Machine and deep learning models for accurate detection of ischemia and scar with myocardial blood flow positron emission tomography imaging

Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelligence (AI) approach using the results to derive enhanced total perfusion deficit (TPD) and 17-segment summed scores. Methods: We used a cohort of patients undergoing myocardial perfusion imaging within 180 d of invasive coronary angiography. Obstructive CAD was defined as any stenosis of at least 70% or at least 50% in the left main coronary artery. We used per-vessel DL predictions to modulate polar map pixel scores. These transformed polar maps were then used to derive TPD-DL and summed stress score-DL. We compared diagnostic performance using area under the receiver operating characteristic curve (AUC). Results: In the 555 patients held out for testing, the median age was 65 y (interquartile range, 57-73 y), and 381 (69%) were male. Obstructive CAD was present in 329 (59%) patients. The prediction performance for obstructive CAD of stress TPD-DL (AUC, 0.837; 95% CI, 0.804-0.870) was higher than AI prediction alone (AUC, 0.795; 95% CI, 0.758-0.831; P = 0.005) and traditional stress TPD (AUC, 0.737; 95% CI, 0.696-0.778; P < 0.001). Summed stress score-DL had the second highest prediction performance (AUC, 0.822; 95% CI, 0.788-0.857) and higher AUC than traditional quantitative summed stress score (AUC, 0.728; 95% CI, 0.686-0.769; P < 0.001). At a threshold of 5%, the sensitivity and specificity of TPD rose from 72% to 79% and from 62% to 70%, respectively. Conclusion: Integrating AI predictions with traditional quantitative approaches leads to a simplified AI approach, presenting clinicians with familiar measures but operating with higher accuracy than traditional quantitative scoring. This approach may facilitate integration of new AI methods into clinical practice.

Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends

Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.

Current status and future directions in artificial intelligence for nuclear cardiology

INTRODUCTION

Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging tests. Accurate motion correction, image registration, and reconstruction are critical for high-quality imaging, but this can be technically challenging and has traditionally relied on expert manual processing. With accurate processing, there is a rich variety of clinical, stress, functional, and anatomic data that can be integrated to guide patient management.

AREAS COVERED

PubMed and Google Scholar were reviewed for articles related to artificial intelligence in nuclear cardiology published between 2020 and 2024. We will outline the prominent roles for artificial intelligence (AI) solutions to provide motion correction, image registration, and reconstruction. We will review the role for AI in extracting anatomic data for hybrid MPI which is otherwise neglected. Lastly, we will discuss AI methods to integrate the wealth of data to improve disease diagnosis or risk stratification.

EXPERT OPINION

There is growing evidence that AI will transform the performance of MPI by automating and improving on aspects of image acquisition and reconstruction. Physicians and researchers will need to understand the potential strengths of AI in order to benefit from the full clinical utility of MPI.

Machine learning models for positron emission tomography myocardial perfusion imaging

Machine learning has the potential to improve patient care by automating the assessment of medical imaging. Machine learning models have been developed to identify ischaemia and scar on rest and stress myocardial perfusion imaging from positron emission tomography (PET). Application of these tools could aid reporting of PET by highlighting patients and vessels likely to have abnormalities. How this information should be integrated into clinical practice and the impact on patient management or outcomes is not currently known.