Artificial Intelligence-Derived Extracellular Volume Fraction for Diagnosis and Prognostication in Patients with Light-Chain Cardiac Amyloidosis

Aims: T1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL–CA). We conducted this study to evaluate the performance of T1 mapping parameters for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL–CA, using a semi-automated deep learning algorithm. Methods and Results: A total of 300 patients who underwent CMR for differential diagnosis of LVH were analyzed. CA was confirmed in 50 patients (39 with AL–CA and 11 with transthyretin amyloidosis), hypertrophic cardiomyopathy in 198, hypertensive heart disease in 47, and Fabry disease in 5. A semi-automated deep learning algorithm (Myomics–Q) was used for the analysis of the CMR images. The optimal cutoff extracellular volume fraction (ECV) for the differentiation of CA from other etiologies was 33.6% (diagnostic accuracy 85.6%). he artificial intelligence (AI)–derived ECV showed a significant prognostic value for a composite of cardiovascular death and heart failure hospitalization in patients with AL-CA (revised Mayo stage III or IV) (adjusted hazard ratio 4.247 for ECV ≥40%, 95% confidence interval 1.215–14.851, p–value=0.024). Incorporation of AI–derived ECV into the revised Mayo staging system resulted in better risk stratification (integrated discrimination index 27.9%, p=0.013; net reclassification index 13.8%, p=0.007). Conclusions: AI–assisted T1 mapping on CMR imaging allows for improved diagnosis of CA from other etiologies of LVH. Furthermore, AI-derived ECV has significant prognostic value in patients with AL–CA, suggesting its clinical usefulness. ### Competing Interest Statement Conflict of interest: Pan Ki Kim and Byoung Wook Choi are founders of Phantomics, Inc. (Seoul, Korea), the company that supports the software used in this study. The other authors have no conflicts of interest to declare. ### Funding Statement This work was supported by the Medical AI Clinic Program through the National IT Industry Promotion Agency (NIPA), funded by the Ministry of Science and ICT (MSIT) of the Republic of Korea, and with a grant from SNUBH (grant number: 06-2020-0130). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional Review Board of Seoul National University Bundang Hospital gave ethical approval for this study (IRB No. B-2208-773-108). Institutional Review Board of Seoul National University Bundang Hospital waived the requirement for informed consent owing to the retrospective nature of the study and the minimal expected risk to the patients. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data used in this study cannot be made publicly available because of the strict ethical restrictions set by the Institutional Review Board (IRB) of Seoul National University Bundang Hospital (https://e-irb.snubh.org). Please contact the corresponding authors (inchang.hwang@gmail.com or humandr@snubh.org) or the ethics board at Seoul National University Bundang Hospital (snubhirb@gmail.com) for further inquiries regarding data availability within the scope permitted by the IRB.