Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Early signs of myocardial systolic dysfunction in patients with type 2 diabetes are strongly associated with myocardial microvascular dysfunction independent of myocardial fibrosis: a prospective cohort study

BACKGROUND

Patients with diabetes demonstrate early left ventricular systolic dysfunction. Notably reduced global longitudinal strain (GLS) is related to poor outcomes, the underlying pathophysiology is however still not clearly understood. We hypothesized that pathophysiologic changes with microvascular dysfunction and interstitial fibrosis contribute to reduced strain.

METHODS

211 patients with type 2 diabetes and 25 control subjects underwent comprehensive cardiovascular phenotyping by magnetic resonance imaging. Myocardial blood flow (MBF), perfusion reserve (MPR), extracellular volume (ECV), and 3D feature tracking GLS and global circumferential (GCS) and radial strain (GRS) were quantified.

RESULTS

Patients (median age 57 [IQR 50, 67] years, 70% males) had a median diabetes duration of 12 [IQR 6, 18] years. Compared to control subjects GLS, GCS, and GRS were reduced in the total diabetes cohort, and GLS was also reduced in the sub-group of patients without diabetic complications compared to control subjects (controls - 13.9 ± 2.0%, total cohort - 11.6 ± 3.0%; subgroup - 12.3 ± 2.6%, all p < 0.05). Reduced GLS, but not GCS or GRS, was associated with classic diabetes complications of albuminuria (UACR ≥ 30 mg/g) [β (95% CI) 1.09 (0.22-1.96)] and autonomic neuropathy [β (95% CI) 1.43 (0.54-2.31)] but GLS was not associated with retinopathy or peripheral neuropathy. Independently of ECV, a 10% increase in MBF at stress and MPR was associated with higher GLS [multivariable regression adjusted for age, sex, hypertension, smoking, and ECV: MBF stress (β (95% CI) - 0.2 (- 0.3 to - 0.08), MPR (β (95% CI) - 0.5 (- 0.8 to - 0.3), p < 0.001 for both]. A 10% increase in ECV was associated with a decrease in GLS in univariable [β (95% CI) 0.6 (0.2 to 1.1)] and multivariable regression, but this was abolished when adjusted for MPR [multivariable regression adjusted for age, sex, hypertension, smoking, and MPR (β (95% CI) 0.1 (- 0.3 to 0.6)]. On the receiver operating characteristics curve, GLS showed a moderate ability to discriminate a significantly lowered stress MBF (AUC 0.72) and MPR (AUC 0.73).

CONCLUSIONS

Myocardial microvascular dysfunction was independent of ECV, a biomarker of myocardial fibrosis, associated with GLS. Further, 3D GLS could be a potential screening tool for myocardial microvascular dysfunction. Future directions should focus on confirming these results in longitudinal and/or interventional studies.

Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking-a cardiovascular MR study in health and disease

OBJECTIVES

The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (AI)-based contours with manually derived strain values in healthy volunteers and patients with cardiac pathologies.

MATERIALS AND METHODS

A cohort of 136 subjects (60 healthy volunteers and 76 patients; of those including 46 cases with left ventricular hypertrophy (LVH) of varying etiology and 30 cases with chronic myocardial infarction) was analyzed. Comparisons were based on quantitative strain analysis and on a geometric level by the Dice similarity coefficient (DSC) of the segmentations. Strain quantification was performed in 3 long-axis slices and short-axis (SAX) stack with epi- and endocardial contours in end-diastole. AI contours were checked for plausibility and potential errors in the tracking algorithm.

RESULTS

AI-derived strain values overestimated radial strain (+ 1.8 ± 1.7% (mean difference ± standard deviation); p = 0.03) and underestimated circumferential (- 0.8 ± 0.8%; p = 0.02) and longitudinal strain (- 0.1 ± 0.8%; p = 0.54). Pairwise group comparisons revealed no significant differences for global strain. The DSC showed good agreement for healthy volunteers (85.3 ± 10.3% for SAX) and patients (80.8 ± 9.6% for SAX). In 27 cases (27/76; 35.5%), a tracking error was found, predominantly (24/27; 88.9%) in the LVH group and 22 of those (22/27; 81.5%) at the insertion of the papillary muscle in lateral segments.

CONCLUSIONS

Strain analysis based on AI-segmented images shows good results in healthy volunteers and in most of the patient groups. Hypertrophied ventricles remain a challenge for contouring and feature tracking.

CLINICAL RELEVANCE STATEMENT

AI-based segmentations can help to streamline and standardize strain analysis by feature tracking.

KEY POINTS

• Assessment of strain in cardiovascular magnetic resonance by feature tracking can generate global and segmental strain values. • Commercially available artificial intelligence algorithms provide segmentation for strain analysis comparable to manual segmentation. • Hypertrophied ventricles are challenging in regards of strain analysis by feature tracking.

