Joon-Myoung Kwon1, Kyung-Hee Kim2, Jose Medina-Inojosa3, Ki-Hyun Jeon4, Jinsik Park5, Byung-Hee Oh5. 1. Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea; Artificial Intelligence and Big Data Center, Sejong Medical Research Center, Bucheon, Korea. 2. Artificial Intelligence and Big Data Center, Sejong Medical Research Center, Bucheon, Korea; Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea. Electronic address: learnbyliving9@gmail.com. 3. Department of Cardiovascular Disease, Division of Preventive Cardiology, Mayo Clinic, Rochester, Minnesota. 4. Artificial Intelligence and Big Data Center, Sejong Medical Research Center, Bucheon, Korea; Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea. 5. Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
Abstract
BACKGROUND: Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting PH using electrocardiography (ECG). METHODS: This historical cohort study included data from consecutive patients from 2 hospitals. The patients in one hospital were divided into derivation (56,670 ECGs from 24,202 patients) and internal validation (3,174 ECGs from 3,174 patients) datasets, whereas the patients in the other hospital were included in only an external validation (10,865 ECGs from 10,865 patients) dataset. An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal and demographic information from the derivation dataset. The end-point was the diagnosis of PH. In addition, the interpretable AI algorithm identified which region had the most significant effect on decision making using a sensitivity map. RESULTS: During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm for detecting PH was 0.859 and 0.902, respectively. In the 2,939 individuals without PH at initial echocardiography, those patients that the AI defined as having a higher risk had a significantly higher chance of developing PH than those in the low-risk group (31.5% vs 5.9%, p < 0.001) during the follow-up period. The sensitivity map showed that the AI algorithm focused on the S-wave, P-wave, and T-wave for each patient by QRS complex characteristics. CONCLUSIONS: The AI algorithm demonstrated high accuracy for PH prediction using 12-lead and single-lead ECGs.
BACKGROUND: Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting PH using electrocardiography (ECG). METHODS: This historical cohort study included data from consecutive patients from 2 hospitals. The patients in one hospital were divided into derivation (56,670 ECGs from 24,202 patients) and internal validation (3,174 ECGs from 3,174 patients) datasets, whereas the patients in the other hospital were included in only an external validation (10,865 ECGs from 10,865 patients) dataset. An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal and demographic information from the derivation dataset. The end-point was the diagnosis of PH. In addition, the interpretable AI algorithm identified which region had the most significant effect on decision making using a sensitivity map. RESULTS: During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm for detecting PH was 0.859 and 0.902, respectively. In the 2,939 individuals without PH at initial echocardiography, those patients that the AI defined as having a higher risk had a significantly higher chance of developing PH than those in the low-risk group (31.5% vs 5.9%, p < 0.001) during the follow-up period. The sensitivity map showed that the AI algorithm focused on the S-wave, P-wave, and T-wave for each patient by QRS complex characteristics. CONCLUSIONS: The AI algorithm demonstrated high accuracy for PH prediction using 12-lead and single-lead ECGs.
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