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Lu J, Stewart J, Bennamoun M, Goudie A, Eshraghian J, Ihdayhid A, Sanfilippo F, Small GR, Chow BJ, Dwivedi G. Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107717. [PMID: 37454499 DOI: 10.1016/j.cmpb.2023.107717] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 05/27/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Cardiac exercise stress testing (EST) offers a non-invasive way in the management of patients with suspected coronary artery disease (CAD). However, up to 30% EST results are either inconclusive or non-diagnostic, which results in significant resource wastage. Our aim was to build machine learning (ML) based models, using patients demographic (age, sex) and pre-test clinical information (reason for performing test, medications, blood pressure, heart rate, and resting electrocardiogram), capable of predicting EST results beforehand including those with inconclusive or non-diagnostic results. METHODS A total of 30,710 patients (mean age 54.0 years, 69% male) were included in the study with 25% randomly sampled in the test set, and the remaining samples were split into a train and validation set with a ratio of 9:1. We constructed different ML models from pre-test variables and compared their discriminant power using the area under the receiver operating characteristic curve (AUC). RESULTS A network of Oblivious Decision Trees provided the best discriminant power (AUC=0.83, sensitivity=69%, specificity=0.78%) for predicting inconclusive EST results. A total of 2010 inconclusive ESTs were correctly identified in the testing set. CONCLUSIONS Our ML model, developed using demographic and pre-test clinical information, can accurately predict EST results and could be used to identify patients with inconclusive or non-diagnostic results beforehand. Our system could thus be used as a personalised decision support tool by clinicians for optimizing the diagnostic test selection strategy for CAD patients and to reduce healthcare expenditure by reducing nondiagnostic or inconclusive ESTs.
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Affiliation(s)
- Juan Lu
- Department of Computer Science and Software Engineering, The University of Western Australia, Australia; Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia
| | - Jonathon Stewart
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Australia
| | - Adrian Goudie
- Emergency Department, Fiona Stanley Hospital, Perth, Australia
| | - Jason Eshraghian
- Department of Computer Science and Software Engineering, The University of Western Australia, Australia; Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
| | - Abdul Ihdayhid
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia; Cardiology Department, Fiona Stanley Hospital, Perth, Australia
| | - Frank Sanfilippo
- School of Population and Global Health, The University of Western Australia, Australia
| | - Gary R Small
- Division of Cardiology, University of Ottawa Heart Institute, Department of Medicine, University of Ottawa, Canada
| | - Benjamin Jw Chow
- Division of Cardiology, University of Ottawa Heart Institute, Department of Medicine, University of Ottawa, Canada
| | - Girish Dwivedi
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia; Cardiology Department, Fiona Stanley Hospital, Perth, Australia.
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de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
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Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
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