Systematic review and meta-analysis for the value of cardiac magnetic resonance strain to predict cardiac outcomes

Cardiac magnetic resonance (CMR) is the gold standard for the diagnostic classification and risk stratification in most patients with cardiac disorders. The aim of the present study was to investigate the ability of Strain-encoded MR (SENC) for the prediction of major adverse cardiovascular events (MACE). A systematic review and meta-analysis was performed according to the PRISMA Guidelines, including patients with or without cardiovascular disease and asymptomatic individuals. Myocardial strain by HARP were used as pulse sequences in 1.5 T scanners. Published literature in MEDLINE (PubMed) and Cochrane's databases were explored before February 2023 for studies assessing the clinical utility of myocardial strain by Harmonic Phase Magnetic Resonance Imaging (HARP), Strain-encoded MR (SENC) or fast-SENC. In total, 8 clinical trials (4 studies conducted in asymptomatic individuals and 4 in patients with suspected or known cardiac disease) were included in this systematic review, while 3 studies were used for our meta-analysis, based on individual patient level data. Kaplan-Meier analysis and Cox proportional hazard models were used, testing the ability of myocardial strain by HARP and SENC/fast-SENC for the prediction of MACE. Strain enabled risk stratification in asymptomatic individuals, predicting MACE and the development of incident heart failure. Of 1332 patients who underwent clinically indicated CMR, including SENC or fast-SENC acquisitions, 19 patients died, 28 experienced non-fatal infarctions, 52 underwent coronary revascularization and 86 were hospitalized due to heart failure during median 22.4 (17.2-28.5) months of follow-up. SENC/fast-SENC, predicted both all-cause mortality and MACE with high accuracy (HR = 3.0, 95% CI = 1.2-7.6, p = 0.02 and HR = 4.1, 95% CI = 3.0-5.5, respectively, p < 0.001). Using hierarchical Cox-proportional hazard regression models, SENC/fast-SENC exhibited incremental value to clinical data and conventional CMR parameters. Reduced myocardial strain predicts of all-cause mortality and cardiac outcomes in symptomatic patients with a wide range of ischemic or non-ischemic cardiac diseases, whereas in asymptomatic individuals, reduced strain was a precursor of incident heart failure.

Enhancing Arrhythmogenic Right Ventricular Cardiomyopathy Detection and Risk Stratification: Insights from Advanced Echocardiographic Techniques

INTRODUCTION

The echocardiographic diagnosis criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC) are highly specific but sensitivity is low, especially in the early stages of the disease. The role of echocardiographic strain in ARVC has not been fully elucidated, although prior studies suggest that it can improve the detection of subtle functional abnormalities. The purposes of the study were to determine whether these advanced measures of right ventricular (RV) dysfunction on echocardiogram, including RV strain, increase diagnostic value for ARVC disease detection and to evaluate the association of echocardiographic parameters with arrhythmic outcomes.

METHODS

The study included 28 patients from the Heart Institute of São Paulo ARVC cohort with a definite diagnosis of ARVC established according to the 2010 Task Force Criteria. All patients were submitted to ECHO's advanced techniques including RV strain, and the parameters were compared to prior conventional visual ECHO and CMR.

RESULTS

In total, 28 patients were enrolled in order to perform ECHO's advanced techniques. A total of 2/28 (7%) patients died due to a cardiovascular cause, 2/28 (7%) underwent heart transplantation, and 14/28 (50%) patients developed sustained ventricular arrhythmic events. Among ECHO's parameters, RV dilatation, measured by RVDd (p = 0.018) and RVOT PSAX (p = 0.044), was significantly associated with arrhythmic outcomes. RV free wall longitudinal strain < 14.35% in absolute value was associated with arrhythmic outcomes (p = 0.033).

CONCLUSION

Our data suggest that ECHO's advanced techniques improve ARVC detection and that abnormal RV strain can be associated with arrhythmic risk stratification. Further studies are necessary to better demonstrate these findings and contribute to risk stratification in ARVC, in addition to other well-known risk markers.

Inter-study reproducibility of cardiovascular magnetic resonance-derived hemodynamic force assessments

Cardiovascular magnetic resonance (CMR)-derived hemodynamic force (HDF) analyses have been introduced recently enabling more in-depth cardiac function evaluation. Inter-study reproducibility is important for a widespread clinical use but has not been quantified for this novel CMR post-processing tool yet. Serial CMR imaging was performed in 11 healthy participants in a median interval of 63 days (range 49-87). HDF assessment included left ventricular (LV) longitudinal, systolic peak and impulse, systolic/diastolic transition, diastolic deceleration as well as atrial thrust acceleration forces. Inter-study reproducibility and study sample sizes required to demonstrate 10%, 15% or 20% relative changes of HDF measurements were calculated. In addition, intra- and inter-observer analyses were performed. Intra- and inter-observer reproducibility was excellent for all HDF parameters according to intraclass correlation coefficient (ICC) values (> 0.80 for all). Inter-study reproducibility of all HDF parameters was excellent (ICC ≥ 0.80 for all) with systolic parameters showing lower coeffients of variation (CoV) than diastolic measurements (CoV 15.2% for systolic impulse vs. CoV 30.9% for atrial thrust). Calculated sample sizes to detect relative changes ranged from n = 12 for the detection of a 20% relative change in systolic impulse to n = 200 for the detection of 10% relative change in atrial thrust. Overall inter-study reproducibility of CMR-derived HDF assessments was sufficient with systolic HDF measurements showing lower inter-study variation than diastolic HDF analyses.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.