1
|
Mai HN, Win TT, Kim HS, Pae A, Att W, Nguyen DD, Lee DH. Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance. J Prosthodont 2024. [PMID: 39010644 DOI: 10.1111/jopr.13900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 06/06/2024] [Indexed: 07/17/2024] Open
Abstract
PURPOSE This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts. MATERIALS AND METHODS This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors' significance and contributions. RESULTS The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors. CONCLUSIONS This study sheds light on the factors influencing dental professionals' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.
Collapse
Affiliation(s)
- Hang-Nga Mai
- Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea
- Hanoi University of Business and Technology, Hanoi, Vietnam
| | - Thaw Thaw Win
- Department of Prosthodontics, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
| | - Hyeong-Seob Kim
- Department of Prosthodontics, Kyung Hee University College of Dentistry, Kyung Hee University Medical Center, Seoul, Republic of Korea
| | - Ahran Pae
- Department of Prosthodontics, Kyung Hee University College of Dentistry, Kyung Hee University Medical Center, Seoul, Republic of Korea
| | - Wael Att
- Center for Dental Medicine, Department of Prosthetic Dentistry, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Dang Dinh Nguyen
- Department of Prosthodontics, University of Iowa College of Dentistry and Dental Clinics, Iowa City, Iowa, USA
| | - Du-Hyeong Lee
- Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea
- Department of Prosthodontics, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
- The Face Dental Group, Boston, Massachusetts, USA
| |
Collapse
|
2
|
Bishop AJ, Nehme Z, Nanayakkara S, Anderson D, Stub D, Meadley BN. Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review. Am J Emerg Med 2024; 83:1-8. [PMID: 38936320 DOI: 10.1016/j.ajem.2024.06.026] [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: 04/28/2024] [Revised: 06/13/2024] [Accepted: 06/22/2024] [Indexed: 06/29/2024] Open
Abstract
INTRODUCTION The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria show promise in improving early ACO diagnosis, but complexity hinders widespread adoption. The potential integration of Artificial Neural Networks (ANN) holds promise for enhancing diagnostic accuracy and addressing reliability issues in ECG interpretation for ACO symptoms. METHODS Ovid MEDLINE, CINAHL, EMBASE, Cochrane, PubMed and Scopus were searched from inception through to 8th of December 2023. A thorough search of the grey literature and reference lists of relevant articles was also performed to identify additional studies. Articles were included if they reported the use of ANN for ECG interpretation of Acute Coronary Syndrome in the Emergency Department patients. RESULTS The search yielded a total of 244 articles. After removing duplicates and excluding non-relevant articles, 14 remained for analysis. There was significant heterogeneity in the types of ANN models used and the outcomes assessed, making direct comparisons challenging. Nevertheless, ANN appeared to demonstrate higher accuracy than physician interpreters for the evaluated outcomes and this proved independent of both specialty and years of experience. CONCLUSIONS The interpretation of ECGs in patients with suspected ACS using ANN appears to be accurate and potentially superior when compared to human interpreters and computerised algorithms. This appears consistent across various ANN models and outcome variables. Future investigations should emphasise ANN interpretation of ECGs in patients with ACO, where rapid and accurate diagnosis can significantly benefit patients through timely access to reperfusion therapies.
Collapse
Affiliation(s)
- Andrew J Bishop
- Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia.
| | - Ziad Nehme
- Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia; School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shane Nanayakkara
- Department of Cardiology, Alfred Health, Melbourne, Victoria, Australia; Department of Cardiology, Cabrini Hospital, Melbourne, Victoria, Australia; Monash-Alfred-Baker Centre for Cardiovascular Research, Monash University, Melbourne, Victoria, Australia
| | - David Anderson
- Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia; School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dion Stub
- Ambulance Victoria, Doncaster, Victoria, Australia; School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Department of Cardiology, Alfred Health, Melbourne, Victoria, Australia
| | - Benjamin N Meadley
- Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia
| |
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
|
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: 0] [Impact Index Per Article: 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
|
5
|
Yamasawa D, Ozawa H, Goto S. The Importance of Interpretability and Validations of Machine-Learning Models. Circ J 2023; 88:157-158. [PMID: 38057101 DOI: 10.1253/circj.cj-23-0857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Affiliation(s)
| | - Hideki Ozawa
- Department of Medicine, Tokai University School of Medicine
| | - Shinichi Goto
- Department of Medicine, Tokai University School of Medicine
| |
Collapse
|
6
|
Miura K, Yagi R, Miyama H, Kimura M, Kanazawa H, Hashimoto M, Kobayashi S, Nakahara S, Ishikawa T, Taguchi I, Sano M, Sato K, Fukuda K, Deo RC, MacRae CA, Itabashi Y, Katsumata Y, Goto S. Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study. EClinicalMedicine 2023; 63:102141. [PMID: 37753448 PMCID: PMC10518511 DOI: 10.1016/j.eclinm.2023.102141] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 09/28/2023] Open
Abstract
Background Atrial septal defect (ASD) increases the risk of adverse cardiovascular outcomes. Despite the potential for risk mitigation through minimally invasive percutaneous closure, ASD remains underdiagnosed due to subtle symptoms and examination findings. To bridge this diagnostic gap, we propose a novel screening strategy aimed at early detection and enhanced diagnosis through the implementation of a convolutional neural network (CNN) to identify ASD from 12-lead electrocardiography (ECG). Methods ECGs were collected from patients with at least one recorded echocardiogram at 3 hospitals from 2 continents (Keio University Hospital from July 2011 to December 2020, Brigham and Women's Hospital from January 2015 to December 2020, and Dokkyo Medical University Saitama Medical Center from January 2010 and December 2021). ECGs from patients with a diagnosis of ASD were labeled as positive cases while the remainder were labeled as negative. ECGs after the closure of ASD were excluded. After randomly splitting the ECGs into 3 datasets (50% derivation, 20% validation, and 30% test) with no patient overlap, a CNN-based model was trained using the derivation datasets from 2 hospitals and was tested on held-out datasets along with an external validation on the 3rd hospital. All eligible ECGs were used for derivation and validation whereas the earliest ECG for each patient was used for the test and external validation. The discrimination of ASD was assessed by the area under the receiver operating characteristic curve (AUROC). Multiple subgroups were examined to identify any heterogeneity. Findings A total of 671,201 ECGs from 80,947 patients were collected from the 3 institutions. The AUROC for detecting ASD was 0.85-0.90 across the 3 hospitals. The subgroup analysis showed excellent performance across various characteristics Screening simulation using the model greatly increased sensitivity from 80.6% to 93.7% at specificity 33.6% when compared to using overt ECG abnormalities. Interpretation A CNN-based model using 12-lead ECG successfully identified the presence of ASD with excellent generalizability across institutions from 2 separate continents. Funding This work was supported by research grants from JST (JPMJPF2101), JSR corporation, Taiju Life Social Welfare Foundation, Kondou Kinen Medical Foundation, Research fund of Mitsukoshi health and welfare foundation, Tokai University School of Medicine Project Research and Internal Medicine Project Research, Secom Science and Technology Foundation, and Grants from AMED (JP23hma922012 and JP23ym0126813). This work was partially supported by One Brave Idea, co-funded by the American Heart Association and Verily with significant support from AstraZeneca and pillar support from Quest Diagnostics.
