Human intelligence, especially visual perception, has always inspired Alaa. The innovation draws from the field of pre-attentive processing theory in human vision, showing through several studies that using pseudo-color to expose QT-interval duration on the ECG significantly improves laypeople’s accuracy in detecting LQTS at risk of sudden death. An understanding of how humans perceive pseudo-color to interpret ECG data was combined with clinical knowledge to develop a novel, human-like AI that reliably automates the detection of LQTS, thus facilitating an intuitively explainable, shared human-machine ECG interpretation.

Sudden cardiac death accounts for 15-20% of all deaths worldwide annually. This innovation takes a completely new approach to ECG interpretation, allowing people to monitor LQTS at risk of sudden death at home, potentially saving thousands of lives every year.