This white paper introduces an AI-driven ECG quality scoring system designed to automatically assess large volumes of continuous ECG data. Using deep learning models trained on labeled ECG epochs, the tool assigns quality scores that identify usable versus poor-quality data segments. Performance metrics demonstrate high sensitivity and specificity, reducing the need for manual review and accelerating study workflows. The document explains how quality scores can guide ECG extraction, improve data completeness, and support early-phase decision-making. Practical applications are highlighted for Phase I studies and continuous Holter monitoring. The paper positions AI-powered quality assessment as a scalable solution to enhance ECG data reliability and operational efficiency.

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