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

Liastuti, Lies Dina; Siswanto, Bambang Budi; Sukmawan, Renan; Jatmiko, Wisnu; Alwi, Idrus; Wiweko, Budi; Kekalih, Aria; Nursakina, Yosilia; Putri, Rindayu Yusticia Indira; Jati, Grafika; Ramadhan, Mgs M Luthfi; Govardi, Ericko; Nur, Aqsha Azhary

Journal SINTA 1

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
Published: 2022-09-24
Publisher: The Indonesian Society of Internal Medicine
Language: eng

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 236 co-authorship links · 2 across different organisations · showing the 40 most connected

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University of Indonesia
The University of Melbourne
Affiliation unknown

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Same organisation
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Co-author	Organisation	Joint articles
Kuntjoro Harimurti	Affiliation unknown	7
Ari Fahrial Syam	Affiliation unknown	5
Eka Ginanjar	Affiliation unknown	4
Arif Mansjoer	Affiliation unknown	4
Hamzah Shatri	Affiliation unknown	3
Muhammad Yamin	Affiliation unknown	3
Taufik Indrajaya	Affiliation unknown	3
Cosphiadi Irawan	Affiliation unknown	3
Siti Setiati	Affiliation unknown	3
Sally Aman Nasution	Affiliation unknown	3
Em Yunir	University of Indonesia	3
Hadiki Habib	Affiliation unknown	3
Nury Nusdwinuringtyas	Affiliation unknown	3
Adila Alfa Krisnadhi	University of Indonesia	2
N. W. Pambudi	University of Indonesia	2
Dewa Made Sri Arsa	University of Indonesia	2
Muhammad Hafizhuddin Hilman	The University of Melbourne	2
Ika P Wijaya	Affiliation unknown	2
Simon Salim	Affiliation unknown	2
Suzanna Immanuel	Affiliation unknown	2
Aida Lydia	Affiliation unknown	2
Suhendro Suhendro	Affiliation unknown	2
Dita Aditianingsih	Affiliation unknown	2
Trimartani Trimartani	Affiliation unknown	2
Suhendro Suwarto	Affiliation unknown	2
Imam Subekti	Affiliation unknown	2
Adityo Susilo	Affiliation unknown	2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://www.actamedindones.org/index.php/ijim/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<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>