Collapse
Affiliation(s)
- Kotaro Miura
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ryuichiro Yagi
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Harvard Medical School, Boston, MA, USA
| | - Hiroshi Miyama
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Mai Kimura
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Hideaki Kanazawa
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Hashimoto
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Sayuki Kobayashi
- Department of Cardiology, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Shiro Nakahara
- Department of Cardiology, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Tetsuya Ishikawa
- Department of Cardiology, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Isao Taguchi
- Department of Cardiology, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Motoaki Sano
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Kazuki Sato
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Keiichi Fukuda
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Rahul C. Deo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Harvard Medical School, Boston, MA, USA
| | - Calum A. MacRae
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Harvard Medical School, Boston, MA, USA
| | - Yuji Itabashi
- Department of Cardiology, Dokkyo Medical University Saitama Medical Center, Saitama, Japan
| | - Yoshinori Katsumata
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shinichi Goto
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine & Family Medicine, Department of General and Acute Medicine, Tokai University School of Medicine, Isehara, Japan
| |
Collapse
|
7
|
Nakayama M, Goto S, Sakano T, Goto S. Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population. J Atheroscler Thromb 2023; 30:1002-1009. [PMID: 36273901 PMCID: PMC10406660 DOI: 10.5551/jat.63798] [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: 06/29/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023] Open
Abstract
AIMS Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. METHODS Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. RESULTS AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). CONCLUSION Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.
Collapse
Affiliation(s)
- Masamitsu Nakayama
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| | - Shinichi Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| | | | - Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| |
Collapse
|
8
|
Nakamura T, Aiba T, Shimizu W, Furukawa T, Sasano T. Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence. Circ J 2023; 87:1007-1014. [PMID: 36372400 DOI: 10.1253/circj.cj-22-0496] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
BACKGROUND Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model can predict a previous or future ventricular fibrillation (VF) episode from a Brugada ECG.Methods and Results: We developed an AI-enabled algorithm using a convolutional neural network. From 157 patients with suspected Brugada syndrome, 2,053 ECGs were obtained, and the dataset was divided into 5 datasets for cross-validation. In the ECG-based evaluation, the precision, recall, and F1score were 0.79±0.09, 0.73±0.09, and 0.75±0.09, respectively. The average area under the receiver-operating characteristic curve (AUROC) was 0.81±0.09. On per-patient evaluation, the AUROC was 0.80±0.07. This model predicted the presence of VF with a precision of 0.93±0.02, recall of 0.77±0.14, and F1score of 0.81±0.11. The negative predictive value was 0.94±0.11 while its positive predictive value was 0.44±0.29. CONCLUSIONS This proof-of-concept study showed that an AI-enabled algorithm can predict the presence of VF with a substantial performance. It implies that the AI model may detect a subtle ECG change that is undetectable by humans.
Collapse
Affiliation(s)
- Tomofumi Nakamura
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University
| | - Takeshi Aiba
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center
| | - Wataru Shimizu
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Tetsushi Furukawa
- Department of Bio-informational Pharmacology, Medical Research Institute, Tokyo Medical and Dental University
| | - Tetsuo Sasano
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University
| |
Collapse
|
9
|
Shiraishi Y, Goto S, Niimi N, Katsumata Y, Goda A, Takei M, Saji M, Sano M, Fukuda K, Kohno T, Yoshikawa T, Kohsaka S. Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography. Europace 2023; 25:922-930. [PMID: 36610062 PMCID: PMC10062335 DOI: 10.1093/europace/euac261] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/10/2022] [Indexed: 01/09/2023] Open
Abstract
AIMS Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional predictors of SCD would improve the SCD stratification among HF patients. METHODS AND RESULTS In a prospective observational study, 4 tertiary care hospitals in Tokyo enrolled 2559 patients hospitalized for HF who were successfully discharged after acute decompensation. The ECG data during the index hospitalization were extracted from the hospitals' electronic medical record systems. The association of the ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The ECG-AI index plus classical predictive guidelines (i.e. LVEF ≤35%, NYHA Class II and III) significantly improved the discriminative value of SCD [receiver operating characteristic area under the curve (ROC-AUC), 0.66 vs. 0.59; P = 0.017; Delong's test] with good calibration (P = 0.11; Hosmer-Lemeshow test) and improved net reclassification [36%; 95% confidence interval (CI), 9-64%; P = 0.009]. The Fine-Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% CI, 1.04-1.49; P = 0.015). An increased proportional risk of SCD vs. non-SCD with an increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7%; P for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischaemic aetiology and an LVEF of >35%. CONCLUSION To improve risk stratification of SCD, ECG-based AI may provide additional values in the management of patients with HF.
