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Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography

ACTA MEDICA INDONESIANA · Vol. 54 No. 3 (2022)

ISSN
2338-2732, 0125-9326
Terbit
2022-09-24
Penerbit
The Indonesian Society of Internal Medicine
Bahasa
eng

Abstrak

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.

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  • University of Indonesia
  • The University of Melbourne
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Rekan penulisOrganisasiArtikel bersama
Kuntjoro HarimurtiAfiliasi tidak diketahui7
Ari Fahrial SyamAfiliasi tidak diketahui5
Eka GinanjarAfiliasi tidak diketahui4
Arif MansjoerAfiliasi tidak diketahui4
Hamzah ShatriAfiliasi tidak diketahui3
Muhammad YaminAfiliasi tidak diketahui3
Taufik IndrajayaAfiliasi tidak diketahui3
Cosphiadi IrawanAfiliasi tidak diketahui3
Siti SetiatiAfiliasi tidak diketahui3
Sally Aman NasutionAfiliasi tidak diketahui3
Em YunirUniversity of Indonesia3
Hadiki HabibAfiliasi tidak diketahui3
Nury NusdwinuringtyasAfiliasi tidak diketahui3
Adila Alfa KrisnadhiUniversity of Indonesia2
N. W. PambudiUniversity of Indonesia2
Dewa Made Sri ArsaUniversity of Indonesia2
Muhammad Hafizhuddin HilmanThe University of Melbourne2
Ika P WijayaAfiliasi tidak diketahui2
Simon SalimAfiliasi tidak diketahui2
Suzanna ImmanuelAfiliasi tidak diketahui2
Aida LydiaAfiliasi tidak diketahui2
Suhendro SuhendroAfiliasi tidak diketahui2
Dita AditianingsihAfiliasi tidak diketahui2
Trimartani TrimartaniAfiliasi tidak diketahui2
Suhendro SuwartoAfiliasi tidak diketahui2
Imam SubektiAfiliasi tidak diketahui2
Adityo SusiloAfiliasi tidak diketahui2

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Sumber metadata: OAI-PMH (metadata jurnal) http://www.actamedindones.org/index.php/ijim/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography</dc:title>
	<dc:creator>Liastuti, Lies Dina</dc:creator>
	<dc:creator>Siswanto, Bambang Budi</dc:creator>
	<dc:creator>Sukmawan, Renan</dc:creator>
	<dc:creator>Jatmiko, Wisnu</dc:creator>
	<dc:creator>Alwi, Idrus</dc:creator>
	<dc:creator>Wiweko, Budi</dc:creator>
	<dc:creator>Kekalih, Aria</dc:creator>
	<dc:creator>Nursakina, Yosilia</dc:creator>
	<dc:creator>Putri, Rindayu Yusticia Indira</dc:creator>
	<dc:creator>Jati, Grafika</dc:creator>
	<dc:creator>Ramadhan, Mgs M Luthfi</dc:creator>
	<dc:creator>Govardi, Ericko</dc:creator>
	<dc:creator>Nur, Aqsha Azhary</dc:creator>
	<dc:subject xml:lang="en-US">artificial intelligence</dc:subject>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:subject xml:lang="en-US">echocardiography</dc:subject>
	<dc:subject xml:lang="en-US">heart failure</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">The Indonesian Society of Internal Medicine</dc:publisher>
	<dc:date>2022-09-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>http://www.actamedindones.org/index.php/ijim/article/view/2185</dc:identifier>
	<dc:source xml:lang="en-US">Acta Medica Indonesiana; Vol. 54 No. 3 (2022): Acta Medica Indonesiana; 428</dc:source>
	<dc:source>2338-2732</dc:source>
	<dc:source>0125-9326</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://www.actamedindones.org/index.php/ijim/article/view/2185/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Lies Dina Liastuti</dc:rights>
</oai_dc:dc>