Impact of Case and Control Selection on Training Artificial Intelligence Screening of Cardiac Amyloidosis.

ABSTRACT:

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 model training is unknown and can significantly impact model performance.

Objectives

This study evaluates the performance of electrocardiogram (ECG) waveform-based artificial intelligence models for CA screening and assesses impact of different criteria for defining cases and controls.

Methods

Using a primary cohort of ∼1.3 million ECGs from 341,989 patients, models were trained using different case and control definitions. Case definitions included ECGs from patients with an amyloidosis diagnosis by International Classification of Diseases-9/10 code, patients with CA, and pa

SUBMITTER: Vrudhula A 

PROVIDER: S-EPMC11450940 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature