Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography
Background: The accuracy of an artificial intelligence model based on echocardiography video data in the diagnosis of heart failure (HF) called LIFES (Learning Intelligent for Effective Sonography) was investigated. Methods: A cross-sectional diagnostic test was conducted using consecutive sampling...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Interna Publishing
2022-07-01
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| Series: | Acta Medica Indonesiana |
| Subjects: | |
| Online Access: | https://actamedindones.org/index.php/ijim/article/view/2185 |
| _version_ | 1798001250295349248 |
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| author | Lies Dina Liastuti Bambang Budi Siswanto Renan Sukmawan Wisnu Jatmiko Idrus Alwi Budi Wiweko Aria Kekalih Yosilia Nursakina Rindayu Yusticia Indira Putri Grafika Jati Mgs M Luthfi Ramadhan Ericko Govardi Aqsha Azhary Nur |
| author_facet | Lies Dina Liastuti Bambang Budi Siswanto Renan Sukmawan Wisnu Jatmiko Idrus Alwi Budi Wiweko Aria Kekalih Yosilia Nursakina Rindayu Yusticia Indira Putri Grafika Jati Mgs M Luthfi Ramadhan Ericko Govardi Aqsha Azhary Nur |
| author_sort | Lies Dina Liastuti |
| collection | DOAJ |
| description | Background: The accuracy of an artificial intelligence model based on echocardiography video data in the diagnosis of heart failure (HF) called LIFES (Learning Intelligent for Effective Sonography) was investigated. Methods: A cross-sectional diagnostic test was conducted using consecutive sampling of HF and normal patients’ echocardiography data. The gold-standard comparison was HF diagnosis established by expert cardiologists based on clinical data and echocardiography. After pre-processing, the AI model is built based on Long-Short Term Memory (LSTM) using independent variable estimation and video classification techniques. The model will classify the echocardiography video data into normal and heart failure category. Statistical analysis was carried out to calculate the value of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). Results: A total of 138 patients with HF admitted to Harapan Kita National Heart Center from January 2020 to October 2021 were selected as research subjects. The first scenario yielded decent diagnostic performance for distinguishing between heart failure and normal patients. In this model, the overall diagnostic accuracy of A2C, A4C, PLAX-view were 92,96%, 90,62% and 88,28%, respectively. The automated ML-derived approach had the best overall performance using the 2AC view, with a misclassification rate of only 7,04%. Conclusion: The LIFES model was feasible, accurate, and quick in distinguishing between heart failure and normal patients through series of echocardiography images. |
| first_indexed | 2024-04-11T11:33:13Z |
| format | Article |
| id | doaj.art-13e907971f904c1c99b799f1fd744b7d |
| institution | Directory Open Access Journal |
| issn | 0125-9326 2338-2732 |
| language | English |
| last_indexed | 2024-04-11T11:33:13Z |
| publishDate | 2022-07-01 |
| publisher | Interna Publishing |
| record_format | Article |
| series | Acta Medica Indonesiana |
| spelling | doaj.art-13e907971f904c1c99b799f1fd744b7d2022-12-22T04:26:03ZengInterna PublishingActa Medica Indonesiana0125-93262338-27322022-07-01543532Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in EchocardiographyLies Dina Liastuti0Bambang Budi Siswanto1Renan Sukmawan2Wisnu Jatmiko3Idrus Alwi4Budi Wiweko5Aria Kekalih6Yosilia Nursakina7Rindayu Yusticia Indira Putri8Grafika Jati9Mgs M Luthfi Ramadhan10Ericko Govardi11Aqsha Azhary Nur121. Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, Jakarta, Indonesia 2. Dr. Cipto Mangunkusumo Hospital, Jakarta, IndonesiaDepartment of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, Jakarta, IndonesiaDepartment of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, Jakarta, IndonesiaFaculty of Computer Science Universitas Indonesia, IndonesiaFaculty of Medicine, Universitas Indonesia, Jakarta, IndonesiaFaculty of Medicine, Universitas Indonesia, Jakarta, IndonesiaFaculty of Medicine, Universitas Indonesia, Jakarta, Indonesia1. Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia 2. School of Public Health, Imperial College London, United KingdomDepartment of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, Jakarta, IndonesiaFaculty of Computer Science Universitas Indonesia, IndonesiaFaculty of Computer Science Universitas Indonesia, IndonesiaDepartment of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, Jakarta, IndonesiaThe Johns Hopkins Bloomberg School of Public Health, Baltimore, United StatesBackground: The accuracy of an artificial intelligence model based on echocardiography video data in the diagnosis of heart failure (HF) called LIFES (Learning Intelligent for Effective Sonography) was investigated. Methods: A cross-sectional diagnostic test was conducted using consecutive sampling of HF and normal patients’ echocardiography data. The gold-standard comparison was HF diagnosis established by expert cardiologists based on clinical data and echocardiography. After pre-processing, the AI model is built based on Long-Short Term Memory (LSTM) using independent variable estimation and video classification techniques. The model will classify the echocardiography video data into normal and heart failure category. Statistical analysis was carried out to calculate the value of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). Results: A total of 138 patients with HF admitted to Harapan Kita National Heart Center from January 2020 to October 2021 were selected as research subjects. The first scenario yielded decent diagnostic performance for distinguishing between heart failure and normal patients. In this model, the overall diagnostic accuracy of A2C, A4C, PLAX-view were 92,96%, 90,62% and 88,28%, respectively. The automated ML-derived approach had the best overall performance using the 2AC view, with a misclassification rate of only 7,04%. Conclusion: The LIFES model was feasible, accurate, and quick in distinguishing between heart failure and normal patients through series of echocardiography images.https://actamedindones.org/index.php/ijim/article/view/2185artificial intelligencemachine learningechocardiographyheart failure |
| spellingShingle | Lies Dina Liastuti Bambang Budi Siswanto Renan Sukmawan Wisnu Jatmiko Idrus Alwi Budi Wiweko Aria Kekalih Yosilia Nursakina Rindayu Yusticia Indira Putri Grafika Jati Mgs M Luthfi Ramadhan Ericko Govardi Aqsha Azhary Nur Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography Acta Medica Indonesiana artificial intelligence machine learning echocardiography heart failure |
| title | Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography |
| title_full | Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography |
| title_fullStr | Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography |
| title_full_unstemmed | Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography |
| title_short | Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography |
| title_sort | learning intelligent for effective sonography lifes model for rapid diagnosis of heart failure in echocardiography |
| topic | artificial intelligence machine learning echocardiography heart failure |
| url | https://actamedindones.org/index.php/ijim/article/view/2185 |
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