1
|
Chaiter Y, Fink DL, Machluf Y. Vascular medicine in the 21 st century: Embracing comprehensive vasculature evaluation and multidisciplinary treatment. World J Clin Cases 2024; 12:6032-6044. [DOI: 10.12998/wjcc.v12.i27.6032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 06/25/2024] [Accepted: 07/10/2024] [Indexed: 07/29/2024] Open
Abstract
The field of vascular medicine has undergone a profound transformation in the 21st century, transforming our approach to assessment and treatment. Atherosclerosis, a complex inflammatory disease that affects medium and large arteries, presents a major challenge for researchers and healthcare professionals. This condition, characterized by arterial plaque formation and narrowing, poses substantial challenges to vascular health at individual, national, and global scales. Its repercussions are far-reaching, with clinical outcomes including ischemic heart disease, ischemic stroke, and peripheral arterial disease—conditions with escalating global prevalence. Early detection of vascular changes caused by atherosclerosis is crucial in preventing these conditions, reducing morbidity, and averting mortality. This article underscored the imperative of adopting a holistic approach to grappling with the intricacies, trajectories, and ramifications of atherosclerosis. It stresses the need for a thorough evaluation of the vasculature and the implementation of a multidisciplinary treatment approach. By considering the entire vascular system, healthcare providers can explore avenues for prevention, early detection, and effective management of this condition, ultimately leading to improved patient outcomes. We discussed current practices and proposed new directions made possible by emerging diagnostic modalities and treatment strategies. Additionally, we considered healthcare expenditure, resource allocation, and the transformative potential of new innovative treatments and technologies.
Collapse
Affiliation(s)
- Yoram Chaiter
- The Israeli Center for Emerging Technologies in Hospitals and Hospital-Based Health Technology Assessment, Shamir (Assaf Harofeh) Medical Center, Zerifin 7030100, Israel
| | - Daniel Lyon Fink
- Department of Pediatric Cardiology Unit, HaEmek Medical Center, Afula 1834111, Israel
| | - Yossy Machluf
- Shamir Research Institute, University of Haifa, Kazerin 1290000, Israel
| |
Collapse
|
2
|
Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
Collapse
Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
| |
Collapse
|
3
|
Herman R, Meyers HP, Smith SW, Bertolone DT, Leone A, Bermpeis K, Viscusi MM, Belmonte M, Demolder A, Boza V, Vavrik B, Kresnakova V, Iring A, Martonak M, Bahyl J, Kisova T, Schelfaut D, Vanderheyden M, Perl L, Aslanger EK, Hatala R, Wojakowski W, Bartunek J, Barbato E. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:123-133. [PMID: 38505483 PMCID: PMC10944682 DOI: 10.1093/ehjdh/ztad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/13/2023] [Accepted: 11/02/2023] [Indexed: 03/21/2024]
Abstract
Aims A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
Collapse
Affiliation(s)
- Robert Herman
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | | | - Stephen W Smith
- Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN, USA
| | - Dario T Bertolone
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Attilio Leone
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Konstantinos Bermpeis
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Michele M Viscusi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Marta Belmonte
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | | | - Vladimir Boza
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
- Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Boris Vavrik
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Viera Kresnakova
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
- Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Kosice, Slovakia
| | - Andrej Iring
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Michal Martonak
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Jakub Bahyl
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Timea Kisova
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
- Faculty of Medicine and Dentistry, Barts and The London School of Medicine and Dentistry, London, UK
| | - Dan Schelfaut
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Marc Vanderheyden
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Leor Perl
- Department of Cardiology, Rabin Medical Center, Petah Tikvah, Israel
| | - Emre K Aslanger
- Department of Cardiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Robert Hatala
- Department of Arrhythmia and Pacing, National Institute of Cardiovascular Diseases, Bratislava, Slovakia
| | - Wojtek Wojakowski
- Department of Cardiology and Structural Heart Diseases, Medical University of Silesia, Katowice, Poland
| | - Jozef Bartunek
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Emanuele Barbato
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| |
Collapse
|
4
|
Terzi MB, Arikan O. Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram. BIOMED ENG-BIOMED TE 2024; 69:79-109. [PMID: 37823386 DOI: 10.1515/bmt-2022-0406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 08/25/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. METHODS In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. RESULTS Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. CONCLUSIONS The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).
Collapse
Affiliation(s)
- Merve Begum Terzi
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
| | - Orhan Arikan
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
| |
Collapse
|
5
|
Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
Collapse
Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
| |
Collapse
|
6
|
Xu M, Yang F, Shen B, Wang J, Niu W, Chen H, Li N, Chen W, Wang Q, HE Z, Ding R. A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022. Front Cardiovasc Med 2023; 10:1090220. [PMID: 37576112 PMCID: PMC10416645 DOI: 10.3389/fcvm.2023.1090220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 06/01/2023] [Indexed: 08/15/2023] Open
Abstract
Background Plenty of publications had been written in the last several decades on acute myocardial infarction (AMI) in women. However, there are few bibliometric analyses in such field. In order to solve this problem, we attempted to examine the knowledge structure and development of research about AMI in women based on analysis of related publications. Method The Web of Science Core Collection was used to extract all publications regarding AMI in women, ranging from January 2000 to August 2022. Bibliometric analysis was performed using VOSviewer, Cite Space, and an online bibliometric analysis platform. Results A total of 14,853 publications related to AMI in women were identified from 2000 to 2022. Over the past 20 years, the United States had published the most articles in international research and participated in international cooperation the most frequently. The primary research institutions were Harvard University and University of Toronto. Circulation was the most cited journal and had an incontrovertible academic impact. 67,848 authors were identified, among which Harlan M Krumholz had the most significant number of articles and Thygesen K was co-cited most often. And the most common keywords included risk factors, disease, prognosis, mortality, criteria and algorithm. Conclusion The research hotspots and trends of AMI in women were identified and explored using bibliometric and visual methods. Researches about AMI in women are flourishing. Criteria and algorithms might be the focus of research in the near future, which deserved great attentions.
