Yang G, Zhang W, Wang W, Zhou Z, Zhang H, Xu L, Chen Y. Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. Eur J Radiol 2021; 134:109420. [PMID: 33302029 PMCID: PMC7814341 DOI: 10.1016/j.ejrad.2020.109420] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/27/2020] [Accepted: 11/14/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores (CACS) quantification via deep learning on nonenhanced cardiac CT images. METHODS In this retrospective single-centre study, a multi-task deep learning framework was proposed to detect and quantify coronary artery calcification from CT images collected between October 2018 and March 2019. A total of 232 non-contrast cardiac-gated CT scans were retrieved and studied (80 % for model training and 20 % for testing). CACS results of testing datasets (n = 46), including Agatston score, calcium volume score, calcium mass score, were calculated fully automatically and manually at total and vessel-specific levels, respectively. RESULTS No significant differences were found in CACS quantification obtained using automatic or manual methods at total and vessel-specific levels (Agatston score: automatic 535.3 vs. manual 542.0, P = 0.993; calcium volume score: automatic 454.2 vs. manual 460.6, P = 0.990; calcium mass score: automatic 128.9 vs. manual 129.5, P = 0.992). Compared to the ground truth, the number of calcified vessels can be accurate recognized automatically (total: automatic 107 vs. manual 102, P = 0.125; left main artery: automatic 15 vs. manual 14, P = 1.000 ; left ascending artery: automatic 37 vs. manual 37, P = 1.000; left circumflex artery: automatic 22 vs. manual 20, P = 0.625; right coronary artery: automatic 33 vs. manual 31, P = 0.500). At the patient's level, there was no statistic difference existed in the classification of Agatston scoring (P = 0.317) and the number of calcified vessels (P = 0.102) between the automatic and manual results. CONCLUSIONS The proposed framework can achieve reliable and comprehensive quantification for the CACS, including the calcified extent and distribution indicators at both total and vessel-specific levels.
Collapse
Affiliation(s)
- Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Weiwei Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, China
| | - Wenjing Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China
| | - Zhen Zhou
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China.
| | - Yundai Chen
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
26
|
Bruns S, Wolterink JM, Takx RAP, Hamersvelt RW, Suchá D, Viergever MA, Leiner T, Išgum I. Deep learning from dual‐energy information for whole‐heart segmentation in dual‐energy and single‐energy non‐contrast‐enhanced cardiac CT. Med Phys 2020; 47:5048-5060. [DOI: 10.1002/mp.14451] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 07/27/2020] [Accepted: 08/03/2020] [Indexed: 11/11/2022] Open
Affiliation(s)
- Steffen Bruns
- Department of Biomedical Engineering and Physics Amsterdam UMC – location AMCUniversity of Amsterdam Amsterdam1105 AZ Netherlands
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
- Amsterdam Cardiovascular SciencesAmsterdam UMC Amsterdam1105 AZ Netherlands
| | - Jelmer M. Wolterink
- Department of Biomedical Engineering and Physics Amsterdam UMC – location AMCUniversity of Amsterdam Amsterdam1105 AZ Netherlands
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
- Amsterdam Cardiovascular SciencesAmsterdam UMC Amsterdam1105 AZ Netherlands
| | - Richard A. P. Takx
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Robbert W. Hamersvelt
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Dominika Suchá
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Max A. Viergever
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Tim Leiner
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics Amsterdam UMC – location AMCUniversity of Amsterdam Amsterdam1105 AZ Netherlands
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
- Amsterdam Cardiovascular SciencesAmsterdam UMC Amsterdam1105 AZ Netherlands
- Department of Radiology and Nuclear Medicine Amsterdam UMC – location AMC Amsterdam1105 AZ Netherlands
| |
Collapse
|
27
|
Slomka PJ, Miller RJH, Isgum I, Dey D. Application and Translation of Artificial Intelligence to Cardiovascular Imaging in Nuclear Medicine and Noncontrast CT. Semin Nucl Med 2020; 50:357-366. [DOI: 10.1053/j.semnuclmed.2020.03.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
