Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology

The Accuracy of Technetium-99 m Pyrophosphate Imaging in Diagnosing Transthyretin Cardiac Amyloidosis and Its Impact on Patient Management

PURPOSE OF REVIEW

This review evaluates recent advancements in Technetium-99 m pyrophosphate (99mTc-PYP) imaging for transthyretin amyloid cardiomyopathy (ATTR-CM). We summarize the advantages of single-photon emission computed tomography (SPECT) over planar imaging, the potential impact of quantitative methods, and emerging data for quantifying response to therapy.

RECENT FINDINGS

The current literature demonstrates the superior diagnostic accuracy of SPECT compared with planar imaging in 99mTc-PYP studies. Emerging quantitative methods, using hybrid SPECT/CT and artificial intelligence, show promise for enhancing diagnostic precision and risk assessment. Recent studies have also highlighted the potential of 99mTc-PYP quantification for monitoring treatment response. 99mTc-PYP SPECT imaging has exceptional diagnostic accuracy for ATTR-CM. Quantitative techniques, which can be facilitated by artificial intelligence, improve risk stratification and may aid in treatment monitoring. Future research should focus on clarifying the clinical role and optimal approach for quantification.

Potential application of ChatGPT in Helicobacter pylori disease relevant queries

Background

Advances in artificial intelligence are gradually transforming various fields, but its applicability among ordinary people is unknown. This study aims to explore the ability of a large language model to address Helicobacter pylori related questions.

Methods

We created several prompts on the basis of guidelines and the clinical concerns of patients. The capacity of ChatGPT on Helicobacter pylori queries was evaluated by experts. Ordinary people assessed the applicability.

Results

The responses to each prompt in ChatGPT-4 were good in terms of response length and repeatability. There was good agreement in each dimension (Fleiss' kappa ranged from 0.302 to 0.690, p < 0.05). The accuracy, completeness, usefulness, comprehension and satisfaction scores of the experts were generally high. Rated usefulness and comprehension among ordinary people were significantly lower than expert, while medical students gave a relatively positive evaluation.

Conclusion

ChatGPT-4 performs well in resolving Helicobacter pylori related questions. Large language models may become an excellent tool for medical students in the future, but still requires further research and validation.

A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning

Background To assess the feasibility of a machine learning (ML) approach using radiomics features of perfusion defects on rest myocardial perfusion imaging (MPI) to detect the presence of hibernating myocardium. Methodology Data of patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for myocardial viability assessment were retrieved. Rest MPI data were processed on ECToolbox, and polar maps were saved using the NFile PMap tool. The reference standard for defining hibernating myocardium was the presence of mismatched perfusion-metabolism defect with impaired myocardial contractility at rest. Perfusion defects on the polar maps were delineated with regions of interest (ROIs) after spatial resampling and intensity discretization. Replicable random sampling allocated 80% (257) of the perfusion defects of the patients from January 2017 to September 2022 to the training set and the remaining 20% (64) to the validation set. An independent dataset of perfusion defects from 29 consecutive patients from October 2022 to January 2023 was used as the testing set for model evaluation. One hundred ten first and second-order texture features were extracted for each ROI. After feature normalization and imputation, 14 best-ranked features were selected using a multistep feature selection process including the Logistic Regression and Fast Correlation-Based Filter. Thirteen supervised ML algorithms were trained with stratified five-fold cross-validation on the training set and validated on the validation set. The ML algorithms with a Log Loss of <0.688 and <0.672 in the cross-validation and validation steps were evaluated on the testing set. Performance matrices of the algorithms assessed included area under the curve (AUC), classification accuracy (CA), F1 score, precision, recall, and specificity. To provide transparency and interpretability, SHapley Additive exPlanations (SHAP) values were assessed and depicted as beeswarm plots. Results Two hundred thirty-nine patients (214 males; mean age 56 ± 11 years) were enrolled in the study. There were 371 perfusion defects (321 in the training and validation sets; 50 in the testing set). Based on the reference standard, 168 perfusion defects had hibernating myocardium (139 in the training and validation sets; 29 in the testing set). On cross-validation, six ML algorithms with Log Loss <0.688 had AUC >0.800. On validation, 10 ML algorithms had a Log Loss value <0.672, among which six had AUC >0.800. On model evaluation of the selected models on the unseen testing set, nine ML models had AUC >0.800 with Gradient Boosting Random Forest (xgboost) [GB RF (xgboost)] achieving the highest AUC of 0.860 and could detect the presence of hibernating myocardium in 21/29 (72.4%) perfusion defects with a precision of 87.5% (21/24), specificity 85.7% (18/21), CA 78.0% (39/50) and F1 Score 0.792. Four models depicted a clear pattern of model interpretability based on the beeswarm SHAP plots. These were GB RF (xgboost), GB (scikit-learn), GB (xgboost), and Random Forest. Conclusion Our study demonstrates the potential of ML in detecting hibernating myocardium using radiomics features extracted from perfusion defects on rest MPI images. This proof-of-concept underscores the notion that radiomics features capture nuanced information beyond what is perceptible to the human eye, offering promising avenues for improved myocardial viability assessment.

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

BACKGROUND

Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.

METHODS

Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.

FINDINGS

Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).

INTERPRETATION

Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.

FUNDING

This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].

Advances in Single-Photon Emission Computed Tomography

The clinical presentation of coronary artery disease (CAD) has changed during the last 20 years with less ischemia on stress testing and more nonobstructive CAD on coronary angiography. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging should include the measurement of myocardial flow reserve and assessment of coronary calcium for the diagnosis of nonobstructive CAD and coronary microvascular disease. SPECT/CT systems provide reliable attenuation correction for better specificity and low-dose CT for coronary calcium evaluation. SPECT MFR measurement is accurate, well validated, and repeatable.

Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data

The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model’s hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging data (geometrical and blood flow based) and non-imaging data. The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data. The innovative aspect of the proposed study is the combination of imaging-based data with the typical CAD risk factors to provide an integrated CAD risk-predictive model.