Machine learning for nuclear cardiology: The way forward
Machine learning for nuclear cardiology: The way forward
Sirish Shrestha 0
Partho P. Sengupta 0
0 Reprint requests: Partho P. Sengupta, MD, DM , WVU Heart & Vascular Institute, West Virginia University , 1 Medical Center Drive, Morgantown, WV 26506-8059 , USA
1 WVU Heart & Vascular Institute, West Virginia University , Morgantown, WV , USA
Coronary artery disease (CAD) is the single most
common cause of death in the developed
world—responsible for about 1 in every 5 deaths.1 The major
challenge in diagnostic and prognosis of the patients
with suspected CAD emanates from the associated
comorbidities and the heterogeneity of clinical
presentations which modifies the performance of commonly
used tests like myocardial perfusion single-photon
emission computed tomography (SPECT).2 This
variance in diagnostic performance myocardial perfusion
SPECT (MPS) can be attributed to a clinician’s
difficulty in interpreting the results and extracting
information contained in multitude of perfusion and
functional parameters. In this regard, machine
learning—a subset of artificial intelligence—can leverage the
knowledge representation and automated reasoning to
detect and extrapolate patterns from the large number of
features. To this end, Alonso et al.,3 in this issue of the
Journal of Nuclear Cardiology, illustrate the benefits of
machine learning techniques for developing a model to
enhance the prognostic value from a complex data of the
MPS, electrocardiogram, and clinical variables while
Machine learning works in iterations and attempts
to learn the data to discern the pattern without much
regard for assumptions. The performance of the
algorithms improves as the data get larger but sacrifices the
interpretability of the features that drives the
performance. Machine learning can be trained as a supervised,
unsupervised, semi-supervised, or reinforcement method
to learn from complex disease representation (Table 1);
the most frequently studied problems in MPS so far have
been supervised. Similarly, Alonso et al. trained an
assortment of supervised machine learning algorithms to
appraise the accuracy and interpretability in contrast to
widely popular logistic regression. In a retrospective
analysis, the authors extracted a total of 122 clinical and
imaging features from 8321 patients (their database
previously reported diverse patient groups including
those with valvular heart disease and non-ischemic
cardiomyopathy, coronary artery bypass grafting and
percutaneous coronary intervention) who underwent
dual-isotope MPS with adenosine stress test. All
machine learning models were evaluated with fivefold
cross-validation for optimization to reduce overfitting.
The machine learning models outperformed the
parametric statistical model in predicting cardiac death.
Support vector machine—a popular classification tool
known for its accuracy and generalizability—performed
the best among the list of algorithms tested.4
Interestingly, however, lasso regression (least absolute
shrinkage and selection operator) modified to shrink
parameters performed better with only 6 features than
the logistic regression with 14 features.
Do the results of the present study mean that our
predilection for statistical methods is fading as we
become more reliant on machine learning? Perhaps, not
categorically; it depends on the purpose of the analysis.
There are overlaps in algorithms and methods in the
orbits of both statistical models and machine learning
algorithms, but the salient characteristic difference must
be realized. Machine learning seeks to learn from the
data without specific programming, whereas statistical
models formalize the relationship between the variables
for inference; they both learn from the data, but the
purpose and approaches are different. Every statistical
model comes with its own set of assumptions that need
to be validated using statistical tools5 and tactics such as
diagnostics, transformation, and model validations to
1 Supervised Most common learning method applied to the database with labeled outcomes or classes. It
involves inferring from labeled training data. Tasks such as regression, classification,
predictive modeling, and survival analysis apply supervised learning. Examples include
logistic regression, SVM, and Neural Networks
2 Unsupervised A learning method where labeled outcomes or classes do not exist. The goal is to observe
similarities and relationships among groups and variables. It is used mostly used in clustering
and dimensionality reduction problems. Examples include hierarchical clustering, k-means
clustering, and principal component analysis
3 Semi- A learning method where the data input has a mixture of labeled and unlabeled outcomes or
supervised classes. It utilizes the data that are not completely classified. Examples include web-page
classification and speech recognition
4 Reinforcement Learning method based on behavioral psychology. The learning agent explores the
environment to maximize a reward. It updates based on the feedback it receives from the
choices it makes until the ‘reward’ criteria are met to handle the decision-making function. It
is currently being used in medical imaging analytics, disease screening, and personalized
determine the quality of the model. Nevertheless,
prediction is not a strong suit of statistics since it makes use
of estimation of parameters. This was well illustrated in
the present investigation where the machine learning
classification algorithm—support vector machine (Area
under the curve, AUC: 0.83) outperformed parametric
regression model—logistic regression (AUC: 0.76).