28
|
Martin SS, van Assen M, Rapaka S, Hudson HT, Fischer AM, Varga-Szemes A, Sahbaee P, Schwemmer C, Gulsun MA, Cimen S, Sharma P, Vogl TJ, Schoepf UJ. Evaluation of a Deep Learning–Based Automated CT Coronary Artery Calcium Scoring Algorithm. JACC Cardiovasc Imaging 2020; 13:524-526. [DOI: 10.1016/j.jcmg.2019.09.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 08/19/2019] [Accepted: 09/13/2019] [Indexed: 11/28/2022]
|
29
|
Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I. Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey. Front Cardiovasc Med 2019; 6:172. [PMID: 32039237 PMCID: PMC6988816 DOI: 10.3389/fcvm.2019.00172] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/10/2023] Open
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
Collapse
Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jelmer M Wolterink
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sanne G M van Velzen
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
30
|
Sandstedt M, Henriksson L, Janzon M, Nyberg G, Engvall J, De Geer J, Alfredsson J, Persson A. Evaluation of an AI-based, automatic coronary artery calcium scoring software. Eur Radiol 2019; 30:1671-1678. [PMID: 31728692 PMCID: PMC7033052 DOI: 10.1007/s00330-019-06489-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/26/2019] [Accepted: 10/09/2019] [Indexed: 11/04/2022]
Abstract
Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.
Collapse
Affiliation(s)
- Mårten Sandstedt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. .,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden.
| | - Lilian Henriksson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Magnus Janzon
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Gusten Nyberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Jan Engvall
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Jakob De Geer
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Joakim Alfredsson
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Anders Persson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| |
Collapse
|
31
|
Kuijf HJ, Biesbroek JM, De Bresser J, Heinen R, Andermatt S, Bento M, Berseth M, Belyaev M, Cardoso MJ, Casamitjana A, Collins DL, Dadar M, Georgiou A, Ghafoorian M, Jin D, Khademi A, Knight J, Li H, Llado X, Luna M, Mahmood Q, McKinley R, Mehrtash A, Ourselin S, Park BY, Park H, Park SH, Pezold S, Puybareau E, Rittner L, Sudre CH, Valverde S, Vilaplana V, Wiest R, Xu Y, Xu Z, Zeng G, Zhang J, Zheng G, Chen C, van der Flier W, Barkhof F, Viergever MA, Biessels GJ. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2556-2568. [PMID: 30908194 PMCID: PMC7590957 DOI: 10.1109/tmi.2019.2905770] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.
Collapse
|
32
|
Rajendra Acharya U, Meiburger KM, Wei Koh JE, Vicnesh J, Ciaccio EJ, Shu Lih O, Tan SK, Aman RRAR, Molinari F, Ng KH. Automated plaque classification using computed tomography angiography and Gabor transformations. Artif Intell Med 2019; 100:101724. [PMID: 31607348 DOI: 10.1016/j.artmed.2019.101724] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/23/2019] [Accepted: 09/06/2019] [Indexed: 12/18/2022]
Abstract
Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients. The automated technique is based on various features that are extracted from the Gabor transform of the acquired CTA images. Specifically, seven features are extracted from the Gabor coefficients : energy, and Kapur, Max, Rényi, Shannon, Vajda, and Yager entropies. The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. Moreover, two well-known feature reduction techniques were employed, and the features acquired were also ranked according to F-value and input to several classifiers. The best classification results were obtained using all computed features without the employment of feature reduction, using a probabilistic neural network. An accuracy, positive predictive value, sensitivity, and specificity of 89.09%, 91.70%, 91.83% and 83.70% was obtained, respectively. Based on these results, it is evident that the technique can be helpful in the automated classification of plaques present in CTA images, and may become an important tool to reduce procedural costs and patient radiation dose. This could also aid clinicians in plaque diagnostics.
