Impact of Case and Control Selection on Training AI Screening of Cardiac Amyloidosis

Background Recent studies suggest that cardiac amyloidosis (CA) is significantly underdiagnosed. For rare diseases like CA, the optimal selection of cases and controls for artificial intelligence (AI) model training is unknown and can significantly impact model performance. Objectives This study evaluates the performance of ECG waveform-based AI models for CA screening and assesses impact of different criteria for defining cases and controls. Methods Models were trained using different criteria for defining cases and controls including amyloidosis by ICD 9/10 code, cardiac amyloidosis, patients seen in CA clinic). The models were then tested on test cohorts with identical selection criteria as well as population-prevalence cohorts. Results In matched held out test datasets, different model AUCs ranged from 0.660 to 0.898. However, these same algorithms exhibited variable generalizability when tested on a population cohort, with AUCs dropping to 0.467 to 0.880. More stringent case definitions during training result in higher AUCs on the similarly constructed test cohort; however representative population controls matched for age and sex resulted in the best population screening performance. Conclusions AUC in isolation is insufficient to evaluate the performance of a deep learning algorithm, and the evaluation in the most clinically meaningful population is key. Models designed for disease screening are best with matched population controls and performed similarly irrespective of case definitions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### 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: The institutional review boards (IRBs) of Cedars-Sinai Medical Center and Stanford Healthcare approved the study protocol. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 All data produced in the present study are available upon reasonable request to the authors. * (CA) : Cardiac Amyloidosis (AI) : Artificial Intelligence (ECG) : Electrocardiogram (ROC) : Receiver Operating Curve (AUC) : Area Under the Curve (BNP) : Brain Natriuretic Peptide (ICD) : International Classification of Diseases (ATTR) : transthyretin amyloid (AL) : light chain amyloid (LVPW) : Left Ventricular Posterior Wall (IVS) : Interventricular Septum (EHR) : Electronic Health Record