Collapse
Affiliation(s)
- Ming Xu
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
- Department of Cardiology, Shanghai Navy Feature Medical Center, Naval Medical University, Shanghai, China
| | - Fupeng Yang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Bin Shen
- Department of Cardiology, Shanghai Navy Feature Medical Center, Naval Medical University, Shanghai, China
| | - Jiamei Wang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Wenhao Niu
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Hui Chen
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Na Li
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Wei Chen
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Qinqin Wang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Zhiqing HE
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Ru Ding
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| |
Collapse
|
7
|
Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, Bouzid Z, Faramand Z, Alrawashdeh MO, Gregg RE, Helman S, Riek NT, Kraevsky-Phillips K, Clermont G, Akcakaya M, Sereika SM, Van Dam P, Smith SW, Birnbaum Y, Saba S, Sejdic E, Callaway CW. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med 2023; 29:1804-1813. [PMID: 37386246 PMCID: PMC10353937 DOI: 10.1038/s41591-023-02396-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/11/2023] [Indexed: 07/01/2023]
Abstract
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
Collapse
Affiliation(s)
- Salah S Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Christian Martin-Gill
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Zeineb Bouzid
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziad Faramand
- Department of Emergency Medicine, Northeast Georgia Health System, Gainesville, GA, USA
| | - Mohammad O Alrawashdeh
- School of Nursing, Jordan University of Science and Technology, Irbid, Jordan
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Richard E Gregg
- Advanced Algorithm Development Center, Philips Healthcare, Cambridge, MA, USA
| | - Stephanie Helman
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nathan T Riek
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Murat Akcakaya
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan M Sereika
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter Van Dam
- Division of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN, USA
- Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
| | - Samir Saba
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ervin Sejdic
- Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
- Artificial Intelligence for Health Outcomes at Research & Innovation, North York General Hospital, Toronto, ON, Canada
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| |
Collapse
|
8
|
Ledziński Ł, Grześk G. Artificial Intelligence Technologies in Cardiology. J Cardiovasc Dev Dis 2023; 10:jcdd10050202. [PMID: 37233169 DOI: 10.3390/jcdd10050202] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
Collapse
Affiliation(s)
- Łukasz Ledziński
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | - Grzegorz Grześk
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| |
Collapse
|
9
|
Liu B, Chang H, Yang D, Yang F, Wang Q, Deng Y, Li L, Lv W, Zhang B, Yu L, Burkhoff D, He K. A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection. Sci Rep 2023; 13:3. [PMID: 36593284 PMCID: PMC9807607 DOI: 10.1038/s41598-022-27211-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are normally adapted to a few diseases and specific equipment. Here, we present an end-to-end deep learning framework named AIEchoDx that differentiates four common cardiovascular diseases (Atrial Septal Defect, Dilated Cardiomyopathy, Hypertrophic Cardiomyopathy, prior Myocardial Infarction) from normal subjects with performance comparable to that of consensus of three senior cardiologists in AUCs (99.50% vs 99.26%, 98.75% vs 92.75%, 99.57% vs 97.21%, 98.52% vs 84.20%, and 98.70% vs 89.41%), respectively. Meanwhile, AIEchoDx accurately recognizes critical lesion regions of interest along with each disease by visualizing the decision-making process. Furthermore, our analysis indicates that heterogeneous diseases, like dilated cardiomyopathy, could be classified into two phenogroups with distinct clinical characteristics. Finally, AIEchoDx performs efficiently as an anomaly detection tool when applying handheld device-produced videos. Together, AIEchoDx provides a potential diagnostic assistant tool in either cart-based echocardiography equipment or handheld echocardiography device for primary and point-of-care medical personnel with high diagnostic performance, and the application of lesion region identification and heterogeneous disease phenogrouping, which may broaden the application of artificial intelligence in echocardiography.
Collapse
Affiliation(s)
- Bohan Liu
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
| | - Hao Chang
- grid.47100.320000000419368710Department of Genetics, Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT 06510 USA
| | - Dong Yang
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China
| | - Feifei Yang
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China ,grid.414252.40000 0004 1761 8894Department of Cardiology, The Fourth Medical Center of Chinese PLA General Hospital, Beijing, 100037 China
| | - Qiushuang Wang
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China ,grid.414252.40000 0004 1761 8894Department of Cardiology, The Fourth Medical Center of Chinese PLA General Hospital, Beijing, 100037 China
| | - Yujiao Deng
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China ,grid.414252.40000 0004 1761 8894Department of Ultrasound Diagnosis, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853 China
| | - Lijun Li
- grid.414252.40000 0004 1761 8894Department of Cardiology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853 China
| | - Wenqing Lv
- grid.414373.60000 0004 1758 1243Department of Cardiology, Beijing Tongren Hospital, Beijing, 100176 China
| | - Bo Zhang
- Department of Ultrasound, Chinese PLA 923 Hospital, Nanning, 530021 China
| | - Liheng Yu
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
| | - Daniel Burkhoff
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China ,grid.21729.3f0000000419368729Division of Cardiology, Columbia University, New York City, NY 10027 USA
| | - Kunlun He
- grid.414252.40000 0004 1761 8894Medical Big Data Research Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China ,grid.414252.40000 0004 1761 8894Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
10
|
Groun N, Villalba-Orero M, Lara-Pezzi E, Valero E, Garicano-Mena J, Le Clainche S. A novel data-driven method for the analysis and reconstruction of cardiac cine MRI. Comput Biol Med 2022; 151:106317. [PMID: 36442273 DOI: 10.1016/j.compbiomed.2022.106317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 11/08/2022] [Accepted: 11/13/2022] [Indexed: 11/17/2022]
Abstract
Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood flow, which makes it highly used in medical science. But due to the image time acquisition and several other factors the MRI sequences can easily get corrupted, causing radiologists to misdiagnose 40 million people worldwide each and every single year. Hence, the urge to decrease these numbers, researchers from different fields have been introducing novel tools and methods in the medical field. Aiming to the same target, we consider in this work the application of the higher order dynamic mode decomposition (HODMD) technique. The HODMD algorithm is a linear method, which was originally introduced in the fluid dynamics domain, for the analysis of complex systems. Nevertheless, the proposed method has extended its applicability to numerous domains, including medicine. In this work, HODMD in used to analyze sets of MR images of a heart, with the ultimate goal of identifying the main patterns and frequencies driving the heart dynamics. Furthermore, a novel interpolation algorithm based on singular value decomposition combined with HODMD is introduced, providing a three-dimensional reconstruction of the heart. This algorithm is applied (i) to reconstruct corrupted or missing images, and (ii) to build a reduced order model of the heart dynamics.
