Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging

Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials

Cardiovascular disease (CVD) is a leading cause of death, accounting for over 30% of annual global fatalities. Ischemic heart disease, in turn, is a frontrunner of worldwide CVD mortality. With the burden of coronary disease rapidly growing, understanding the nuances of cardiac imaging and risk prognostication becomes paramount. Myocardial perfusion imaging (MPI) is a frequently utilized and well established testing modality due to its significant clinical impact in disease diagnosis and risk assessment. Recently, nuclear cardiology has witnessed major advancements, driven by innovations in novel imaging technologies and improved understanding of cardiovascular pathophysiology. Applications of artificial intelligence (AI) to MPI have enhanced diagnostic accuracy, risk stratification, and therapeutic decision-making in patients with coronary artery disease (CAD). AI techniques such as machine learning (ML) and deep learning (DL) neural networks offer new interpretations of immense data fields, acquired through cardiovascular imaging modalities such as nuclear medicine (NM). Recently, AI algorithms have been employed to enhance image reconstruction, reduce noise, and assist in the interpretation of complex datasets. The rise of AI in nuclear medicine (AI-NM) has proven itself groundbreaking in the efficiency of image acquisition, post-processing time, diagnostic ability, consistency, and even in risk-stratification and outcome prognostication. To that end, this narrative review will explore these latest advances in AI in nuclear medicine and its rapid transformation of the cardiac diagnostics landscape. This paper will examine the evolution of AI-NM, review novel AI techniques and applications in nuclear cardiac imaging, summarize recent AI-NM clinical trials, and explore the technical and clinical challenges in its implementation of artificial intelligence.

Advancements in artificial intelligence-driven techniques for interventional cardiology

This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

Stress-only versus rest-stress SPECT MPI in the detection and diagnosis of myocardial ischemia and infarction by machine learning

BACKGROUND

Rest-stress SPECT myocardial perfusion imaging (MPI) is widely used to evaluate coronary artery disease (CAD). We aim to evaluate stress-only versus rest-stress MPI in diagnosing CAD by machine learning (ML).

METHODS

A total of 276 patients with suspected CAD were randomly divided into training (184 patients) and validation (92 patients) cohorts. Variables extracted from clinical, physiological, and rest-stress SPECT MPI were screened. Stress-only and rest-stress MPI using ML were established and compared using the training cohort. Then the diagnostic performance of two models in diagnosing myocardial ischemia and infarction was evaluated in the validation cohort.

RESULTS

Six ML models based on stress-only MPI selected summed stress score, summed wall thickness score of stress%, and end-diastolic volume of stress as key variables and performed equally good as rest-stress MPI in detecting CAD [area under the curve (AUC): 0.863 versus 0.877, P  = 0.519]. Furthermore, stress-only MPI showed a reasonable prediction of reversible deficit, as shown by rest-stress MPI (AUC: 0.861). Subsequently, nomogram models using the above-stated stress-only MPI variables showed a good prediction of CAD and reversible perfusion deficit in training and validation cohorts.

CONCLUSION

Stress-only MPI demonstrated similar diagnostic performance compared with rest-stress MPI using 6 ML algorithms. Stress-only MPI with ML models can diagnose CAD and predict ischemia from scar.

Quantitation of diffuse myocardial ischemia with mental stress and its association with cardiovascular events in individuals with recent myocardial infarction

Microcirculatory dysfunction during psychological stress may lead to diffuse myocardial ischemia. We developed a novel quantification method for diffuse ischemia during mental stress (dMSI) and examined its relationship with outcomes after a myocardial infarction (MI). We studied 300 patients ≤ 61 years of age (50% women) with a recent MI. Patients underwent myocardial perfusion imaging with mental stress and were followed for 5 years. dMSI was quantified from cumulative count distributions of rest and stress perfusion. Focal ischemia was defined in a conventional fashion. The main outcome was a composite outcome of recurrent MI, heart failure hospitalizations, and cardiovascular death. A dMSI increment of 1 standard deviation was associated with a 40% higher risk for adverse events (HR 1.4, 95% CI 1.2-1.5). Results were similar after adjustment for viability, demographic and clinical factors and focal ischemia. In sex-specific analysis, higher levels of dMSI (per standard deviation increment) were associated with 53% higher risk of adverse events in women (HR 1.5, 95% CI 1.2-2.0) but not in men (HR 0.9, 95% CI 0.5-1.4), P 0.001. A novel index of diffuse ischemia with mental stress was associated with recurrent events in women but not in men after MI.