Collapse
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
| | - Kristen M Meiburger
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - Edward J Ciaccio
- Department of Medicine - Division of Cardiology, Columbia University, New York, USA
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Sock Keow Tan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Raja Rizal Azman Raja Aman
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| |
Collapse
|
33
|
de Vos BD, Wolterink JM, Leiner T, de Jong PA, Lessmann N, Isgum I. Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2127-2138. [PMID: 30794169 DOI: 10.1109/tmi.2019.2899534] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet the demands of the increasing interest in quantification of CAC, i.e., coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. The current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two convolutional neural networks: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing time-consuming intermediate CAC segmentation. Optional decision feedback provides insight into the regions that are contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1687 chest CT scans. The method predicted calcium scores in less than 0.3 s. The intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both the datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of the state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings.
Collapse
|
34
|
Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, Išgum I. State-of-the-Art Deep Learning in Cardiovascular Image Analysis. JACC Cardiovasc Imaging 2019; 12:1549-1565. [DOI: 10.1016/j.jcmg.2019.06.009] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 05/13/2019] [Accepted: 06/13/2019] [Indexed: 02/07/2023]
|
35
|
Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I. A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1588-1598. [PMID: 30507498 DOI: 10.1109/tmi.2018.2883807] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with a coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This paper includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. In these, the centerlines of the coronary arteries were extracted and used to reconstruct multi-planar reformatted (MPR) images for the coronary arteries. To define the reference standard, the presence and the type of plaque in the coronary arteries (no plaque, non-calcified, mixed, calcified), as well as the presence and the anatomical significance of coronary stenosis (no stenosis, non-significant, i.e., <50% luminal narrowing, and significant, i.e., ≥50% luminal narrowing) were manually annotated in the MPR images by identifying the start- and end-points of the segment of the artery affected by the plaque. To perform an automatic analysis, a multi-task recurrent convolutional neural network is applied on coronary artery MPR images. First, a 3D convolutional neural network is utilized to extract features along the coronary artery. Subsequently, the extracted features are aggregated by a recurrent neural network that performs two simultaneous multi-class classification tasks. In the first task, the network detects and characterizes the type of the coronary artery plaque. In the second task, the network detects and determines the anatomical significance of the coronary artery stenosis. The network was trained and tested using the CCTA images of 98 and 65 patients, respectively. For detection and characterization of coronary plaque, the method was achieved an accuracy of 0.77. For detection of stenosis and determination of its anatomical significance, the method was achieved an accuracy of 0.80. The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible. This may enable automated triage of patients to those without coronary plaque and those with coronary plaque and stenosis in need for further cardiovascular workup.
Collapse
|
36
|
Screening for Early Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease (the Big-3) Using Low-dose Chest Computed Tomography. J Thorac Imaging 2019; 34:160-169. [DOI: 10.1097/rti.0000000000000379] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
37
|
Wolterink JM, van Hamersvelt RW, Viergever MA, Leiner T, Išgum I. Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med Image Anal 2019; 51:46-60. [DOI: 10.1016/j.media.2018.10.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 10/05/2018] [Accepted: 10/18/2018] [Indexed: 01/16/2023]
|
38
|
|
39
|
Dumeer S, Einstein AJ. Coronary calcium scoring of CT attenuation correction scans: Automatic, manual, or visual? J Nucl Cardiol 2018; 25:2144-2147. [PMID: 28741065 DOI: 10.1007/s12350-017-0995-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 06/21/2017] [Indexed: 12/19/2022]
Affiliation(s)
- Shifali Dumeer
- Department of Radiology, Columbia University Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Andrew J Einstein
- Department of Medicine, Cardiology Division, and Department of Radiology, Columbia University Medical Center and New York-Presbyterian Hospital, 622 West 168th Street PH 10-203A, New York, NY, 10032, USA.
| |
Collapse
|
40
|
Khalil A, Ng SC, Liew YM, Lai KW. An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment. Cardiol Res Pract 2018; 2018:1437125. [PMID: 30159169 PMCID: PMC6109558 DOI: 10.1155/2018/1437125] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/05/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.