Collapse
Affiliation(s)
- Nourelhouda Groun
- ETSI Aeronáutica y del Espacio and ETSI Telecomunicación - Universidad Politécnica de Madrid, 28040 Madrid, Spain.
| | - María Villalba-Orero
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), C. de Melchor Fernández Almagro, 3, 28029 Madrid, Spain; Departamento de Medicina y Cirugía Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Enrique Lara-Pezzi
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), C. de Melchor Fernández Almagro, 3, 28029 Madrid, Spain
| | - Eusebio Valero
- ETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, 28040 Madrid, Spain; Center for Computational Simulation (CCS), 28660 Boadilla del Monte, Spain
| | - Jesús Garicano-Mena
- ETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, 28040 Madrid, Spain; Center for Computational Simulation (CCS), 28660 Boadilla del Monte, Spain
| | - Soledad Le Clainche
- ETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, 28040 Madrid, Spain; Center for Computational Simulation (CCS), 28660 Boadilla del Monte, Spain.
| |
Collapse
|
11
|
Kong V, Somakhamixay O, Cho WS, Kang G, Won H, Rah H, Bang HJ. Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study. PeerJ 2022; 10:e14348. [PMID: 36405028 PMCID: PMC9673763 DOI: 10.7717/peerj.14348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/16/2022] [Indexed: 11/16/2022] Open
Abstract
Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively.
Collapse
Affiliation(s)
| | - Oui Somakhamixay
- Department of Management Information Systems, Chungbuk National University, Cheongju, South Korea
| | - Wan-Sup Cho
- Department of Management Information Systems, Chungbuk National University, Cheongju, South Korea
| | - Gilwon Kang
- Department of Health Informatics and Management, College of Medicine, Chungbuk National University, Cheongju, South Korea
| | - Heesun Won
- Cybrebain Research Section, Electronics and Telecommunications Research Institute, Daejeon, South Korea
| | - HyungChul Rah
- Research Institute of Veterinary Medicine, Chungbuk National University, Cheongju, South Korea
| | - Heui Je Bang
- Department of Rehabilitation Medicine, College of Medicine, Chungbuk National University, Cheongju, South Korea
| |
Collapse
|
12
|
Chen KW, Wang YC, Liu MH, Tsai BY, Wu MY, Hsieh PH, Wei JT, Shih ESC, Shiao YT, Hwang MJ, Wu YL, Hsu KC, Chang KC. Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care. Front Cardiovasc Med 2022; 9:1001982. [PMID: 36312246 PMCID: PMC9614054 DOI: 10.3389/fcvm.2022.1001982] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/29/2022] [Indexed: 12/27/2022] Open
Abstract
Objective To implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy. Methods The proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as "STEMI" or "Not STEMI". In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback. Results Between July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16-20.8) minutes. Conclusion Implementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.
Collapse
Affiliation(s)
- Ke-Wei Chen
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Yu-Chen Wang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- Division of Cardiovascular Medicine, Asia University Hospital, Taichung, Taiwan
- Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, Taiwan
| | - Meng-Hsuan Liu
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Being-Yuah Tsai
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Mei-Yao Wu
- School of Post-Baccalaureate Chinese Medicine, China Medical University, Taichung, Taiwan
- Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan
| | | | - Jung-Ting Wei
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | | | - Yi-Tzone Shiao
- Center of Institutional Research and Development, Asia University, Taichung, Taiwan
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ya-Lun Wu
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Kai-Cheng Hsu
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Kuan-Cheng Chang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| |
Collapse
|
13
|
An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07527-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
14
|
Groun N, Villalba-Orero M, Lara-Pezzi E, Valero E, Garicano-Mena J, Le Clainche S. Higher order dynamic mode decomposition: From fluid dynamics to heart disease analysis. Comput Biol Med 2022; 144:105384. [DOI: 10.1016/j.compbiomed.2022.105384] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/26/2022] [Accepted: 03/02/2022] [Indexed: 11/15/2022]
|
15
|
Wu L, Huang G, Yu X, Ye M, Liu L, Ling Y, Liu X, Liu D, Zhou B, Liu Y, Zheng J, Liang S, Pu R, He X, Chen Y, Han L, Qian X. Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel. Front Cardiovasc Med 2022; 9:797207. [PMID: 35360023 PMCID: PMC8960131 DOI: 10.3389/fcvm.2022.797207] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/01/2022] [Indexed: 12/30/2022] Open
Abstract
Early diagnosis of acute ST-segment elevation myocardial infarction (STEMI) and early determination of the culprit vessel are associated with a better clinical outcome. We developed three deep learning (DL) models for detecting STEMIs and culprit vessels based on 12-lead electrocardiography (ECG) and compared them with conclusions of experienced doctors, including cardiologists, emergency physicians, and internists. After screening the coronary angiography (CAG) results, 883 cases (506 control and 377 STEMI) from internal and external datasets were enrolled for testing DL models. Convolutional neural network-long short-term memory (CNN-LSTM) (AUC: 0.99) performed better than CNN, LSTM, and doctors in detecting STEMI. Deep learning models (AUC: 0.96) performed similarly to experienced cardiologists and emergency physicians in discriminating the left anterior descending (LAD) artery. Regarding distinguishing RCA from LCX, DL models were comparable to doctors (AUC: 0.81). In summary, we developed ECG-based DL diagnosis systems to detect STEMI and predict culprit vessel occlusion, thus enhancing the accuracy and effectiveness of STEMI diagnosis.