Integrating artificial intelligence and natural language processing for computer-assisted reporting and report understanding in nuclear cardiology

Natural language processing (NLP) offers many opportunities in Nuclear Cardiology. These opportunities include applications in converting nuclear cardiology imaging reports to digital searchable information that may be used as Big Data for machine learning and registries. Another major NLP application is, with the support of AI, in automatically translating MPI image features directly into nuclear cardiology reports. This review describes the symbiotic relationship between AI and NLP in that NLP is being used to facilitate AI applications and, AI techniques are being used to facilitate NLP. This article reviews the fundamentals of NLP and describes various conventional and AI techniques that have been applied in imaging. Key nuclear cardiology applications are reviewed such as conversion of MPI free-text reports to digital documents as well as direct conversion of MPI images into structured medical reports.

Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology

A major opportunity in nuclear cardiology is the many significant artificial intelligence (AI) applications that have recently been reported. These developments include using deep learning (DL) for reducing the needed injected dose and acquisition time in perfusion acquisitions also due to DL improvements in image reconstruction and filtering, SPECT attenuation correction using DL without need for transmission images, DL and machine learning (ML) use for feature extraction to define myocardial left ventricular (LV) borders for functional measurements and improved detection of the LV valve plane and AI, ML, and DL implementations for MPI diagnosis, prognosis, and structured reporting. Although some have, most of these applications have yet to make it to widespread commercial distribution due to the recency of their developments, most reported in 2020. We must be prepared both technically and socio-economically to fully benefit from these and a tsunami of other AI applications that are coming.

Indicators of abnormal PET coronary flow capacity in detecting cardiac ischemia

Coronary flow capacity (CFC) categorizes severity of left ventricular (LV) ischemia by PET myocardial blood flow (MBF). Our objective was to correlate abnormal CFC with other indicators of regional ischemia. Data were examined retrospectively for 231 patients evaluated for known/suspected CAD who underwent rest and regadenoson-stress ⁸²Rb PET/CT. MBF and myocardial flow reserve (MFR) were quantified, from which CFC was categorized as Normal CFC (1), Minimally reduced (2), Mildly reduced (3), Moderately reduced (4), and Severely reduced (5) for the three main arterial territories as well as globally. Relative perfusion summed stress score (SSS) and systolic phase contraction bandwidth (BW) were assessed. Accuracy to detect arteries with CFC ≥ 4 was highest for a Regional Index combining SSS and BW (88 ± 3%). A Global Index formed from stress ejection fraction, SSS and BW was the most accurate means of identifying patients with global CFC ≥ 4 (84 ± 3%). Arteries with abnormal CFC derived from absolute myocardial blood flow measurements are accurately identified by composite parameters combining regionally aberrant relative perfusion patterns and asynchrony.

"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

BACKGROUND

Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.

METHODS

A trained convolutional neural network (CNN) was used for cardiac segmentation in 18F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans.

RESULTS

Mean (± SD) values with manual vs. CNN-based segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P = .21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P = .26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P = .34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators.

CONCLUSION

The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.

Clinical implications of compromised 82Rb PET data acquisition

BACKGROUND

We wished to document the prevalence and quantitative effects of compromised 82Rb PET data acquisitions on myocardial flow reserve (MFR).

METHODS AND RESULTS

Data were analyzed retrospectively for 246 rest and regadenoson-stress studies of 123 patients evaluated for known or suspected CAD. An automated injector delivered pre-determined activities of 82Rb. Automated quality assurance algorithms identified technical problems for 7% (9/123) of patients. Stress data exhibited 2 instances of scanner saturation, 1 blood peak detection, 1 blood peak width, 1 gradual patient motion, and 2 abrupt patient motion problems. Rest data showed 1 instance of blood peak width and 2 abrupt patient motion problems. MFR was lower for patients with technical problems flagged by the quality assurance algorithms than those without technical problems (1.5 ± 0.5 versus 2.1 ± 0.7, P = 0.01), even though rest and stress ejection fraction, asynchrony and relative myocardial perfusion measures were similar for these two groups (P > 0.05), suggesting that MFR accuracy was adversely affected by technical errors.

CONCLUSION

It is important to verify integrity of 82Rb data to ensure MFR computation quality.

Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology

The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.

The evolving role of artificial intelligence in cardiac image analysis

Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.

Early onset of left ventricular regional asynchrony in arteries with sub-clinical stenosis

BACKGROUND

Asynchrony has been reported to be a marker of ischemic-induced left ventricular dysfunction, the magnitude of which correlates with extent of epicardial coronary disease. We wished to determine whether normal-appearing arterial territories with mild degrees of asynchrony have lower 82Rb PET absolute myocardial blood flow (MBF) and/or lower myocardial flow reserve (MFR).

