Academic Journal
Artificial intelligence for detecting electrolyte imbalance using electrocardiography
| العنوان: | Artificial intelligence for detecting electrolyte imbalance using electrocardiography |
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| المؤلفون: | Joon‐myoung Kwon, Min‐Seung Jung, Kyung‐Hee Kim, Yong‐Yeon Jo, Jae‐Hyun Shin, Yong‐Hyeon Cho, Yoon‐Ji Lee, Jang‐Hyeon Ban, Ki‐Hyun Jeon, Soo Youn Lee, Jinsik Park, Byung‐Hee Oh |
| المصدر: | Annals of Noninvasive Electrocardiology, Vol 26, Iss 3, Pp n/a-n/a (2021) |
| بيانات النشر: | Wiley, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Diseases of the circulatory (Cardiovascular) system |
| مصطلحات موضوعية: | artificial intelligence, deep learning, electrocardiography, electrolytes, Diseases of the circulatory (Cardiovascular) system, RC666-701 |
| الوصف: | Abstract Introduction The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. Methods and Results This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12‐lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. Conclusion The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1542-474X 1082-720X |
| Relation: | https://doaj.org/toc/1082-720X; https://doaj.org/toc/1542-474X |
| DOI: | 10.1111/anec.12839 |
| URL الوصول: | https://doaj.org/article/386a234b95b248d8a2334ef8cb560a9b |
| رقم الانضمام: | edsdoj.386a234b95b248d8a2334ef8cb560a9b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1542474X 1082720X |
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| DOI: | 10.1111/anec.12839 |