Collapse
Affiliation(s)
- Lin Wu
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Endocrine and Metabolic Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guifang Huang
- Center for Artificial Intelligence, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Xianguan Yu
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Minzhong Ye
- Novelty-Checking Center, Guangdong Institute of Scientific and Technical Information, Guangzhou, China
| | - Lu Liu
- Department of Anesthesiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yesheng Ling
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiangyu Liu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Dinghui Liu
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bin Zhou
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yong Liu
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jianrui Zheng
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Suzhen Liang
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Rui Pu
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xuemin He
- Department of Endocrine and Metabolic Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yanming Chen
- Department of Endocrine and Metabolic Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Yanming Chen
| | - Lanqing Han
- Center for Artificial Intelligence, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
- Lanqing Han
| | - Xiaoxian Qian
- Department of Cardiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Xiaoxian Qian
| |
Collapse
|
16
|
Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A. Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9288452. [PMID: 35154361 PMCID: PMC8831075 DOI: 10.1155/2022/9288452] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
Collapse
Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Sweden
| | - Shafqat Ullah Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan
| | - Liaqat Ali
- Department of Electronics, University of Buner, Buner, Pakistan
| | - Sardar Ali
- School of Engineering and Applied Sciences, Isra University Islamabad Campus, Pakistan
| | - Yakubu Imrana
- School of Engineering, University of Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Atiqur Rahman
- Department of Computer Science, University of Science and Technology Bannu, Pakistan
| |
Collapse
|
17
|
Gibson CM, Mehta S, Ceschim MRS, Frauenfelder A, Vieira D, Botelho R, Fernandez F, Villagran C, Niklitschek S, Matheus CI, Pinto G, Vallenilla I, Lopez C, Acosta MI, Munguia A, Fitzgerald C, Mazzini J, Pisana L, Quintero S. Evolution of single-lead ECG for STEMI detection using a deep learning approach. Int J Cardiol 2022; 346:47-52. [PMID: 34801613 DOI: 10.1016/j.ijcard.2021.11.039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/05/2021] [Accepted: 11/15/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis. OBJECTIVES To develop and validate a machine learning-guided algorithm which uses a single‑lead electrocardiogram (ECG) for STEMI detection to speed diagnosis. METHODS Data was extracted from the Latin America Telemedicine Infarct Network (LATIN), a population-based Acute Myocardial Infarction (AMI) program that provides care to patients in Brazil, Colombia, Mexico, and Argentina through telemedicine. SAMPLE the first dataset was comprised of 8511 ECGs that were used for various machine learning experiments to test our Deep Learning approach for STEMI diagnosis. The second dataset of 2542 confirmed STEMI diagnosis EKG records, including specific ischemic heart wall information (anterior, inferior, and lateral), was derived from the previous dataset to test the STEMI localization model. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats with fixed window of 0.4 s to the left and 0.9 s to the right of main. Training & Testing: 90% and 10% of the total dataset, respectively, were used for both models. CLASSIFICATION two 1-D convolutional neural networks were implemented, two classes were considered for first models (STEMI/Not-STEMI) and three classes for the second model (Anterior/Inferior/Lateral) each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each model. RESULTS The single‑lead ECG strategy was able to provide an accuracy of 90.5% for STEMI detection with Lead V2, which also yielded the best results overall among individual leads. STEMI Localization model provided promising results for anterior and inferior wall STEMIs but remained suboptimal for Lateral STEMI. CONCLUSIONS An Artificial Intelligence-enhanced single‑lead ECG is a promising screening tool. This technology provides an autonomous and accurate STEMI diagnostic alternative that can be incorporated into wearable devices, potentially providing patients reliable means to seek treatment early and offers the potential to thereby improve STEMI outcomes in the long run.
Collapse
Affiliation(s)
- C Michael Gibson
- Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Sameer Mehta
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Mariana R S Ceschim
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | | | - Daniel Vieira
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Roberto Botelho
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA; Triangulo Heart Institute, Uberlandia, MG, Brazil
| | | | | | | | - Cristina I Matheus
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Gladys Pinto
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Isabella Vallenilla
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Claudia Lopez
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Maria I Acosta
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Anibal Munguia
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Clara Fitzgerald
- Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Jorge Mazzini
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Lorena Pisana
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| | - Samantha Quintero
- Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
| |
Collapse
|
18
|
Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review. Adv Ther 2021; 38:5078-5086. [PMID: 34528221 DOI: 10.1007/s12325-021-01908-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/25/2021] [Indexed: 02/08/2023]
Abstract
Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques. The application of AI in healthcare systems including hospitals and clinics has many possible advantages and future prospects. Applications of AI in cardiovascular medicine are machine learning techniques for diagnostic procedures including imaging modalities and biomarkers and predictive analytics for personalized therapies and improved outcomes. In cardiovascular medicine, AI-based systems have found new applications in risk prediction for cardiovascular diseases, in cardiovascular imaging, in predicting outcomes after revascularization procedures, and in newer drug targets. AI such as machine learning has partially resolved and provided possible solutions to unmet requirements in interventional cardiology. Predicting economically vital endpoints, predictive models with a wide range of health factors including comorbidities, socioeconomic factors, and angiographic factors comprising of the size of stents, the volume of contrast agent which was infused during angiography, stent malposition, and so on have been possible owing to machine learning and AI. Nowadays, machine learning techniques might possibly help in the identification of patients at risk, with higher morbidity and mortality following acute coronary syndrome (ACS). AI through machine learning has shown several potential benefits in patients with ACS. From diagnosis to treatment effects to predicting adverse events and mortality in patients with ACS, machine learning should find an essential place in clinical medicine and in interventional cardiology for the treatment and management of patients with ACS. This paper is a review of the literature which will focus on the application of AI in ACS.
