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.
KEYWORDS
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Published online: April 23, 2020
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