Collapse
Affiliation(s)
- Yasuyuki Shiraishi
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan
| | - Shinichi Goto
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Nozomi Niimi
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan
| | - Yoshinori Katsumata
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ayumi Goda
- Department of Cardiovascular Medicine, Kyorin University Faculty of Medicine, Tokyo, Japan
| | - Makoto Takei
- Department of Cardiology, Saiseikai Central Hospital, Tokyo, Japan
| | - Mike Saji
- Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Motoaki Sano
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan
| | - Keiichi Fukuda
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan
| | - Takashi Kohno
- Department of Cardiovascular Medicine, Kyorin University Faculty of Medicine, Tokyo, Japan
| | | | - Shun Kohsaka
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan
| |
Collapse
|
10
|
Jin BT, Palleti R, Shi S, Ng AY, Quinn JV, Rajpurkar P, Kim D. Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography. J Am Med Inform Assoc 2022; 29:1908-1918. [PMID: 35994003 PMCID: PMC9552286 DOI: 10.1093/jamia/ocac135] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/26/2022] [Accepted: 08/03/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Chest pain is common, and current risk-stratification methods, requiring 12-lead electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly resourced settings. Our objective was to predict myocardial injury using continuous single-lead ECG waveforms similar to those obtained from wearable devices and to evaluate the potential of transfer learning from labeled 12-lead ECGs to improve these predictions. METHODS We studied 10 874 Emergency Department (ED) patients who received continuous ECG monitoring and troponin testing from 2020 to 2021. We defined myocardial injury as newly elevated troponin in patients with chest pain or shortness of breath. We developed deep learning models of myocardial injury using continuous lead II ECG from bedside monitors as well as conventional 12-lead ECGs from triage. We pretrained single-lead models on a pre-existing corpus of labeled 12-lead ECGs. We compared model predictions to those of ED physicians. RESULTS A transfer learning strategy, whereby models for continuous single-lead ECGs were first pretrained on 12-lead ECGs from a separate cohort, predicted myocardial injury as accurately as models using patients' own 12-lead ECGs: area under the receiver operating characteristic curve 0.760 (95% confidence interval [CI], 0.721-0.799) and area under the precision-recall curve 0.321 (95% CI, 0.251-0.397). Models demonstrated a high negative predictive value for myocardial injury among patients with chest pain or shortness of breath, exceeding the predictive performance of ED physicians, while attending to known stigmata of myocardial injury. CONCLUSIONS Deep learning models pretrained on labeled 12-lead ECGs can predict myocardial injury from noisy, continuous monitor data early in a patient's presentation. The utility of continuous single-lead ECG in the risk stratification of chest pain has implications for wearable devices and preclinical settings, where external validation of the approach is needed.
Collapse
Affiliation(s)
- Boyang Tom Jin
- Department of Computer Science, Stanford University, Palo Alto, California, USA
| | - Raj Palleti
- Department of Computer Science, Stanford University, Palo Alto, California, USA
| | - Siyu Shi
- Department of Medicine, Stanford University, Palo Alto, California, USA
| | - Andrew Y Ng
- Department of Computer Science, Stanford University, Palo Alto, California, USA
| | - James V Quinn
- Department of Emergency Medicine, Stanford University, Palo Alto, California, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts, USA
| | - David Kim
- Department of Emergency Medicine, Stanford University, Palo Alto, California, USA
| |
Collapse
|
11
|
Al-Zaiti S, Macleod R, Dam PV, Smith SW, Birnbaum Y. Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities. J Electrocardiol 2022; 74:65-72. [PMID: 36027675 DOI: 10.1016/j.jelectrocard.2022.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/13/2022]
Abstract
Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.
Collapse
Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Robert Macleod
- Department of Biomedical Engineering, University of Utah, Salt Lake, UT, USA
| | - Peter Van Dam
- Department of Cardiology, University Medical Center Utrecht, the Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare and University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
| |
Collapse
|
12
|
Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
Collapse
Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
| |
Collapse
|
13
|
Katoh T, Yashima M, Takahashi N, Watanabe E, Ikeda T, Kasamaki Y, Sumitomo N, Ueda N, Morita H, Hiraoka M. Expert consensus document on automated diagnosis of the electrocardiogram: The task force on automated diagnosis of the electrocardiogram in Japan: Part 2: Current status of inappropriate automated diagnosis is widely used electrocardiographs in Japan. J Arrhythm 2021; 37:1427-1433. [PMID: 34887946 PMCID: PMC8637077 DOI: 10.1002/joa3.12646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- Takao Katoh
- Clinic of Tobu Railway Co. Ltd.Sumida‐kuJapan
- Nippon Medical School Department of CardiologyBunkyo‐kuJapan
| | - Masaaki Yashima
- Nippon Medical School Department of CardiologyBunkyo‐kuJapan
| | - Naohiko Takahashi
- Oita University Department of Cardiology and Clinical ExaminationYufuJapan
| | | | | | - Yuji Kasamaki
- Kanazawa Medical University Himi Medical CenterHimiJapan
| | - Naokata Sumitomo
- Saitama Medical University International Medical CenterSaitamaJapan
| | - Norihiro Ueda
- Nagoya City University Department of Medical EducationNagoyaJapan
| | - Hiroshi Morita
- Graduate School of Medicine Dentistry and Pharmaceutical SciencesOkayama UniversityOkayamaJapan
| | | |
Collapse
|
14
|
Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
Collapse
|
15
|
Tadesse GA, Javed H, Weldemariam K, Liu Y, Liu J, Chen J, Zhu T. DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time. Artif Intell Med 2021; 121:102192. [PMID: 34763807 DOI: 10.1016/j.artmed.2021.102192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/07/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022]
Abstract
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from Normal cases as well as identifying the time-occurrence of MI (defined as Acute, Recent and Old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify Normal cases as well as Acute, Recent and Old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.
Collapse
Affiliation(s)
- Girmaw Abebe Tadesse
- Department of Engineering, University of Oxford, Oxford, United Kingdom; IBM Research, Kenya.
| | - Hamza Javed
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | | | - Yong Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jin Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jiyan Chen
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Tingting Zhu
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
16
|
Goto S, McGuire DK, Goto S. The Future Role of High-Performance Computing in Cardiovascular Medicine and Science -Impact of Multi-Dimensional Data Analysis. J Atheroscler Thromb 2021; 29:559-562. [PMID: 34602525 PMCID: PMC9135644 DOI: 10.5551/jat.rv17062] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Advances in High-performance computing (HPC) technology have reached the capacity to inform cardiovascular (CV) science in the realm of both inductive and constructive approaches. Clinical trials allow for the comparison of the effect of an intervention without the need to understand the mechanism. This is a typical example of an inductive approach. In the HPC field, training an artificial intelligence (AI) model, constructed by neural networks, to predict future CV events with the use of large scale multi-dimensional datasets is the counterpart that may rely on as well as inform understanding of mechanistic underpinnings for optimization. However, in contrast to clinical trials, AI can calculate event risk at the individual level and has the potential to inform and refine the application of personalized medicine. Despite this clear strength, results from AI analyses may identify otherwise unidentified/unexpected (i.e. non-intuitive) relationships between multi-dimensional data and clinical outcomes that may further unravel potential mechanistic pathways and identify potential therapeutic targets, therebycontributing to the parsing of observational associations from causal links. The constructive approach will remain critical to overcome limitations of existing knowledge and anchored biases to actualize a more sophisticated understanding of the complex pathobiology of CV diseases. HPC technology has the potential to underpin this constructive approach in CV basic and clinical science. In general, even complex biological phenomena can be reduced to combinations of simple biological/chemical/physical laws. In the deductive approach, the focus/intent is to explain complex CV diseases by combinations of simple principles.
