Wednesday, October 27, 2021
The quest for a practical AI solution for automated ECG diagnostic is motivated by desire to reduce the human and financial resources required for patient monitoring and to enable more ubiquitous remote outpatient monitoring. Today, Deep Neural Networks (DNN) are considered the building blocks of all AI solutions. Yet, DNNs are not widely adopted in hospitals for automatic diagnostic for the following reasons. First, doctors do not have the time nor the desire to be “mechanical Turks” who label row-by-row millions of ECG patient records for model training. Second, doctors do not trust black box diagnostic predictions as humans need reasons to support deliberate actions. Third, “the right-to-know” regulation included in GDPR requires organizations to provide to stakeholders explanations for any automatic decision making. We overcomes these challenges with an innovative, patent-pending variant of the Neural Networks. The LNN by Trendalyze in cooperation with LaTrobe University (Australia) and St. Ekaterina University Hospital (Bulgaria). It achieved the best performance of 100% within-patient accuracy in recognizing atrial fibrillation in 12-lead ECG recordings and showed robustness with respect to the wide variations of ECG patterns among different patients.