Cardiac imaging: working towards fully-automated machine analysis & interpretation

Abstract

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.

Keywords: Artificial intelligence; cardiac imaging; deep learning; image segmentation; machine learning.

Figure 1

Inter-observer variability in nuclear cardiology.

Figure 2

Diagnostic accuracy of automated versus visual analysis. A recent study confirmed that diagnostic accuracy for detecting CAD (≥70% stenosis) on a per-patient basis using automated methods is at least similar or marginally superior to that achieved by two expert visual readers. Comparisons were made for both attenuation-corrected (AC) and non-attenuation corrected (NC) data, and using variable amounts of imaging and clinical data available to the reader (V1-V4) with V4 representing full imaging and clinical information available. This research was originally published in JNM. Arsanjani R, Xu Y, Hayes SW, et al. Comparison of Fully Automated Computer Analysis and Visual Scoring for Detection of Coronary Artery Disease from Myocardial Perfusion SPECT in a Large Population. J Nucl Med. 2013;54:221–228. © by the Society of Nuclear Medicine and Molecular Imaging, Inc [11].

Figure 3

Standardized quantification of high-risk plaque lesion in the left anterior descending artery. Full color available online. The quantification allows standardized measurement of several parameters such as maximal stenosis, volumes of non-calcified (red) and calcified (yellow) plaques, total plaque volume, plaque composition, length of the lesion and drop of CT contrast.

Figure 4

Traditional machine learning vs. deep learning. In typical machine learning (a) the pre-processing step extracts relevant features of the data (images) and then perform machine learning taking as an input extracted features. In deep learning (b) the deep network defines features as a part of learning process. The input to DL are raw data (images).

Figure 5

Automatic identification of coronary lesions from coronary CT Angiography by an algorithm based on machine learning. An example of lumen segmentation with lesion detection. This research was originally published in Journal of Medical Imaging. Kang D, Dey D, Slomka PJ, et al. Structured Learning Algorithm for Detection of Nonobstructive and Obstructive Coronary Plaque Lesions from Coronary CT Angiography. 2(1), 014003 (Mar 06 2015) [98].

Figure 6

Application of machine learning to automated quantitation. When clinical and imaging information was provided to the LogitBoost machine learning technique in a large study (n = 1181), it achieved a significantly higher diagnostic accuracy for detection of significant CAD (87%) than one of the expert readers (82%) or TPD (83%; P < 0.01); and a higher AUC (0.94 ± 0.01) than TPD (0.88 ± 0.01) or 2 visual readers (0.89,0.85; P < 0.001). With kind permission from Springer Science+Business Media: Journal of Nuclear Cardiology, Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population, Volume 20, 2013, Page 558, Arsanjani R, Xu Y, Dey D, et al., Figure 3, © by the American Society of Nuclear Cardiology [111].

Figure 7

Nuclear cardiology prediction of revascularization after the scan. Automated measures have similar area under the ROC curve as one experienced reader and much higher than second reader in N = 713 patients. With kind permission from Springer Science+Business Media: Journal of Nuclear Cardiology, Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population, Volume 22, 2015, Page 882, Arsanjani R, Dey D, Khachatryan T, et al., Figure 2, © by the American Society of Nuclear Cardiology [112].

Figure 8

5-year all-cause prediction of mortality. Machine learning boosting model (ML-B) was able to predict mortality much better than the existing imaging scores derived by visual expert analysis (Summed segmental scores-SSS, Segment Involvement scores-SIS) or clinical scores such Duke index (DI) or Framingham Risk Score (FRS). Reproduced, with permission, from Manish Motwani et al. Machine Learning For Prediction Of All-Cause Mortality In Patients With Suspected Coronary Artery Disease: A 5-Year Multicentre Prospective Registry Analysis. European Heart Journal (2016) DOI: http://dx.doi.org/10.1093/eurheartj/ehw188. Published by Oxford University Press on behalf of the European Society of Cardiology [113].