Collapse
Affiliation(s)
- Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine
| | - Darren K McGuire
- Department of Internal Medicine, Division of Cardiology University of Texas Southwestern Medical Center and Parkland Health and Hospital System
| | - Shinichi Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine
| |
Collapse
|
17
|
Doran S, Arif M, Lam S, Bayraktar A, Turkez H, Uhlen M, Boren J, Mardinoglu A. Multi-omics approaches for revealing the complexity of cardiovascular disease. Brief Bioinform 2021; 22:bbab061. [PMID: 33725119 PMCID: PMC8425417 DOI: 10.1093/bib/bbab061] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/20/2021] [Accepted: 02/05/2021] [Indexed: 02/06/2023] Open
Abstract
The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.
Collapse
Affiliation(s)
- Stephen Doran
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Abdulahad Bayraktar
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| |
Collapse
|
18
|
Hur S, Min JY, Yoo J, Kim K, Chung CR, Dykes PC, Cha WC. Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study. J Med Internet Res 2021; 23:e23508. [PMID: 34382940 PMCID: PMC8387891 DOI: 10.2196/23508] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 10/19/2020] [Accepted: 07/13/2021] [Indexed: 12/23/2022] Open
Abstract
Background Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. Objective This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. Methods This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve. Results Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740. Conclusions We successfully developed and validated machine learning–based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.
Collapse
Affiliation(s)
- Sujeong Hur
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.,Department of Patient Experience Management, Samsung Medical Center, Seoul, Republic of Korea
| | - Ji Young Min
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Junsang Yoo
- Department of Nursing, College of Nursing, Sahmyook University, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.,Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Chi Ryang Chung
- Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Patricia C Dykes
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Won Chul Cha
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.,Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea
| |
Collapse
|
19
|
Al Hinai G, Jammoul S, Vajihi Z, Afilalo J. Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:416-423. [PMID: 34604757 PMCID: PMC8482047 DOI: 10.1093/ehjdh/ztab048] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/14/2021] [Indexed: 01/31/2023]
Abstract
The aim of this review was to assess the evidence for deep learning (DL) analysis of resting electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular (LV) systolic dysfunction, myocardial hypertrophy, and ischaemic heart disease. A systematic literature search was conducted to identify published original articles on end-to-end DL analysis of resting ECG signals for the detection of structural cardiac pathologies. Studies were excluded if the ECG was acquired by ambulatory, stress, intracardiac, or implantable devices, and if the pathology of interest was arrhythmic in nature. After duplicate reviewers screened search results, 12 articles met the inclusion criteria and were included. Three articles used DL to detect LV systolic dysfunction, achieving an area under the curve (AUC) of 0.89-0.93 and an accuracy of 98%. One study used DL to detect LV hypertrophy, achieving an AUC of 0.87 and an accuracy of 87%. Six articles used DL to detect acute myocardial infarction, achieving an AUC of 0.88-1.00 and an accuracy of 83-99.9%. Two articles used DL to detect stable ischaemic heart disease, achieving an accuracy of 95-99.9%. Deep learning models, particularly those that used convolutional neural networks, outperformed rules-based models and other machine learning models. Deep learning is a promising technique to analyse resting ECG signals for the detection of structural cardiac pathologies, which has clinical applicability for more effective screening of asymptomatic populations and expedited diagnostic work-up of symptomatic patients at risk for cardiovascular disease.
Collapse
Affiliation(s)
- Ghalib Al Hinai
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
| | - Samer Jammoul
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
| | - Zara Vajihi
- Department of Emergency Medicine, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, H-126, Montreal, QC H3T 1E2, Canada
| | - Jonathan Afilalo
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada,Centre for Clinical Epidemiology, Jewish General Hospital, 3755 Cote Ste Catherine Rd, H-411, Montreal, QC H3T 1E2, Canada,Corresponding author. Tel: (514) 340-7540, Fax: (514) 340-7534,
| |
Collapse
|
20
|
Mori H, Inai K, Sugiyama H, Muragaki Y. Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning. Pediatr Cardiol 2021; 42:1379-1387. [PMID: 33907875 DOI: 10.1007/s00246-021-02622-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
The heart murmur associated with atrial septal defects is often faint and can thus only be detected by chance. Although electrocardiogram examination can prompt diagnoses, identification of specific findings remains a major challenge. We demonstrate improved diagnostic accuracy realized by incorporating a proposed deep learning model, comprising a convolutional neural network (CNN) and long short-term memory (LSTM), with electrocardiograms. This retrospective observational study included 1192 electrocardiograms of 728 participants from January 1, 2000, to December 31, 2017, at Tokyo Women's Medical University Hospital. Using echocardiography, we confirmed the status of healthy subjects-no structural heart disease-and the diagnosis of atrial septal defects in patients. We used a deep learning model comprising a CNN and LTSMs. All pediatric cardiologists (n = 12) were blinded to patient groupings when analyzing them by electrocardiogram. Using electrocardiograms, the model's diagnostic ability was compared with that of pediatric cardiologists. We assessed 1192 electrocardiograms (828 normally structured hearts and 364 atrial septal defects) pertaining to 792 participants. The deep learning model results revealed that the accuracy, sensitivity, specificity, positive predictive value, and F1 score were 0.89, 0.76, 0.96, 0.88, and 0.81, respectively. The pediatric cardiologists (n = 12) achieved means of accuracy, sensitivity, specificity, positive predictive value, and F1 score of 0.58 ± 0.06, 0.53 ± 0.04, 0.67 ± 0.10, 0.69 ± 0.18, and 0.58 ± 0.06, respectively. The proposed method is a superior alternative to accurately diagnose atrial septal defects.
Collapse
Affiliation(s)
- Hiroki Mori
- Department of Pediatric Cardiology, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan.,Institute of Advanced BioMedical Engineering and Science, Tokyo Women's Medical University, Tokyo, 162-0054, Japan
| | - Kei Inai
- Department of Pediatric Cardiology, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan
| | - Hisashi Sugiyama
- Department of Pediatric Cardiology, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan
| | - Yoshihiro Muragaki
- Institute of Advanced BioMedical Engineering and Science, Tokyo Women's Medical University, Tokyo, 162-0054, Japan.
| |
Collapse
|
21
|
Ogasawara J, Ikenoue S, Yamamoto H, Sato M, Kasuga Y, Mitsukura Y, Ikegaya Y, Yasui M, Tanaka M, Ochiai D. Deep neural network-based classification of cardiotocograms outperformed conventional algorithms. Sci Rep 2021; 11:13367. [PMID: 34183748 PMCID: PMC8238938 DOI: 10.1038/s41598-021-92805-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 06/16/2021] [Indexed: 11/10/2022] Open
Abstract
Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH < 7.20 or Apgar score at 1 min < 7) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73 ± 0.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.
Collapse
Affiliation(s)
- Jun Ogasawara
- Department of Pharmacology, School of Medicine, Keio University, Tokyo, 160-8582, Japan
| | - Satoru Ikenoue
- Department of Obstetrics and Gynecology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hiroko Yamamoto
- Department of Systems Design Engineering, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan
| | - Motoshige Sato
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Yoshifumi Kasuga
- Department of Obstetrics and Gynecology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Yasue Mitsukura
- Department of Systems Design Engineering, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan
| | - Yuji Ikegaya
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan.,Institute for AI and Beyond, The University of Tokyo, Tokyo, 113-0033, Japan.,Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan
| | - Masato Yasui
- Department of Pharmacology, School of Medicine, Keio University, Tokyo, 160-8582, Japan
| | - Mamoru Tanaka
- Department of Obstetrics and Gynecology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Daigo Ochiai
- Department of Obstetrics and Gynecology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| |
Collapse
|
22
|
Shibutani H, Fujii K, Ueda D, Kawakami R, Imanaka T, Kawai K, Matsumura K, Hashimoto K, Yamamoto A, Hao H, Hirota S, Miki Y, Shiojima I. Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning. Atherosclerosis 2021; 328:100-105. [PMID: 34126504 DOI: 10.1016/j.atherosclerosis.2021.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/23/2021] [Accepted: 06/03/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND AND AIMS We developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imaging (OFDI) and performed quantitative and visual evaluations. METHODS A total of 1103 histological cross-sections from 45 autopsy hearts were examined to compare the ex vivo OFDI scans. The images were segmented and annotated considering four histological categories: pathological intimal thickening (PIT), fibrous cap atheroma (FA), fibrocalcific plaque (FC), and healed erosion/rupture (HER). The DL model was developed based on pyramid scene parsing network (PSPNet). Given an input image, a convolutional neural network (ResNet50) was used as an encoder to generate feature maps of the last convolutional layer. RESULTS For the quantitative evaluation, the mean F-score and IoU values, which are used to evaluate how close the predicted results are to the ground truth, were used. The validation and test dataset had F-score and IoU values of 0.63, 0.49, and 0.66, 0.52, respectively. For the section-level diagnostic accuracy, the areas under the receiver-operating characteristic curve produced by the DL model for FC, PIT, FA, and HER were 0.91, 0.85, 0.86, and 0.86, respectively, and were comparable to those of an expert observer. CONCLUSIONS DL semantic segmentation of coronary plaques in OFDI images was used as a tool to automatically categorize atherosclerotic plaques using histological findings as the gold standard. The proposed method can support interventional cardiologists in understanding histological properties of plaques.
Collapse
Affiliation(s)
- Hiroki Shibutani
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Kenichi Fujii
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan.
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Rika Kawakami
- Division of Surgical Pathology, Hyogo College of Medicine, Nishinomiya, Japan
| | - Takahiro Imanaka
- Division of Cardiovascular Medicine and Coronary Heart Disease, Hyogo College of Medicine, Nishinomiya, Japan
| | - Kenji Kawai
- Division of Cardiovascular Medicine and Coronary Heart Disease, Hyogo College of Medicine, Nishinomiya, Japan
| | - Koichiro Matsumura
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Kenta Hashimoto
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiroyuki Hao
- Division of Human Pathology, Department of Pathology and Microbiology, Nihon University School of Medicine, Tokyo, Japan
| | - Seiichi Hirota
- Division of Surgical Pathology, Hyogo College of Medicine, Nishinomiya, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Ichiro Shiojima
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| |
Collapse
|
23
|
Gnecchi M, Sala L, Schwartz PJ. Precision Medicine and cardiac channelopathies: when dreams meet reality. Eur Heart J 2021; 42:1661-1675. [PMID: 33686390 PMCID: PMC8088342 DOI: 10.1093/eurheartj/ehab007] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/10/2020] [Accepted: 01/12/2021] [Indexed: 12/17/2022] Open
Abstract
Precision Medicine (PM) is an innovative approach that, by relying on large populations’ datasets, patients’ genetics and characteristics, and advanced technologies, aims at improving risk stratification and at identifying patient-specific management through targeted diagnostic and therapeutic strategies. Cardiac channelopathies are being progressively involved in the evolution brought by PM and some of them are benefiting from these novel approaches, especially the long QT syndrome. Here, we have explored the main layers that should be considered when developing a PM approach for cardiac channelopathies, with a focus on modern in vitro strategies based on patient-specific human-induced pluripotent stem cells and on in silico models. PM is where scientists and clinicians must meet and integrate their expertise to improve medical care in an innovative way but without losing common sense. We have indeed tried to provide the cardiologist’s point of view by comparing state-of-the-art techniques and approaches, including revolutionary discoveries, to current practice. This point matters because the new approaches may, or may not, exceed the efficacy and safety of established therapies. Thus, our own eagerness to implement the most recent translational strategies for cardiac channelopathies must be tempered by an objective assessment to verify whether the PM approaches are indeed making a difference for the patients. We believe that PM may shape the diagnosis and treatment of cardiac channelopathies for years to come. Nonetheless, its potential superiority over standard therapies should be constantly monitored and assessed before translating intellectually rewarding new discoveries into clinical practice.