Several recent studies in Nuclear Cardiology7–13
have attempted to use machine learning in the
identification of perfusion defects and location, prediction of
revascularization and cardiovascular events, and
diagnostic and prognostic accuracy (Table 2). However,
viewing through the lens of these studies, the
investigation by Alonso et al., is singular to apply supervised
machine learning in predicting cardiac death from the
features while attempting to identify interpretable
highperformance model. The investigators additionally
demonstrated an enticing visualization tool to risk
assessment that is manifested by features’ contribution
to the risk and patient’s ranking with respect to the
population. The value of such interpretation
scheme would require to be prospectively evaluated in
While training supervised machine learning model,
the feature space derived from the perfusion
quantification and clinical variables must be supplied in
conjunction with the outcomes. The application of
supervised machine learning requires intense
preparations in collecting, identifying, and classifying the
diseases before the data can be utilized. The outcomes of
interests however need to be provided with an inherent
assumption that features of interest in the data set are
somehow related to the outcome. Unsupervised learning,
on the other hand, is example agnostic but finds patterns
that are otherwise inconceivable. It can distil
high-dimensional data from perfusion and functional
parameters to comport naturally occurring patterns in
the data. It doesn’t require a prior label or annotation of
the outcome for the dataset, rather it learns and identifies
the relations and uncovers the hidden structure.
Therefore, exclusively relying in supervised learning methods
in heterogeneous datasets may not produce stellar
accuracy, inter-observer variability may be introduced,
and bias can creep into the algorithm from
semi-quantitatively examined MPS data. However, in conjunction
with unsupervised learning, it could ameliorate
prediction of the localization and severity of hypo-perfusion
that accounts to heterogeneity of the patients and the
disease. This may be achieved by condensing and
representing the data in a low-dimensional space that is rich
in features in identifying severity in hypo-perfusion, or
by conglomeration of similar patients or features in
clustering, for instance.
Deep learning network is another possibility to
extract information from heterogenous data and predict
cardiac death with very high accuracy, provided there’s
enough data for it to train with.15 It learns in increment
in each hierarchy of the network layers where it extracts
predictive features unlike old generations of machine
learning algorithms. It performs by learning from
examples rather than engineered and hard-coded
features. Interestingly, compared to the performance of
shallow machine learning algorithms that plateaus for
very large dataset, deep learning is scalable and
bgender and images
cgender, body mass index, and images
continues to perform better with larger data. This
capability assisted it to demonstrate its incredible results
in recognizing objects in an image, translating
languages, driving cars, and remarkable potential in
predictive analytics from complex disease
representation. Recently, a new deep learning technique called
automated Transform by manifold approximation
(AUTOMAP) was created which can reconstruct images
from range of modalities such as MRI, CT Scan, PET,
and X-Ray. It can handle imperfections in the raw data
without having the expert to manually tune
parameters.16 This could translate to lower-dose exposure to
patients during perfusion SPECT, or real-time diagnosis
while producing high-quality images. It could augment
our current diagnosis processes; and whether it be from
perfusion SPECT images or a parametric representation
of those images such as polar maps, Fourier
decomposition, or segmental uptake, deep learning can be a
powerful tool to assist clinicians in reconciling data and
build patient-specific treatment decisions while
improving report accuracy and turnaround time.
Undoubtedly, there will always be challenges and
new opportunities, and our first instinct will always be to
understand the clinical implications from new ideas.
Some of us may question the viability of novel methods
while others may embrace it. Some may question the
present study for the value of data provided from a
retrospective cohort while nuclear cardiology has
advanced over the years through novel instrumentation
techniques and cameras, and tracers.17 While there is
little doubt that the generalizability of the model would
require external verification in a more contemporary
data set, it is imperative we recognize that machine
learning is more critical to the evolution of nuclear
imaging than we ever imagined. And at the frontier of
pioneering technologies and rechristening new methods,
one of the most important needs is to develop a set of
guidelines and standards for the academic world and
industry to evaluate ethical, legal and social
implications, and ensure interpretability and reproducibility of
the machine learning models.18,19
Dr. Sengupta is a consultant for HeartSciences, Hitachi
Aloka Ltd., and other authors have nothing to disclose.
1. Cassar A , Holmes DR Jr, Rihal CS , Gersh BJ . Chronic coronary artery disease: Diagnosis and management . Mayo Clin Proc 2009 ; 84 : 1130 - 46 . https://doi.org/10.4065/ mcp . 2009 . 0391 .