Collapse
Affiliation(s)
- Azira Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Faculty of Science and Technology, Islamic Science University of Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| |
Collapse
|
41
|
Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: working towards fully-automated machine analysis & interpretation. Expert Rev Med Devices 2017; 14:197-212. [PMID: 28277804 DOI: 10.1080/17434440.2017.1300057] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.
Collapse
Affiliation(s)
- Piotr J Slomka
- a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| | - Damini Dey
- b Biomedical Imaging Research Institute , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| | | | - Manish Motwani
- d Cardiovascular Imaging , Manchester Heart Centre, Manchester Royal Infirmary , Manchester , UK
| | - Daniel S Berman
- a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| | - Guido Germano
- a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| |
Collapse
|
42
|
Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis. DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS FOR MEDICAL IMAGE COMPUTING 2017. [DOI: 10.1007/978-3-319-42999-1_2] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
43
|
Durlak F, Wels M, Schwemmer C, Sühling M, Steidl S, Maier A. Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring. MACHINE LEARNING IN MEDICAL IMAGING 2017. [DOI: 10.1007/978-3-319-67389-9_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
44
|
Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal 2016; 34:123-136. [PMID: 27138584 DOI: 10.1016/j.media.2016.04.004] [Citation(s) in RCA: 165] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Revised: 04/07/2016] [Accepted: 04/19/2016] [Indexed: 02/08/2023]
Abstract
The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be used for this purpose. We present a method for automatic CAC quantification in CCTA. This method uses supervised learning to directly identify and quantify CAC without a need for coronary artery extraction commonly used in existing methods. The study included cardiac CT exams of 250 patients for whom both a CCTA and a CSCT scan were available. To restrict the volume-of-interest for analysis, a bounding box around the heart is automatically determined. The bounding box detection algorithm employs a combination of three ConvNets, where each detects the heart in a different orthogonal plane (axial, sagittal, coronal). These ConvNets were trained using 50 cardiac CT exams. In the remaining 200 exams, a reference standard for CAC was defined in CSCT and CCTA. Out of these, 100 CCTA scans were used for training, and the remaining 100 for evaluation of a voxel classification method for CAC identification. The method uses ConvPairs, pairs of convolutional neural networks (ConvNets). The first ConvNet in a pair identifies voxels likely to be CAC, thereby discarding the majority of non-CAC-like voxels such as lung and fatty tissue. The identified CAC-like voxels are further classified by the second ConvNet in the pair, which distinguishes between CAC and CAC-like negatives. Given the different task of each ConvNet, they share their architecture, but not their weights. Input patches are either 2.5D or 3D. The ConvNets are purely convolutional, i.e. no pooling layers are present and fully connected layers are implemented as convolutions, thereby allowing efficient voxel classification. The performance of individual 2.5D and 3D ConvPairs with input sizes of 15 and 25 voxels, as well as the performance of ensembles of these ConvPairs, were evaluated by a comparison with reference annotations in CCTA and CSCT. In all cases, ensembles of ConvPairs outperformed their individual members. The best performing individual ConvPair detected 72% of lesions in the test set, with on average 0.85 false positive (FP) errors per scan. The best performing ensemble combined all ConvPairs and obtained a sensitivity of 71% at 0.48 FP errors per scan. For this ensemble, agreement with the reference mass score in CSCT was excellent (ICC 0.944 [0.918-0.962]). Aditionally, based on the Agatston score in CCTA, this ensemble assigned 83% of patients to the same cardiovascular risk category as reference CSCT. In conclusion, CAC can be accurately automatically identified and quantified in CCTA using the proposed pattern recognition method. This might obviate the need to acquire a dedicated CSCT scan for CAC scoring, which is regularly acquired prior to a CCTA, and thus reduce the CT radiation dose received by patients.
Collapse
Affiliation(s)
- Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, E.01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Bob D de Vos
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Robbert W van Hamersvelt
- Department of Radiology, University Medical Center Utrecht, E.01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| |
Collapse
|