METHODS AND RESULTS

Data were examined retrospectively for 105 patients evaluated for known/suspected CAD who underwent rest/regadenoson-stress 82Rb PET/CT and quantitative coronary angiography. Rest and stress absolute MBF and MFR were quantified from first-pass 82Rb PET curves. Regional relative myocardial perfusion summed stress score (SSS), summed rest score (SRS), regional phase bandwidth (BW), and regional semi-quantitative asynchrony visual scores of (Asynch) were assessed. We found that in apparently normal arteries (SSS < 4, SRS < 4 and stenosis < 70%), those with abnormally low MFR < 2.0 compared to those with MFR ≥ 2.0 had larger phase BW (186 ± 79° vs 158 ± 67°, P = .02), and more visually apparent Asynch (5.7 ± 4.2 vs 3.9 ± 3.6, P = .02), which was associated with increasing stenosis values (ρ = 0.44, P < .0001).

CONCLUSION

A subgroup of coronary territories with normal relative perfusion and normal or non-obstructive coronary disease may have reduced MFR, which is signaled physiologically by a mild degree of left ventricular asynchrony.

A review of the application of machine learning in molecular imaging

Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications

Quantitative analysis has been applied extensively to image processing and interpretation in nuclear cardiology to improve disease diagnosis and risk stratification. This is Part 2 of a two-part continuing medical education article, which will review the potential clinical role for emerging quantitative analysis tools. The article will describe advanced methods for quantifying dyssynchrony, ventricular function and perfusion, and hybrid imaging analysis. This article discusses evolving methods to measure myocardial blood flow with positron emission tomography and single-photon emission computed tomography. Novel quantitative assessments of myocardial viability, microcalcification and in patients with cardiac sarcoidosis and cardiac amyloidosis will also be described. Lastly, we will review the potential role for artificial intelligence to improve image analysis, disease diagnosis, and risk prediction. The potential clinical role for all these novel techniques will be highlighted as well as methods to optimize their implementation.

Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications

Quantitative analysis has been applied extensively to image processing and interpretation in nuclear cardiology to improve disease diagnosis and risk stratification. This is Part 2 of a two-part continuing medical education article, which will review the potential clinical role for emerging quantitative analysis tools. The article will describe advanced methods for quantifying dyssynchrony, ventricular function and perfusion, and hybrid imaging analysis. This article discusses evolving methods to measure myocardial blood flow with positron emission tomography and single-photon emission computed tomography. Novel quantitative assessments of myocardial viability, microcalcification and in patients with cardiac sarcoidosis and cardiac amyloidosis will also be described. Lastly, we will review the potential role for artificial intelligence to improve image analysis, disease diagnosis, and risk prediction. The potential clinical role for all these novel techniques will be highlighted as well as methods to optimize their implementation. (J Nucl Cardiol 2020).

Leveraging latest computer science tools to advance nuclear cardiology

Nuclear cardiology has unique advantages compared to other modalities, since the image analysis is already much more automated compared to what is currently clinically performed for CT, MR, or echocardiography imaging. The diverse image and clinical data available to assess coronary disease function, perfusion, flow, and associated CT data provide new opportunities, but logistically these additional assessments increase the overall complexity of SPECT/PET reporting, necessitating additional expertise and time. The advances in artificial intelligence software can be leveraged to obtain comprehensive risk predictions and diagnoses from all available data. They will allow nuclear cardiology to retain competitive edge compared to other modalities and improve its overall clinical utility. These tools will enhance diagnosis and risk prediction beyond what is possible by subjective visual analysis and mental integration of data by physicians.

Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?

Radiomics, machine learning, and, more generally, artificial intelligence (AI) provide unique tools to improve the performances of nuclear medicine in all aspects. They may help rationalise the operational organisation of imaging departments, optimise resource allocations, and improve image quality while decreasing radiation exposure and maintaining qualitative accuracy. There is already convincing data that show AI detection, and interpretation algorithms can perform with equal or higher diagnostic accuracy in various specific indications than experts in the field. Preliminary data strongly suggest that AI will be able to process imaging data and information well beyond what is visible to the human eye, and it will be able to integrate features to provide signatures that may further drive personalised medicine. As exciting as these prospects are, they currently remain essentially projects with a long way to go before full validation and routine clinical implementation. AI uses a language that is totally unfamiliar to nuclear medicine physicians, who have not been trained to manage the highly complex concepts that rely primarily on mathematics, computer sciences, and engineering. Nuclear medicine physicians are mostly familiar with biology, pharmacology, and physics, yet, considering the disruptive nature of AI in medicine, we need to start acquiring the knowledge that will keep us in the position of being actors and not merely witnesses of the wonders developed by other stakeholders in front of our incredulous eyes. This will allow us to remain a useful and valid interface between the image, the data, and the patients and free us to pursue other, one might say nobler tasks, such as treating, caring and communicating with our patients or conducting research and development.