Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes

Early-Onset Fetal Growth Restriction Increases Left Ventricular Sphericity in Adolescents Born Very Preterm

Left ventricular shape alterations predict cardiovascular outcomes and have been observed in children born preterm and after fetal growth restriction (FGR). The aim was to investigate whether left ventricular shape is altered in adolescents born very preterm and if FGR has an additive effect. Adolescents born very preterm due to verified early-onset FGR and two control groups with birthweight appropriate for gestational age (AGA), born at similar gestational age and at term, respectively, underwent cardiac MRI. Principal component analysis was applied to find the modes of variation best explaining shape variability for end-diastole, end-systole, and for the combination of both, the latter indicative of function. Seventy adolescents were included (13-16 years; 49% males). Sphericity was increased for preterm FGR versus term AGA for end-diastole (36[0-60] vs - 42[- 82-8]; p = 0.01) and the combined analysis (27[- 23-94] vs - 51[- 119-11]; p = 0.01), as well as for preterm AGA versus term AGA for end-diastole (30[- 56-115] vs - 42[- 82-8]; p = 0.04), for end-systole (57[- 29-89] vs - 30[- 79-34]; p = 0.03), and the combined analysis (44[- 50-145] vs - 51[- 119-11]; p = 0.02). No group differences were observed for left ventricular mass or ejection fraction (all p ≥ 0.33). Sphericity was increased after very preterm birth and exacerbated by early-onset FGR, indicating an additive effect to that of very preterm birth on left ventricular remodeling. Increased sphericity may be a prognostic biomarker of future cardiovascular disease in this cohort that as of yet shows no signs of cardiac dysfunction using standard clinical measurements.

Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy

Aims

Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial fibrillation. Such associations have been studied in relation to total left ventricular hypertrophy, while the regional distribution of myocardial hypertrophy is extremely variable. The clinically significant and genetic associations of such variability require further study.

Methods and results

Here, we use deep learning-derived phenotypes of disproportionate patterns of hypertrophy, namely, apical and septal hypertrophy, to study genome-wide and clinical associations in addition to and independent from total left ventricular mass within 35 268 UK Biobank participants. Using polygenic risk score and Cox regression, we quantified the relationship between incident cardiovascular outcomes and genetically determined phenotypes in the UK Biobank. Adjusting for total left ventricular mass, apical hypertrophy is associated with elevated risk for cardiomyopathy and atrial fibrillation. Cardiomyopathy risk was increased for subjects with increased apical or septal mass, even in the absence of global hypertrophy. We identified 17 genome-wide associations for left ventricular mass, 3 unique associations with increased apical mass, and 3 additional unique associations with increased septal mass. An elevated polygenic risk score for apical mass corresponded with an increased risk of cardiomyopathy and implantable cardioverter-defibrillator implantation.

Conclusion

Apical and septal mass may be driven by genes distinct from total left ventricular mass, suggesting unique genetic profiles for patterns of hypertrophy. Focal hypertrophy confers independent and additive risk to incident cardiovascular disease. Our findings emphasize the significance of characterizing distinct subtypes of left ventricular hypertrophy. Further studies are needed in multi-ethnic cohorts.

Imagenetics for Precision Medicine in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common heart muscle disorder of nonischemic etiology associated with heart failure development and the risk of malignant ventricular arrhythmias and sudden cardiac death. A tailored approach to risk stratification and prevention of sudden cardiac death is required in genetic DCM given its variable presentation and phenotypic severity. Currently, advances in cardiogenetics have shed light on disease mechanisms, the complex genetic architecture of DCM, polygenic contributors to disease susceptibility and the role of environmental triggers. Parallel advances in imaging have also enhanced disease recognition and the identification of the wide spectrum of phenotypes falling under the DCM umbrella. Genotype-phenotype associations have been also established for specific subtypes of DCM, such as DSP (desmoplakin) or FLNC (filamin-C) cardiomyopathy but overall, they remain elusive and not readily identifiable. Also, despite the accumulated knowledge on disease mechanisms, certain aspects remain still unclear, such as which patients with DCM are at risk for disease progression or remission after treatment. Imagenetics, that is, the combination of imaging and genetics, is expected to further advance research in the field and contribute to precision medicine in DCM management and treatment. In the present article, we review the existing literature in the field, summarize the established knowledge and emerging data on the value of genetics and imaging in establishing genotype-phenotype associations in DCM and in clinical decision making for DCM patients.

GANcMRI: Cardiac magnetic resonance video generation and physiologic guidance using latent space prompting

Generative artificial intelligence can be applied to medical imaging on tasks such as privacy-preserving image generation and superresolution and denoising of existing images. Few prior approaches have used cardiac magnetic resonance imaging (cMRI) as a modality given the complexity of videos (the addition of the temporal dimension) as well as the limited scale of publicly available datasets. We introduce GANcMRI, a generative adversarial network that can synthesize cMRI videos with physiological guidance based on latent space prompting. GANcMRI uses a StyleGAN framework to learn the latent space from individual video frames and leverages the timedependent trajectory between end-systolic and end-diastolic frames in the latent space to predict progression and generate motion over time. We proposed various methods for modeling latent time-dependent trajectories and found that our Frame-to-frame approach generates the best motion and video quality. GANcMRI generated high-quality cMRI image frames that are indistinguishable by cardiologists, however, artifacts in video generation allow cardiologists to still recognize the difference between real and generated videos. The generated cMRI videos can be prompted to apply physiologybased adjustments which produces clinically relevant phenotypes recognizable by cardiologists. GANcMRI has many potential applications such as data augmentation, education, anomaly detection, and preoperative planning.