Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR

A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance

A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.

Outcomes of Modified Mayo Stage IIIa and IIIb Cardiac Light-Chain Amyloidosis: Real-World Experience in Clinical Characteristics and Treatment-67 Patients Multicenter Analysis

Light-chain amyloidosis (AL) is a rare multisystem disorder characterized by the deposition of misfolded amyloid fibrils derived from monoclonal immunoglobulin light chains in various organs. One of the most common organs involved in AL is the heart, with 50-70% of patients clinically symptomatic at diagnosis. We conducted a multi-center, retrospective analysis of 67 patients diagnosed between July 2012 and August 2022 with the European 2012 modification of Mayo 2004 stage III cardiac AL. The most important factors identified in the univariate Cox analysis contributing to a longer OS included Eastern Cooperative Oncology Group performance status (ECOG PS) ≤ 1, New York Heart Association functional classification (NYHA FC) ≤ 2, the use of autologous stem cell transplantation (ASCT) after induction treatment, achieving a hematological response (≥very good partial response) and cardiac (≥partial response) response after first-line treatment. The most important prognostic factors with the most significant impact on OS improvement in patients with modified Mayo stage III cardiac AL identified by multivariate Cox analysis are ECOG PS ≤ 1, NYHA FC ≤ 2, and achieving hematological response ≥ VGPR and cardiac response ≥ PR after first-line treatment.

Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy

Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM.

Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images

The aim of this work was to compare the classification of cardiac MR-images of AL versus ATTR amyloidosis by neural networks and by experienced human readers. Cine-MR images and late gadolinium enhancement (LGE) images of 120 patients were studied (70 AL and 50 TTR). A VGG16 convolutional neural network (CNN) was trained with a 5-fold cross validation process, taking care to strictly distribute images of a given patient in either the training group or the test group. The analysis was performed at the patient level by averaging the predictions obtained for each image. The classification accuracy obtained between AL and ATTR amyloidosis was 0.750 for cine-CNN, 0.611 for Gado-CNN and between 0.617 and 0.675 for human readers. The corresponding AUC of the ROC curve was 0.839 for cine-CNN, 0.679 for gado-CNN (p < 0.004 vs. cine) and 0.714 for the best human reader (p < 0.007 vs. cine). Logistic regression with cine-CNN and gado-CNN, as well as analysis focused on the specific orientation plane, did not change the overall results. We conclude that cine-CNN leads to significantly better discrimination between AL and ATTR amyloidosis as compared to gado-CNN or human readers, but with lower performance than reported in studies where visual diagnosis is easy, and is currently suboptimal for clinical practice.