Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population

Abstract

Objective We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms. Methods 1,181 rest 201Tl/stress 99mTc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive coronary angiography (ICA) and suspected coronary artery disease (CAD) and 468 with low likelihood (LLk) of CAD <5%] were considered. Cases with stenosis <70% by ICA and LLk of CAD were considered normal. Total stress perfusion deficit (TPD) for supine/prone data, stress/rest perfusion change, and transient ischemic dilatation were derived by automated perfusion quantification software and were combined with age, sex, and post-electrocardiogram CAD probability by a boosted ensemble ML algorithm (Logit-Boost). The diagnostic accuracy of the model for prediction of obstructive CAD ≥70% was compared to standard prone/supine quantification and to visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. Tenfold stratified cross-validation was performed. Results The diagnostic accuracy of ML (87.3% ± 2.1%) was similar to Expert 1 (86.0% ± 2.1%), but superior to combined supine/prone TPD (82.8% ± 2.2%) and Expert 2 (82.1% ± 2.2%) (P <.01). The receiver operator characteristic areas under curve for ML algorithm (0.94 ± 0.01) were higher than those for TPD and both visual readers (P <.001). The sensitivity of ML algorithm (78.9% ± 4.2%) was similar to TPD (75.6% ± 4.4%) and Expert 1 (76.3% ± 4.3%), but higher than that of Expert 2 (71.1% ± 4.6%), (P <.01). The specificity of ML algorithm (92.1% ± 2.2%) was similar to Expert 1 (91.4% ± 2.2%) and Expert 2 (88.3% ± 2.5%), but higher than TPD (86.8% ± 2.6%), (P <.01). Conclusion ML significantly improves diagnostic performance of MPS by computational integration of quantitative perfusion and clinical data to the level rivaling expert analysis.

Artificial Intelligence and Machine Learning in Cardiology - A Change of Paradigm

“…Garcia et al, 9 in an excellent review on the subject, point out that clinical decision support and artificial intelligence systems serve as warnings for the cognitive bias of clinicians and reduce intra and interobserver variability, allowing to interpret the exams faster and with greater accuracy, as observed in studies in which the diagnostic interpretation of the examination by the computer is similar to that of the experts. 10 We generally agree with this view and consider that the support to make the clinical decision and the improvement of diagnostic and prognostic performances should be encouraged and supported by physicians of the various specialties. The frequent concern about the eventual replacement of the physician by the machines is not substantiated by the facts.…”

ASNC imaging guidelines for nuclear cardiology procedures

“…53,54 Unlike multivariate regression modeling, machine learning algorithms are not fitted models, and thus are not affected by collinearity between variables. Furthermore, they can be improved in an ongoing basis incorporating accumulative observations after clinical implementation.…”

“…The registry contains hundreds of data elements such as referral information, demographics, clinical data, stress data, ECG data, imaging parameters, radiation dosing, perfusion, quantification, left ventricular function parameters, study quality, and signature date/time. 54 Data elements in structured reporting applications within commercially available nuclear cardiology analysis packages are fully homogenized with the ImageGuide TM . Thus, data from each study can be easily submitted from the laboratory to the ImageGuide TM Registry, which in turn tracks and publicly reports, in real-time, indicators of excellence in radionuclide imaging, including crucial reporting measures.…”

“…Thus, data from each study can be easily submitted from the laboratory to the ImageGuide TM Registry, which in turn tracks and publicly reports, in real-time, indicators of excellence in radionuclide imaging, including crucial reporting measures. 16,54,55 Such integration provides a constant quality improvement feedback loop for ever-improving report quality and patient care. 57 The ImageGuide TM Registry is a Qualified Clinical Data Registry (QCDR) through which participating physicians can receive CMS reimbursement credits for participating in a Physician Quality Reporting System (PQRS).…”

Normal limits for transient ischemic dilation with 99mTc myocardial perfusion SPECT protocols

“…This final computed probability can be then discretized to several categories of risk or probability of disease, rather than just assigning a normal/abnormal finding. Indeed, such holistic approach has been shown to be very promising in diagnostic 14 and prognostic 15,16 applications demonstrating a significant overall improvement of diagnostic accuracy or risk reclassification. …”