2. Box LC , Angiolillo DJ , Suzuki N , Box LA , Jian J , Guzman L , et al. Heterogeneity of atherosclerotic plaque characteristics in human coronary artery disease: A three-dimensional intravascular ultrasound study . Catheter Cardiovasc Interv 2007 ; 70 ( 3 ): 349 - 56 . https://doi.org/10.1002/ccd.21088.
3. Alonso DH , Wernick MN , Yang Y , Germano G , Berman DS , Slmoka P. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning . J Nucl Cardiol 2018 . https://doi.org/10.1007/s12350-017-0924-x.
4. Chao CF , Horng MH . The construction of support vector machine classifier using the firefly algorithm . Comput Intell Neurosci 2015 . https://doi.org/10.1155/ 2015 /212719.
5. Ivanescu AE , Li P , George B , Brown AW , Keith SW , Raju D , et al. The importance of prediction model validation and assessment in obesity and nutrition research . Int J Obes 2016 ; 40 : 887 - 94 . https://doi.org/10.1038/ijo. 2015 . 214 .
6. Shameer K , Johnson KW , Glicksberg BS , Dudley JT , Sengupta PP. Machine learning in cardiovascular medicine: Are we there yet? Heart 2018 . https://doi.org/10.1136/heartjnl-2017-311198.
7. Arsanjani R , Dey D , Khachatryan T , Shalev A , Hayes S , Fish M , et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population . J Nucl Cardiol 2015 ; 22 : 877 - 84 . https://doi.org/10.1007/s12350-014-0027-x.
8. Arsanjani R , Xu Y , Dey D , Vahistha V , Shalev A , Nakanishi R , et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population . J Nucl Cardiol 2013 ; 20 : 553 - 62 . https://doi.org/10. 1007/s12350-013-9706-2.
9. Betancur J , Otaki Y , Motwani M , Fish M , Lemley M , Dey D , et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning . J Am Coll Cardiol Imaging 2017 . https://doi.org/10.1016/j.jcmg. 2017 . 07 .024.
10. Arsanjani R , Xu Y , Dey D , Fish M , Dorbala S , Hayes S , et al. Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease by utilizing a support vector machines algorithm . J Nucl Cardiol 2013 ; 54 : 549 - 55 . https://doi. org/10.2967/jnumed.112.111542.
11. Nakajima K , Kudo T , Nakata T , Kiso K , Kasai T , Taniguchi Y , et al. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: A Japanese multicenter study . Eur J Nucl Med Mol Imaging 2017 ; 44 : 2280 - 9 . https://doi.org/10.1007/s00259-017-3834-x.
12. Betancur J , Rubeaux M , Fuchs TA , Otaki Y , Arnson Y , Slipczuk L , et al. Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: Anatomic and clinical validation . J Nucl Med 2016 ; 58 : 961 - 7 . https://doi.org/10.2967/ jnumed.116.179911.
13. Guner LA , Karabacak NI , Akdemir OU , Karagoz PS , Kocaman SA , Cengel A , et al. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT . J Nucl Cardiol 2010 ; 17 : 405 - 13 . https://doi.org/ 10.1007/s12350-010-9207-5.
14. Intrator O , Intrator N. Interpreting neural-network results: A simulation study . Comput Stat Data Anal 1999 ; 37 : 373 - 93 . https:// doi.org/10.1016/S0167- 9473 ( 01 ) 00016 - 0 .
15. Chen X , Lin X . Big data deep learning: Challenges and perspectives . IEEE Access 2014 ; 2 : 514 - 25 . https://doi.org/10.1109/access. 2014 . 2325029 .
16. Zhu B , Liu JZ , Cauley SF , Rosen BR , Rosen MS . Image reconstruction by domain-transform manifold learning . Nature 2018 ; 555 : 487 - 92 . https://doi.org/10.1038/nature25988.
17. De Lorenzo A. The evolving roles of nuclear cardiology . Curr Cardiol Rev 2009 ; 5 : 52 - 5 . https://doi.org/10.2174/157340309787048112.
18. Wilkinson MD , Dumontier M , Aalbersberg IJ , Appleton G , Axton M , Baak A , et al. The FAIR guiding principles for scientific data management and stewardship . Sci Data 2016 ; 3 : 160018 . https:// doi.org/10.1038/2data. 2016 . 18 .
19. Petrick N , Shahiner B , Armato SG , Bert A , Correale L , Delsanto S . Evaluation of computer-aided detection and diagnosis systems . Med Phys 2013 ; 40 : 087001 . https://doi.org/10.1118/1.4816310.