Yong-Yeon Jo1, Younghoon Cho2, Soo Youn Lee3, Joon-Myoung Kwon4, Kyung-Hee Kim5, Ki-Hyun Jeon5, Soohyun Cho2, Jinsik Park3, Byung-Hee Oh3. 1. Medical research team, Medical AI, Seoul, South Korea. 2. Medical Research and Development Center, Bodyfriend, Seoul, South Korea. 3. Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea. 4. Medical research team, Medical AI, Seoul, South Korea; Medical Research and Development Center, Bodyfriend, Seoul, South Korea; Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, South Korea; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Incheon, South Korea. Electronic address: kwonjm@sejongh.co.kr. 5. Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Incheon, South Korea.
Abstract
INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practice owing to their lack of interpretability. We developed an explainable DLM to detect AF using ECG and validated its performance using diverse formats of ECG. METHODS: We conducted a retrospective study. The Sejong ECG dataset comprising 128,399 ECGs was used to develop and internally validated the explainable DLM. DLM was developed with two feature modules, which could describe the reason for DLM decisions. DLM was external validated using data from 21,837, 10,605, and 8528 ECGs from PTB-XL, Chapman, and PhysioNet non-restricted datasets, respectively. The predictor variables were digitally stored ECGs, and the endpoints were AFs. RESULTS: During internal and external validation of the DLM, the area under the receiver operating characteristic curves (AUCs) of the DLM using a 12‑lead ECG in detecting AF were 0.997-0.999. The AUCs of the DLM with VAE using a 6‑lead and single‑lead ECG were 0.990-0.999. The AUCs of explainability about features such as rhythm irregularity and absence of P-wave were 0.961-0.993 and 0.983-0.993, respectively. CONCLUSIONS: Our DLM successfully detected AF using diverse ECGs and described the reason for this decision. The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of the DLM for its application in clinical practice.
INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practice owing to their lack of interpretability. We developed an explainable DLM to detect AF using ECG and validated its performance using diverse formats of ECG. METHODS: We conducted a retrospective study. The Sejong ECG dataset comprising 128,399 ECGs was used to develop and internally validated the explainable DLM. DLM was developed with two feature modules, which could describe the reason for DLM decisions. DLM was external validated using data from 21,837, 10,605, and 8528 ECGs from PTB-XL, Chapman, and PhysioNet non-restricted datasets, respectively. The predictor variables were digitally stored ECGs, and the endpoints were AFs. RESULTS: During internal and external validation of the DLM, the area under the receiver operating characteristic curves (AUCs) of the DLM using a 12‑lead ECG in detecting AF were 0.997-0.999. The AUCs of the DLM with VAE using a 6‑lead and single‑lead ECG were 0.990-0.999. The AUCs of explainability about features such as rhythm irregularity and absence of P-wave were 0.961-0.993 and 0.983-0.993, respectively. CONCLUSIONS: Our DLM successfully detected AF using diverse ECGs and described the reason for this decision. The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of the DLM for its application in clinical practice.
Authors: Jonas L Isaksen; Mathias Baumert; Astrid N L Hermans; Molly Maleckar; Dominik Linz Journal: Herzschrittmacherther Elektrophysiol Date: 2022-02-11
Authors: Rob Brisk; Raymond R Bond; Dewar Finlay; James A D McLaughlin; Alicja J Piadlo; David J McEneaney Journal: Front Physiol Date: 2022-03-17 Impact factor: 4.566