Maximum Wall Thickness and Papillary Muscle Hypertrophy as Complementary Cardiac Biomarkers in Fabry Disease
Schüttler, M.; Witte, J.; Nordbeck, P.; Schindehütte, M.; Ankenbrand, M.
Show abstract
BackgroundFabry disease (O_SCPLOWFDC_SCPLOW) is a rare and severe disease affecting multiple organ systems. However, its non-specific and heterogeneous presentation poses a critical challenge for early diagnosis, often delaying necessary treatment. In re-cent years, imaging-based biomarkers have been increasingly proposed to improve the understanding of O_SCPLOWFDC_SCPLOW and aid its diagnosis. This study presents a comprehensive comparative analysis of several previously proposed imaging-based cardiac biomarkers to assess their potential for diagnostic use. MethodsWe have developed a fully automated image analysis pipeline for quantifying cardiac metrics based on short-axis cine O_SCPLOWCMRC_SCPLOW data available on the UK Biobank. ResultsBased on the UK Biobank cohort, our analyses confirm the diagnostic relevance of the maximum myocardial wall thickness, a metric that mimics the current clinical practice for diagnosing left ventricular hypertrophy. Initial evidence also suggests that the PM/LV ratio, which measures the papillary muscle hypertrophy as the ratio between the areas of the papillary muscles and the left ventricular cavity, has potential prognostic relevance. ConclusionThis study contributes towards a better understanding of the cardiac presentation of FD, which may support future research in improving the diagnostic process. Additionally, our analysis pipeline can serve as a valuable basis for additional data analysis of imaging-based biomarkers for O_SCPLOWFDC_SCPLOW and other diseases.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep Learning-Based Multi-View Echocardiographic Framework for Comprehensive Diagnosis of Pericardial Disease 94%
- Automated Echocardiographic Detection of Mitral Valve Prolapse and Mitral Regurgitation with Video-based Artificial Intelligence Algorithms 94%
- Simple Models Versus Deep Learning in Detecting Low Ejection Fraction From The Electrocardiogram 94%
Similar papers in this journal
- Fairness in Cardiac Magnetic Resonance Imaging: Assessing sex and racial bias in deep learning-based segmentation 97%
- DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics 95%
- Algorithm for Predicting Valvular Heart Disease from Heart Sounds in an Unselected Cohort 94%
Similar papers in this journal
- Reproducibility of 4D Flow MRI-based Personalized Cardiovascular Models; Inter-sequence, Intra-observer, and Inter-observer variability 95%
- Reduced stress perfusion in myocardial infarction with nonobstructive coronary arteries 95%
- Cardiac MRI in common marmosets revealing age-dependency of cardiac function 94%
Similar papers in this journal
- {-}CardiOvascular examination in awake Orangutans (Pongo pygmaeus pygmaeus): Low-stress Echocardiography including Speckle Tracking imaging (the COOLEST method) 94%
- Ventricular anatomical complexity and gender differences impact predictions from computational models 93%
- Development of a Novel Index to Characterise Arterial Dynamics Using Ultrasound Imaging 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.