Collapse
|
19
|
Stewart J, Lu J, Goudie A, Bennamoun M, Sprivulis P, Sanfillipo F, Dwivedi G. Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review. PLoS One 2021; 16:e0252612. [PMID: 34428208 PMCID: PMC8384172 DOI: 10.1371/journal.pone.0252612] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 03/26/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Chest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and it is important for the emergency physician to quickly and accurately diagnose life threatening causes such as acute myocardial infarction (AMI). Multiple clinical decision tools have been developed to assist clinicians in risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to adults presenting to the ED with undifferentiated chest pain and assess if ML models show improved performance when compared to physicians or current risk stratification techniques. METHODS AND FINDINGS We conducted a systematic review of journal articles that applied a ML technique to an adult patient presenting to an emergency department with undifferentiated chest pain. Multiple databases were searched from inception through to November 2020. In total, 3361 articles were screened, and 23 articles were included. We did not conduct a metanalysis due to a high level of heterogeneity between studies in both their methods, and reporting. The most common primary outcomes assessed were diagnosis of acute myocardial infarction (AMI) (12 studies), and prognosis of major adverse cardiovascular event (MACE) (6 studies). There were 14 retrospective studies and 5 prospective studies. Four studies reported the development of a machine learning model retrospectively then tested it prospectively. The most common machine learning methods used were artificial neural networks (14 studies), random forest (6 studies), support vector machine (5 studies), and gradient boosting (2 studies). Multiple studies achieved high accuracy in both the diagnosis of AMI in the ED setting, and in predicting mortality and composite outcomes over various timeframes. ML outperformed existing risk stratification scores in all cases, and physicians in three out of four cases. The majority of studies were single centre, retrospective, and without prospective or external validation. There were only 3 studies that were considered low risk of bias and had low applicability concerns. Two studies reported integrating the ML model into clinical practice. CONCLUSIONS Research on applications of ML for undifferentiated chest pain in the ED has been ongoing for decades. ML has been reported to outperform emergency physicians and current risk stratification tools to diagnose AMI and predict MACE but has rarely been integrated into practice. Many studies assessing the use of ML in undifferentiated chest pain in the ED have a high risk of bias. It is important that future studies make use of recently developed standardised ML reporting guidelines, register their protocols, and share their datasets and code. Future work is required to assess the impact of ML model implementation on clinical decision making, patient orientated outcomes, and patient and physician acceptability. TRIAL REGISTRATION International Prospective Register of Systematic Reviews registration number: CRD42020184977.
Collapse
Affiliation(s)
- Jonathon Stewart
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
| | - Juan Lu
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
| | - Adrian Goudie
- Department of Emergency Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Mohammed Bennamoun
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
| | - Peter Sprivulis
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Department of Health Western Australia, East Perth, Western Australia, Australia
| | - Frank Sanfillipo
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Girish Dwivedi
- School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| |
Collapse
|
20
|
Jiang Y, Zhang X, Ma R, Wang X, Liu J, Keerman M, Yan Y, Ma J, Song Y, Zhang J, He J, Guo S, Guo H. Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China. Clin Epidemiol 2021; 13:417-428. [PMID: 34135637 PMCID: PMC8200454 DOI: 10.2147/clep.s313343] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/17/2021] [Indexed: 12/17/2022] Open
Abstract
Background Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Accurately identifying subjects at high-risk of CVD may improve CVD outcomes. We sought to systematically examine the feasibility and performance of 7 widely used machine learning (ML) algorithms in predicting CVD risks. Methods The final analysis included 1508 Kazakh subjects in China without CVD at baseline who completed follow-up. All subjects were randomly divided into the training set (80%) and the test set (20%). L1-penalized logistic regression (LR), support vector machine with radial basis function (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (KNN), Gaussian naive Bayes (NB), and extreme gradient boosting (XGB) were employed for prediction CVD outcomes. Ten-fold cross-validation was used during model developing and hyperparameters tuning in the training set. Model performance was evaluated in the test set in light of discrimination, calibration, and clinical usefulness. RF was applied to obtain the variable importance of included variables. Twenty-two variables, including sociodemographic characteristics, medical history, cytokines, and synthetic indices, were used for model development. Results Among 1508 subjects, 203 were diagnosed with CVD over a median follow-up of 5.17 years. All 7 models had moderate to excellent discrimination (AUC ranged from 0.770 to 0.872) and were well calibrated. LR and SVM performed identically with an AUC of 0.872 (95% CI: 0.829–0.907) and 0.868 (95% CI: 0.825–0.904), respectively. LR had the lowest Brier score (0.078) and the highest sensitivity (97.1%). Decision curve analysis indicated that SVM was slightly better than LR. The inflammatory cytokines, such as hs-CRP and IL-6, were identified as strong predictors of CVD. Conclusion SVM and LR can be applied to guide clinical decision-making in the Kazakh Chinese population, and further study is required to ensure their accuracies.
Collapse
Affiliation(s)
- Yunxing Jiang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Xianghui Zhang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Rulin Ma
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Xinping Wang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Jiaming Liu
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Mulatibieke Keerman
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Yizhong Yan
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Jiaolong Ma
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Yanpeng Song
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China.,The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People's Republic of China
| | - Jingyu Zhang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Jia He
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Shuxia Guo
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China.,Department of Pathology and Key Laboratory of Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| | - Heng Guo
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China
| |
Collapse
|
21
|
Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00555-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractThe diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.
Collapse
|
22
|
Banerjee A, Chen S, Fatemifar G, Zeina M, Lumbers RT, Mielke J, Gill S, Kotecha D, Freitag DF, Denaxas S, Hemingway H. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med 2021; 19:85. [PMID: 33820530 PMCID: PMC8022365 DOI: 10.1186/s12916-021-01940-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/12/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF). METHODS For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist. RESULTS Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations). CONCLUSIONS Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.
Collapse
Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK.
- Health Data Research UK, University College London, London, UK.
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK.
- Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
| | - Suliang Chen
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | | | - R Thomas Lumbers
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK
| | - Johanna Mielke
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Simrat Gill
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Dipak Kotecha
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Daniel F Freitag
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
| |
Collapse
|
23
|
Abstract
PURPOSE OF REVIEW Artificial intelligence is a broad set of sophisticated computer-based statistical tools that have become widely available. Cardiovascular medicine with its large data repositories, need for operational efficiency and growing focus on precision care is set to be transformed by artificial intelligence. Applications range from new pathophysiologic discoveries to decision support for individual patient care to optimization of system-wide logistical processes. RECENT FINDINGS Machine learning is the dominant form of artificial intelligence wherein complex statistical algorithms 'learn' by deducing patterns in datasets. Supervised machine learning uses classified large data to train an algorithm to accurately predict the outcome, whereas in unsupervised machine learning, the algorithm uncovers mathematical relationships within unclassified data. Artificial multilayered neural networks or deep learning is one of the most successful tools. Artificial intelligence has demonstrated superior efficacy in disease phenomapping, early warning systems, risk prediction, automated processing and interpretation of imaging, and increasing operational efficiency. SUMMARY Artificial intelligence demonstrates the ability to learn through assimilation of large datasets to unravel complex relationships, discover prior unfound pathophysiological states and develop predictive models. Artificial intelligence needs widespread exploration and adoption for large-scale implementation in cardiovascular practice.
Collapse
Affiliation(s)
- Sagar Ranka
- Department of Cardiovascular Medicine, The University of Kansas, Health System, Kansas City, Kansas, USA
| | | | | |
Collapse
|
24
|
Ohu I, Benny PK, Rodrigues S, Carlson JN. Applications of machine learning in acute care research. J Am Coll Emerg Physicians Open 2020; 1:766-772. [PMID: 33145517 PMCID: PMC7593421 DOI: 10.1002/emp2.12156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/22/2020] [Accepted: 05/26/2020] [Indexed: 12/26/2022] Open
Abstract
Artificial intelligence has been successfully applied to numerous health care and non-health care-related applications and its use in emergency medicine has been expanding. Among its advantages are its speed in decision making and the opportunity for rapid, actionable deduction from unstructured data with that increases with access to larger volumes of data. Artificial intelligence algorithms are currently being applied to enable faster prognosis and diagnosis of diseases and to improve patient outcomes.1,2 Despite the successful application of artificial intelligence, it is still fraught with limitations and "unknowns" pertaining to the fact that a model's accuracy is dependent on the amount of information available for training the model, and the understanding of the complexity presented by current artificial intelligence and machine learning algorithms is often limited in many individuals outside of those involved in the field. This paper reviews the applications of artificial intelligence and machine learning to acute care research and highlights commonly used machine learning techniques, limitations, and potential future applications.
Collapse
Affiliation(s)
- Ikechukwu Ohu
- BiomedicalIndustrial and Systems Engineering DepartmentGannon UniversityEriePennsylvaniaUSA
| | - Paul Kummannoor Benny
- BiomedicalIndustrial and Systems Engineering DepartmentGannon UniversityEriePennsylvaniaUSA
| | - Steven Rodrigues
- BiomedicalIndustrial and Systems Engineering DepartmentGannon UniversityEriePennsylvaniaUSA
| | - Jestin N. Carlson
- Patient Simulation CenterMorosky College of Health ProfessionsGannon UniversityEriePennsylvaniaUSA
- Department of Emergency MedicineSaint Vincent HospitalEriePennsylvaniaUSA
| |
Collapse
|
25
|
Fu Y, Zhang Y, Khoo BL. Liquid biopsy technologies for hematological diseases. Med Res Rev 2020; 41:246-274. [PMID: 32929726 DOI: 10.1002/med.21731] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/10/2020] [Accepted: 09/02/2020] [Indexed: 12/18/2022]
Abstract
Since the discovery of circulating tumor cells in 1869, technological advances in studying circulating biomarkers from patients' blood have made the diagnosis of nonhematologic cancers less invasive. Technological advances in the detection and analysis of biomarkers provide new opportunities for the characterization of other disease types. When compared with traditional biopsies, liquid biopsy markers, such as exfoliated bladder cancer cells, circulating cell-free DNA (cfDNA), and extracellular vesicles (EV), are considered more convenient than conventional biopsies. Liquid biopsy markers undoubtedly have the potential to influence disease management and treatment dynamics. Our main focuses of this review will be the cell-based, gene-based, and protein-based key liquid biopsy markers (including EV and cfDNA) in disease detection, and discuss the research progress of these biomarkers used in conjunction with liquid biopsy. First, we highlighted the key technologies that have been broadly adopted used in hematological diseases. Second, we introduced the latest technological developments for the specific detection of cardiovascular disease, leukemia, and coronavirus disease. Finally, we concluded with perspectives on these research areas, focusing on the role of microfluidic technology and artificial intelligence in point-of-care medical applications. We believe that the noninvasive capabilities of these technologies have great potential in the development of diagnostics and can influence treatment options, thereby advancing precision disease management.
Collapse
Affiliation(s)
- Yatian Fu
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Yiyuan Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Bee Luan Khoo
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| |
Collapse
|
26
|
Al-Zaiti S, Besomi L, Bouzid Z, Faramand Z, Frisch S, Martin-Gill C, Gregg R, Saba S, Callaway C, Sejdić E. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nat Commun 2020; 11:3966. [PMID: 32769990 PMCID: PMC7414145 DOI: 10.1038/s41467-020-17804-2] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 07/16/2020] [Indexed: 11/30/2022] Open
Abstract
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.
Collapse
Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Division of Cardiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Lucas Besomi
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zeineb Bouzid
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziad Faramand
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephanie Frisch
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christian Martin-Gill
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Richard Gregg
- Advanced Algorithms Development Research Center, Philips Healthcare, Andover, MA, USA
| | - Samir Saba
- Division of Cardiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Clifton Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Intelligent Systems, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|
27
|
Gruson D, Bernardini S, Dabla PK, Gouget B, Stankovic S. Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine. Clin Chim Acta 2020; 509:67-71. [PMID: 32505771 DOI: 10.1016/j.cca.2020.06.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022]
Abstract
Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enablecost-effectiveness with reducereadmissionand mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.