Collapse
Affiliation(s)
- Massimiliano Gnecchi
- Department of Cardiothoracic and Vascular Sciences-Coronary Care Unit and Laboratory of Clinical and Experimental Cardiology, Fondazione IRCCS Policlinico San Matteo, Viale Golgi 19, 27100 Pavia, Italy.,Department of Molecular Medicine, Unit of Cardiology, University of Pavia, Viale Golgi 19, 27100 Pavia, Italy.,Department of Medicine, University of Cape Town, J-Floor, Old Main Building, Groote Schuur Hospital, Observatory, 7925 Cape Town, South Africa
| | - Luca Sala
- Istituto Auxologico Italiano IRCCS, Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular Genetics, Via Pier Lombardo 22 - 20135 Milan, Italy
| | - Peter J Schwartz
- Istituto Auxologico Italiano IRCCS, Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular Genetics, Via Pier Lombardo 22 - 20135 Milan, Italy
| |
Collapse
|
24
|
Schultz MA, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Douthit BJ, Forbes T, Gao G, Lee MA, Lekan D, Wieben A, Jeffery AD. Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review. Comput Inform Nurs 2021; 39:654-667. [PMID: 34747890 PMCID: PMC8578863 DOI: 10.1097/cin.0000000000000705] [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] [Indexed: 11/26/2022]
Abstract
Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.
Collapse
Affiliation(s)
- Mary Anne Schultz
- Author Affiliations: California State University (Dr Schultz); Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University (Ms Walden); Department of Emergency Medicine, Columbia University School of Nursing (Dr Cato); Grand Valley State University (Dr Coviak); Global Health Technology & Informatics, Chevron, San Ramon, CA (Mr Cruz); Saint Camillus International University of Health Sciences, Rome, Italy (Dr D'Agostino); Duke University School of Nursing (Mr Douthit); East Carolina University College of Nursing (Dr Forbes); St Catherine University Department of Nursing (Dr Gao); Texas Woman's University College of Nursing (Dr Lee); Assistant Professor, University of North Carolina at Greensboro School of Nursing (Dr Lekan); University of Wisconsin School of Nursing (Ms Wieben); and Vanderbilt University School of Nursing, and Tennessee Valley Healthcare System, US Department of Veterans Affairs (Dr Jeffery)
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
25
|
Cesario A, D’Oria M, Bove F, Privitera G, Boškoski I, Pedicino D, Boldrini L, Erra C, Loreti C, Liuzzo G, Crea F, Armuzzi A, Gasbarrini A, Calabresi P, Padua L, Costamagna G, Antonelli M, Valentini V, Auffray C, Scambia G. Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence. J Pers Med 2021; 11:jpm11040265. [PMID: 33918214 PMCID: PMC8065854 DOI: 10.3390/jpm11040265] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 02/07/2023] Open
Abstract
Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.
Collapse
Affiliation(s)
- Alfredo Cesario
- Open Innovation Unit, Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Marika D’Oria
- Open Innovation Unit, Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Correspondence:
| | - Francesco Bove
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (F.B.); (P.C.)
- Department of Neurosciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Giuseppe Privitera
- CEMAD—IBD Unit—Internal Medicine and Gastroenterology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (G.P.); (A.A.); (A.G.)
- Department of Medicine and Translational Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Ivo Boškoski
- Surgical Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (I.B.); (G.C.)
| | - Daniela Pedicino
- Cardiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (D.P.); (G.L.); (F.C.)
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (L.B.); (V.V.)
| | - Carmen Erra
- High Intensity Neurorehabilitation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.E.); (C.L.); (L.P.)
| | - Claudia Loreti
- High Intensity Neurorehabilitation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.E.); (C.L.); (L.P.)
| | - Giovanna Liuzzo
- Cardiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (D.P.); (G.L.); (F.C.)
| | - Filippo Crea
- Cardiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (D.P.); (G.L.); (F.C.)
| | - Alessandro Armuzzi
- CEMAD—IBD Unit—Internal Medicine and Gastroenterology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (G.P.); (A.A.); (A.G.)
- Department of Medicine and Translational Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gasbarrini
- CEMAD—IBD Unit—Internal Medicine and Gastroenterology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (G.P.); (A.A.); (A.G.)
- Department of Medicine and Translational Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Paolo Calabresi
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (F.B.); (P.C.)
- Department of Neurosciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Luca Padua
- High Intensity Neurorehabilitation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.E.); (C.L.); (L.P.)
| | - Guido Costamagna
- Surgical Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (I.B.); (G.C.)
| | - Massimo Antonelli
- Anesthesia, Resuscitation, Intensive Care and Clinical Toxicology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Vincenzo Valentini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (L.B.); (V.V.)
| | - Charles Auffray
- European Institute for Systems Biology and Medicine (EISBM), 69390 Vourles, France;
| | - Giovanni Scambia
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Gynecological Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| |
Collapse
|
26
|
Liu G, Li N, Chen L, Yang Y, Zhang Y. Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov. Front Med (Lausanne) 2021; 8:634197. [PMID: 33842500 PMCID: PMC8024618 DOI: 10.3389/fmed.2021.634197] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/19/2021] [Indexed: 02/05/2023] Open
Abstract
Objective: Clinical trials contribute to the development of clinical practice. However, little is known about the current status of trials on artificial intelligence (AI) conducted in emergency department and intensive care unit. The objective of the study was to provide a comprehensive analysis of registered trials in such field based on ClinicalTrials.gov. Methods: Registered trials on AI conducted in emergency department and intensive care unit were searched on ClinicalTrials.gov up to 12th January 2021. The characteristics were analyzed using SPSS21.0 software. Results: A total of 146 registered trials were identified, including 61 in emergency department and 85 in intensive care unit. They were registered from 2004 to 2021. Regarding locations, 58 were conducted in Europe, 58 in America, 9 in Asia, 4 in Australia, and 17 did not report locations. The enrollment of participants was from 0 to 18,000,000, with a median of 233. Universities were the primary sponsors, which accounted for 43.15%, followed by hospitals (35.62%), and industries/companies (9.59%). Regarding study designs, 85 trials were interventional trials, while 61 were observational trials. Of the 85 interventional trials, 15.29% were for diagnosis and 38.82% for treatment; of the 84 observational trials, 42 were prospective, 14 were retrospective, 2 were cross-sectional, 2 did not report clear information and 1 was unknown. Regarding the trials' results, 69 trials had been completed, while only 10 had available results on ClinicalTrials.gov. Conclusions: Our study suggest that more AI trials are needed in emergency department and intensive care unit and sponsors are encouraged to report the results.
