The application of artificial intelligence in nuclear cardiology

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

OBJECTIVES

To investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).

MATERIALS AND METHODS

This retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances.

RESULTS

Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841-0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm.

CONCLUSION

ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

2022 Artificial intelligence primer for the nuclear cardiologist

Driven by advances in computing power, the past decade has seen rapid developments in artificial intelligence (AI) which now offers potential enhancements to every aspect of nuclear cardiology workflow including acquisition, reconstruction, segmentation, direct image analysis, and interpretation; as well as facilitating clinical and imaging big-data integration for superior personalized risk stratification. To understand the relevance and potential of AI in their field, this review provides a primer for nuclear cardiologists in 2022. The aim is to explain terminology and provide a summary of key current implementations, challenges, and future aspirations of AI-based enhancements to nuclear cardiology.

From FDG and beyond: the evolving potential of nuclear medicine

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

J-ACCESS investigation and nuclear cardiology in Japan: implications for heart failure

While coronary heart disease remains a global cause of mortality, the prevalence of heart failure (HF) is increasing in developed countries including Japan. The continuously increasing aging population and the relatively low incidence of ischemic origins are features of the HF background in Japan. Information about nuclear cardiology practice and prognosis has accumulated, thanks to the multicenter prognostic J-ACCESS investigations (Series 1‒4) over two decades in Japan. Although the rate of hard cardiac events is lower in Japan than in the USA and Europe, similar predictors have been identified as causes of major adverse cardiac events. The highest proportion (50-75%) of major events among patients indicated for nuclear cardiology examinations in the J-ACCESS registries is severe HF requiring hospitalization. Therefore, the background and the possible reasons for the higher proportion of severe HF events in Japan require clarification. Combinations of age, myocardial perfusion defects, left ventricular dysfunction, and comorbid diabetes and chronic kidney disease are major predictors of cardiovascular events including severe HF. Although the Japanese Circulation Society has updated its clinical guidelines to incorporate non-invasive imaging modalities for diagnosing chronic coronary artery disease, the importance of risk-based approaches to optimal medical therapy and coronary revascularization is emphasized herein.

Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction

The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS).

Methods

A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods.

Results

ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.

Statistical methods for validation of predictive models

Predictive models are widely used in clinical practice. Despite of the increasing number of published AI systems, recent systematic reviews have identified lack of statistical rigor in the development and validation of predictive models. This work reviewed the current literature for predictive performance measures and resampling methods. Furthermore, common pitfalls were discussed.

Nuclear Cardiology Data Analyzed Using Machine Learning

Machine learning has become popular in clinical practice, and the amount of research that uses artificial intelligence is rapidly increasing. In contrast to conventional statistical and rule-based methods, machine learning creates algorithms based only on combinations of input and output databases. Basic understanding of the internal workings of artificial intelligence, its structures and need for appropriate databases, as well as its strengths and weaknesses is important for efficient machine learning application. The cardiological applications of machine learning include diagnosing coronary artery diseases and heart failure, and examples are addressed herein. A preliminary application of machine learning to a ¹²³I-metaiodobenzylguanidine-based risk model appears promising, and further studies using similar approaches are anticipated. Nuclear medicine physicians and cardiologists should play key roles in developing machine learning-based methods to ensure practical and reliable decisions.

Diagnosis of Parkinson syndrome and Lewy-body disease using 123I-ioflupane images and a model with image features based on machine learning

OBJECTIVES

¹²³I-ioflupane has been clinically applied to dopamine transporter imaging and visual interpretation assisted by region-of-interest (ROI)-based parameters. We aimed to build a multivariable model incorporating machine learning (ML) that could accurately differentiate abnormal profiles on ¹²³I-ioflupane images and diagnose Parkinson syndrome or disease and dementia with Lewy bodies (PS/PD/DLB).

METHODS

We assessed ¹²³I-ioflupane images from 239 patients with suspected neurodegenerative diseases or dementia and classified them as having PS/PD/DLB or non-PS/PD/DLB. The image features of high or low uptake (F1), symmetry or asymmetry (F2), and comma- or dot-like patterns of caudate and putamen uptake (F3) were analyzed on 137 images from one hospital for training. Direct judgement of normal or abnormal profiles (F4) was also examined. Machine learning methods included logistic regression (LR), k-nearest neighbors (kNNs), and gradient boosted trees (GBTs) that were assessed using fourfold cross-validation. We generated the following multivariable models for the test database (n = 102 from another hospital): Model 1, ROI-based measurements of specific binding ratios and asymmetry indices; Model 2, ML-based judgement of abnormalities (F4); and Model 3, features F1, F2 and F3, plus patient age. Diagnostic accuracy was compared using areas under receiver-operating characteristics curves (AUC).

RESULTS

The AUC was high with all ML methods (0.92-0.96) for high or low uptake. The AUC was the highest for symmetry or asymmetry with the kNN method (AUC 0.75) and the comma-dot feature with the GBT method (AUC 0.94). Based on the test data set, the diagnostic accuracy for a diagnosis of PS/PD/DLB was 0.86 ± 0.04 (SE), 0.87 ± 0.04, and 0.93 ± 0.02 for Models 1, 2 and 3, respectively. The AUC was optimal for Model 3, and significantly differed between Models 3 and 1 (p = 0.027), and 3 and 2 (p = 0.029).

CONCLUSIONS

Image features such as high or low uptake, symmetry or asymmetry, and comma- or dot-like profiles can be determined using ML. The diagnostic accuracy of differentiating PS/PD/DLB was the highest for the multivariate model with three features and age compared with the conventional ROI-based method.