Collapse
Affiliation(s)
- Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Pôle de recherche en Endocrinologie, Diabète et Nutrition, Institut de Recherche Expérimentale et Clinique, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy.
| | - Sergio Bernardini
- Department of Experimental Medicine, University of Tor Vergata, Rome, Italy; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Pradeep Kumar Dabla
- Department of Biochemistry, G.B Pant Institute of Postgraduate Medical Education & Research, Associated to Maulana Azad Medical College, New Delhi, India; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Bernard Gouget
- President-Healthcare Division Committee, Comité Français d'accréditation (Cofrac), 75012 Paris, France; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Sanja Stankovic
- Center for Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| |
Collapse
|
28
|
|
29
|
Zeron RMC, Serrano Junior CV. Artificial intelligence in the diagnosis of cardiovascular disease. ACTA ACUST UNITED AC 2020; 65:1438-1441. [PMID: 31994622 DOI: 10.1590/1806-9282.65.12.1438] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 06/02/2019] [Indexed: 05/30/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enabling cost-effectiveness, and reducing readmission and mortality rates. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.
Collapse
|
30
|
Long G, Yang C. A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock. Mol Med Rep 2020; 21:1561-1571. [PMID: 32016447 PMCID: PMC7003034 DOI: 10.3892/mmr.2020.10959] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 11/12/2019] [Indexed: 11/06/2022] Open
Abstract
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms of PSS using four microarray datasets (GSE26378, GSE26440, GSE13904 and GSE4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease‑associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.
Collapse
Affiliation(s)
- Guoli Long
- Department of The Intensive Care Unit, Eastern Hospital, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610101, P.R. China
| | - Chen Yang
- Department of The Intensive Care Unit, Eastern Hospital, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610101, P.R. China
| |
Collapse
|
31
|
Martin-Isla C, Campello VM, Izquierdo C, Raisi-Estabragh Z, Baeßler B, Petersen SE, Lekadir K. Image-Based Cardiac Diagnosis With Machine Learning: A Review. Front Cardiovasc Med 2020; 7:1. [PMID: 32039241 PMCID: PMC6992607 DOI: 10.3389/fcvm.2020.00001] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/06/2020] [Indexed: 01/28/2023] Open
Abstract
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
Collapse
Affiliation(s)
- Carlos Martin-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Victor M Campello
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Cristian Izquierdo
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Bettina Baeßler
- Department of Diagnostic & Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Steffen E Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| |
Collapse
|
32
|
Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification. PLoS One 2019; 14:e0223682. [PMID: 31600296 PMCID: PMC6786558 DOI: 10.1371/journal.pone.0223682] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 09/25/2019] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES An extended-wavelength diffuse reflectance spectroscopy (EWDRS) technique was evaluated for its ability to differentiate between and classify different skin and tissue types in an in vivo pig model. MATERIALS AND METHODS EWDRS recordings (450-1550 nm) were made on skin with different degrees of pigmentation as well as on the pig snout and tongue. The recordings were used to train a support vector machine to identify and classify the different skin and tissue types. RESULTS The resulting EWDRS curves for each skin and tissue type had a unique profile. The support vector machine was able to classify each skin and tissue type with an overall accuracy of 98.2%. The sensitivity and specificity were between 96.4 and 100.0% for all skin and tissue types. CONCLUSION EWDRS can be used in vivo to differentiate between different skin and tissue types with good accuracy. Further development of the technique may potentially lead to a novel diagnostic tool for e.g. non-invasive tumor margin delineation.
Collapse
|
33
|
Affiliation(s)
- Jan D'hooge
- Department of Cardiovascular Sciences, University of Leuven (KU Leuven), Belgium (J.D'h., A.G.F.).,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway (J.D'h.)
| | - Alan G Fraser
- Department of Cardiovascular Sciences, University of Leuven (KU Leuven), Belgium (J.D'h., A.G.F.).,School of Medicine, Cardiff University, United Kingdom (A.G.F.)
| |
Collapse
|
34
|
Noh YK, Park JY, Choi BG, Kim KE, Rha SW. A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. J Med Syst 2019; 43:253. [PMID: 31254109 DOI: 10.1007/s10916-019-1359-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 05/30/2019] [Indexed: 11/28/2022]
Abstract
The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in those patients presenting early-stage angina-like symptom using machine learning algorithms. We obtained data from 2344 ACS patients, who required revascularization and from 3538 non-ACS patients. We analyzed 20 features that are relevant to ACS using standard algorithms, support vector machines and linear discriminant analysis. Based on feature pattern and filter characteristics, we analyzed and extracted a strong prediction function out of the 20 selected features. The obtained prediction functions are relevant showing the area under curve of 0.860 for the prediction of ACS that requiring revascularization. Some features are missing in many data though they are considered to be very informative; it turned out that omitting those features from the input and using more data without those features for training improves the prediction accuracy. Additionally, from the investigation using the receiver operating characteristic curves, a reliable prediction of 2.60% of non-ACS patients could be made with a specificity of 1.0. For those 2.60% non-ACS patients, we can consider the recommendation of medical treatment without risking misdiagnosis of the patients requiring revascularization. We investigated prediction algorithm to select ACS patients requiring revascularization and non-ACS patients presenting angina-like symptoms at an early stage. In the future, a large cohort study is necessary to increase the prediction accuracy and confirm the possibility of safely discriminating the non-ACS patients from the ACS patients with confidence.
Collapse
Affiliation(s)
- Yung-Kyun Noh
- Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Ji Young Park
- Division of Cardiology, Nowon Eulji Hospital, Eulji University, 68 Hangeulbiseok-ro, Nowon-gu, Seoul, 01830, South Korea
| | - Byoung Geol Choi
- Cardiovascular Center, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
| | - Kee-Eung Kim
- School of Computing, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea
| | - Seung-Woon Rha
- Cardiovascular Center, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea.
| |
Collapse
|
35
|
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019; 110:12-22. [PMID: 30763612 DOI: 10.1016/j.jclinepi.2019.02.004] [Citation(s) in RCA: 812] [Impact Index Per Article: 162.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 01/18/2019] [Accepted: 02/05/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature. STUDY DESIGN AND SETTING We conducted a Medline literature search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes. RESULTS We included 71 of 927 studies. The median sample size was 1,250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and eight events per predictor (range 0.3-6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52-0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, -0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20-0.47) higher for ML. CONCLUSION We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.