Collapse
Affiliation(s)
- Guina Liu
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, Sichuan University, Chengdu, China
| | - Nian Li
- Department of Medical Administration, West China Hospital, Sichuan University, Chengdu, China
| | - Lingmin Chen
- Department of Anesthesiology and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University and The Research Units of West China (2018RU012), Chinese Academy of Medical Sciences, Chengdu, China
| | - Yi Yang
- Department of Clinical Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.,Nursing Key Laboratory of Sichuan Province, Chengdu, China
| |
Collapse
|
27
|
Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. SUSTAINABLE OPERATIONS AND COMPUTERS 2021; 2. [PMCID: PMC8052508 DOI: 10.1016/j.susoc.2021.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background and aims Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. Methods We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. Results AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. Conclusions Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
Collapse
|
28
|
Chen Y, Wu S, Ye J, Wu M, Xiao Z, Ni X, Wang B, Chen C, Chen Y, Tan X, Liu R. Predicting All-Cause Mortality Risk in Atrial Fibrillation Patients: A Novel LASSO-Cox Model Generated From a Prospective Dataset. Front Cardiovasc Med 2021; 8:730453. [PMID: 34733891 PMCID: PMC8558306 DOI: 10.3389/fcvm.2021.730453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/20/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Although mortality remains high in patients with atrial fibrillation (AF), there have been limited studies exploring machine learning (ML) models on mortality risk prediction in patients with AF. Objectives: This study sought to develop an ML model that captures important variables in order to predict all-cause mortality in AF patients. Methods: In this single center prospective study, an ML-based mortality prediction model was developed and validated using a dataset of 2,012 patients who experienced AF from November 2018 to February 2020 at the First Affiliated Hospital of Shantou University Medical College. The dataset was randomly divided into a training set (70%, n = 1,223) and a validation set (30%, n = 552). A total of 122 features were collected for variable selection. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were used for variable selection. Ten ML models were developed using variables selected by LASSO or RF. The best model was selected and compared with conventional risk scores. A nomogram and user-friendly online tool were developed to facilitate the mortality predictions and management recommendations. Results: Thirteen features were selected by the LASSO regression algorithm. The LASSO-Cox model achieved an area under the curve (AUC) of 0.842 in the training dataset, and 0.854 in the validation dataset. A nomogram based on eight independent features was developed for the prediction of survival at 30, 180, and 365 days following discharge. Both the time dependent receiver operating characteristic (ROC) and decision curve analysis (DCA) showed better performances of the nomogram compared to the CHA2DS2-VASc and HAS-BLED models. Conclusions: The LASSO-Cox mortality predictive model shows potential benefits in death risk evaluation for AF patients over the 365-day period following discharge. This novel ML approach may also provide physicians with personalized management recommendations.
Collapse
Affiliation(s)
- Yu Chen
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shiwan Wu
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Jianfeng Ye
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Muli Wu
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zhongbo Xiao
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiaobin Ni
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Bin Wang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Chang Chen
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yequn Chen
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Clinical Research Center, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Institute of Cardiac Engineering, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Yequn Chen
| | - Xuerui Tan
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Clinical Research Center, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Institute of Cardiac Engineering, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Xuerui Tan
| | - Ruisheng Liu
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| |
Collapse
|
29
|
Goto S, Goto S, Pieper KS, Bassand JP, Camm AJ, Fitzmaurice DA, Goldhaber SZ, Haas S, Parkhomenko A, Oto A, Misselwitz F, Turpie AGG, Verheugt FWA, Fox KAA, Gersh BJ, Kakkar AK. New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF. EUROPEAN HEART JOURNAL. CARDIOVASCULAR PHARMACOTHERAPY 2020; 6:301-309. [PMID: 31821482 PMCID: PMC7556811 DOI: 10.1093/ehjcvp/pvz076] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/14/2019] [Accepted: 12/05/2019] [Indexed: 12/05/2022]
Abstract
Aims Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.
Collapse
Affiliation(s)
- Shinichi Goto
- Department of Cardiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku 160-8582, Tokyo, Japan
| | - Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, 143 Shimokasuya, Isehara, Kanagawa 259-1193, Japan
| | - Karen S Pieper
- Department of Clinical Research, Thrombosis Research Institute, Emmanuel Kaye Building, Manresa Road, Chelsea, London SW3 6LR, UK
| | - Jean-Pierre Bassand
- Department of Clinical Research, Thrombosis Research Institute, Emmanuel Kaye Building, Manresa Road, Chelsea, London SW3 6LR, UK.,Department of Cardiology, University of Besançon Boulevard Fleming, 25000 Besançon, France
| | - Alan John Camm
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Institute, St. George's University of London, Cranmer Terrace, Tooting, London, UK
| | - David A Fitzmaurice
- Department of Cardio-respiratory Primary Care, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Samuel Z Goldhaber
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Sylvia Haas
- Formerly Klinikum rechts der Isar, Technical University of Munich, Normannenstr. 34a, Munich 80333, Germany
| | - Alexander Parkhomenko
- National Scientific Center, Strazhesko Institute of Cardiology, 5 Narodnogo Opolcheniya Street, Kiev 03680, Ukraine
| | - Ali Oto
- Department of Cardiology, Memorial Ankara Hospital, Sihhiye, 06100, Ankara, Turkey
| | - Frank Misselwitz
- Therapeutic areas Thrombosis & Hematology, Bayer AG, Müllerstraße 178, 13353 Berlin, Germany
| | - Alexander G G Turpie
- Department of Medicine, McMaster University, 237 Barton St E Hamilton, Ontario L8L 2X2, Canada
| | - Freek W A Verheugt
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis (OLVG), Oosterpark 9, NL-1091-AC Amsterdam, Netherlands
| | - Keith A A Fox
- Edinburgh Centre for Cardiovascular Science, University of Edinburgh, Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Bernard J Gersh
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Ajay K Kakkar
- Department of Clinical Research, Thrombosis Research Institute, Emmanuel Kaye Building, Manresa Road, Chelsea, London SW3 6LR, UK.,Department of Surgery, University College London, Gower St, Bloomsbury, London WC1E 6BT, UK
| |
Collapse
|
30
|
Miura K, Goto S, Katsumata Y, Ikura H, Shiraishi Y, Sato K, Fukuda K. Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data. NPJ Digit Med 2020; 3:141. [PMID: 33145437 PMCID: PMC7596490 DOI: 10.1038/s41746-020-00348-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 10/01/2020] [Indexed: 11/09/2022] Open
Abstract
Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the clinically equivalent to aerobic exercise, its evaluation requires an expensive respiratory gas analyzer and expertize. To address these inconveniences, this study investigated the feasibility of a deep learning (DL) algorithm with single-lead electrocardiography (ECG) for estimating the aerobic exercise threshold. Two hundred sixty consecutive patients with CVDs who underwent CPX were analyzed. Single-lead ECG data were stored as time-series voltage data with a sampling rate of 1000 Hz. The data of preprocessed ECG and time point at VT calculated by respiratory gas analyzer were used to train a neural network. The trained model was applied on an independent test cohort, and the DL threshold (DLT; a time of VT estimated through the DL algorithm) was calculated. We compared the correlation between oxygen uptake of the VT (VT-VO2) and the DLT (DLT-VO2). Our DL model showed that the DLT-VO2 was confirmed to be significantly correlated with the VT-VO2 (r = 0.875; P < 0.001), and the mean difference was nonsignificant (-0.05 ml/kg/min, P > 0.05), which displayed strong agreements between the VT and the DLT. The DL algorithm using single-lead ECG data enabled accurate estimation of VT in patients with CVDs. The DL algorithm may be a novel way for estimating aerobic exercise threshold.