Collapse
Affiliation(s)
- Evangelia Christodoulou
- Department of Development & Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000 Belgium
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2333 ZA The Netherlands
| | - Jan Y Verbakel
- Department of Development & Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000 Belgium; Department of Public Health & Primary Care, KU Leuven, Kapucijnenvoer 33J box 7001, Leuven, 3000 Belgium; Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford, OX2 6GG UK
| | - Ben Van Calster
- Department of Development & Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000 Belgium; Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2333 ZA The Netherlands.
| |
Collapse
|
36
|
|
37
|
Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine Learning Approaches in Cardiovascular Imaging. Circ Cardiovasc Imaging 2017; 10:CIRCIMAGING.117.005614. [PMID: 28956772 DOI: 10.1161/circimaging.117.005614] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.
Collapse
Affiliation(s)
- Mir Henglin
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.).
| | - Gillian Stein
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Pavel V Hushcha
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Jasper Snoek
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Alexander B Wiltschko
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Susan Cheng
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| |
Collapse
|
38
|
Zhu Y, Liu D, Grosu R, Wang X, Duan H, Wang G. A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres. SENSORS (BASEL, SWITZERLAND) 2017; 17:E2049. [PMID: 28880228 PMCID: PMC5620658 DOI: 10.3390/s17092049] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 09/04/2017] [Accepted: 09/05/2017] [Indexed: 11/16/2022]
Abstract
Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates "hyperspheres" to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods.
Collapse
Affiliation(s)
- Yungang Zhu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
| | - Dayou Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
| | - Radu Grosu
- Department of Computer Engineering, Vienna University of Technology, Vienna 1040, Austria.
| | - Xinhua Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
- State Key Laboratory of Applied Optics, Chinese Academy of Sciences, Changchun 130012, China.
| | - Hongying Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
| | - Guodong Wang
- Department of Computer Engineering, Vienna University of Technology, Vienna 1040, Austria.
| |
Collapse
|
39
|
González-Robledo J, Martín-González F, Sánchez-Barba M, Sánchez-Hernández F, Moreno-García MN. Multiclassifier Systems for Predicting Neurological Outcome of Patients with Severe Trauma and Polytrauma in Intensive Care Units. J Med Syst 2017; 41:136. [PMID: 28755271 DOI: 10.1007/s10916-017-0789-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 07/23/2017] [Indexed: 11/28/2022]
Abstract
This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the University Hospital in Salamanca. Predictive models were induced from both epidemiologic and clinical variables taken at the emergency room and along the stay in the ICU. The large number of variables leads to a low accuracy in the classifiers even when feature selection methods are used. In addition, the presence of a much larger number of instances of one of the classes in the subgroup of TBI patients produces a significantly lesser precision for the minority class. Usual ways of dealing with the last problem is to use undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively. Our proposal for dealing with these problems is based in the use of ensemble multiclassifiers as well as in the use of an ensemble playing the role of base classifier in multiclassifiers. The proposed strategy gave the best values of the selected quality measures (accuracy, precision, sensitivity, specificity, F-measure and area under the Receiver Operator Characteristic curve) as well as the closest values of precision for the two classes under study in the case of the classification from imbalanced data.
Collapse
Affiliation(s)
| | | | | | - Fernando Sánchez-Hernández
- School of Nursing and Physiotherapy, Prehospital Emergency Services, University of Salamanca, Salamanca, Spain
| | | |
Collapse
|
40
|
Brown AD, Marotta TR. A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization. Acad Radiol 2017; 24:160-166. [PMID: 27889399 DOI: 10.1016/j.acra.2016.09.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 09/20/2016] [Accepted: 09/22/2016] [Indexed: 01/31/2023]
Abstract
RATIONALE AND OBJECTIVES Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. MATERIALS AND METHODS To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve. RESULTS Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling. CONCLUSIONS NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations.
Collapse
|
41
|
Wang S, Yang M, Du S, Yang J, Liu B, Gorriz JM, Ramírez J, Yuan TF, Zhang Y. Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning. Front Comput Neurosci 2016; 10:106. [PMID: 27807415 PMCID: PMC5069288 DOI: 10.3389/fncom.2016.00106] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 09/28/2016] [Indexed: 12/17/2022] Open
Abstract
HighlightsWe develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging. Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems. The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls.
Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.
Collapse
Affiliation(s)
- Shuihua Wang
- School of Electronic Science and Engineering, Nanjing UniversityNanjing, China; School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademyChangsha, China
| | - Ming Yang
- Department of Radiology, Nanjing Children's Hospital, Nanjing Medical UniversityNanjing, China; Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University, Quanzhou Normal UniversityQuanzhou, China
| | - Sidan Du
- School of Electronic Science and Engineering, Nanjing University Nanjing, China
| | - Jiquan Yang
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing, China
| | - Bin Liu
- Department of Radiology, Zhong-Da Hospital of Southeast University Nanjing, China
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain
| | - Ti-Fei Yuan
- School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; State Key Lab of CAD & CG, Zhejiang UniversityHangzhou, China
| | - Yudong Zhang
- School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Key Laboratory of Statistical Information Technology and Data Mining, State Statistics BureauChengdu, China
| |
Collapse
|
42
|
Aydın M, Hardalaç F, Ural B, Karap S. Neonatal Jaundice Detection System. J Med Syst 2016; 40:166. [PMID: 27229489 DOI: 10.1007/s10916-016-0523-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 05/09/2016] [Indexed: 11/27/2022]
Abstract
Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %.
Collapse
Affiliation(s)
- Mustafa Aydın
- Pediatrics-Neonatology, Fırat University, Elazig, Turkey
| | - Fırat Hardalaç
- Electrical Electronics Engineering, Gazi University, Ankara, Turkey.
| | - Berkan Ural
- Electrical Electronics Engineering, Gazi University, Ankara, Turkey
| | - Serhat Karap
- Electrical Electronics Engineering, Gazi University, Ankara, Turkey
| |
Collapse
|
43
|
Peker M. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. J Med Syst 2016; 40:116. [DOI: 10.1007/s10916-016-0477-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 03/15/2016] [Indexed: 11/28/2022]
|