Collapse
Affiliation(s)
- Kotaro Miura
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shinichi Goto
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Yoshinori Katsumata
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Hidehiko Ikura
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yasuyuki Shiraishi
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Kazuki Sato
- Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Keiichi Fukuda
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| |
Collapse
|
31
|
Hata E, Seo C, Nakayama M, Iwasaki K, Ohkawauchi T, Ohya J. Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1548-1551. [PMID: 33018287 DOI: 10.1109/embc44109.2020.9175151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance-This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.
Collapse
|
32
|
Yabushita H, Goto S, Nakamura S, Oka H, Nakayama M, Goto S. Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video. J Atheroscler Thromb 2020; 28:835-843. [PMID: 33012741 PMCID: PMC8326176 DOI: 10.5551/jat.59675] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Aim:
The clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear.
Methods:
The 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model.
Results:
The final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively.
Conclusion:
An artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.
Collapse
Affiliation(s)
- Hiroto Yabushita
- Department of Medicine (Cardiology), Tokai University School of Medicine.,Department of Cardiology, New-Tokyo Hospital
| | - Shinichi Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine
| | | | - Hideki Oka
- Department of Medicine (Cardiology), Tokai University School of Medicine
| | - Masamitsu Nakayama
- Department of Medicine (Cardiology), Tokai University School of Medicine
| | - Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine
| |
Collapse
|
33
|
Haq KT, Howell SJ, Tereshchenko LG. Applying Artificial Intelligence to ECG Analysis: Promise of a Better Future. Circ Arrhythm Electrophysiol 2020; 13:e009111. [PMID: 32809878 DOI: 10.1161/circep.120.009111] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Kazi T Haq
- Knight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University, School of Medicine, Portland
| | - Stacey J Howell
- Knight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University, School of Medicine, Portland
| | - Larisa G Tereshchenko
- Knight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University, School of Medicine, Portland
| |
Collapse
|
34
|
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: 70] [Impact Index Per Article: 17.5] [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
|
35
|
Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med 2020; 122:103801. [PMID: 32658725 DOI: 10.1016/j.compbiomed.2020.103801] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. OBJECTIVE This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives. METHODS We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area. RESULTS The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising. CONCLUSION The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods. SIGNIFICANCE This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
Collapse
Affiliation(s)
- Shenda Hong
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
| | - Yuxi Zhou
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China.
| | - Junyuan Shang
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China.
| | - Cao Xiao
- Analytics Center of Excellence, IQVIA, Cambridge, USA.
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, USA.
| |
Collapse
|
36
|
Lopes MACQ, Oliveira GMMD, Ribeiro ALP, Pinto FJ, Rey HCV, Zimerman LI, Rochitte CE, Bacal F, Polanczyk CA, Halperin C, Araújo EC, Mesquita ET, Arruda JA, Rohde LEP, Grinberg M, Moretti M, Caramori PRA, Botelho RV, Brandão AA, Hajjar LA, Santos AF, Colafranceschi AS, Etges APBDS, Marino BCA, Zanotto BS, Nascimento BR, Medeiros CR, Santos DVDV, Cook DMA, Antoniolli E, Souza Filho EMD, Fernandes F, Gandour F, Fernandez F, Souza GEC, Weigert GDS, Castro I, Cade JR, Figueiredo Neto JAD, Fernandes JDL, Hadlich MS, Oliveira MAP, Alkmim MB, Paixão MCD, Prudente ML, Aguiar Netto MAS, Marcolino MS, Oliveira MAD, Simonelli O, Lemos Neto PA, Rosa PRD, Figueira RM, Cury RC, Almeida RC, Lima SRF, Barberato SH, Constancio TI, Rezende WFD. Guideline of the Brazilian Society of Cardiology on Telemedicine in Cardiology - 2019. Arq Bras Cardiol 2020; 113:1006-1056. [PMID: 31800728 PMCID: PMC7020958 DOI: 10.5935/abc.20190205] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
| | | | | | | | | | | | - Carlos Eduardo Rochitte
- Instituto do Coração (InCor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (USP), São Paulo, SP - Brazil
| | - Fernando Bacal
- Instituto do Coração (InCor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (USP), São Paulo, SP - Brazil
| | - Carisi Anne Polanczyk
- Hospital de Clínicas de Porto Alegre, Porto Alegre, RS - Brazil.,Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brazil.,Instituto de Avaliação de Tecnologias em Saúde (IATS), Porto Alegre, RS - Brazil
| | | | | | | | | | | | - Max Grinberg
- Instituto do Coração (InCor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (USP), São Paulo, SP - Brazil
| | - Miguel Moretti
- Instituto do Coração (InCor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (USP), São Paulo, SP - Brazil
| | | | - Roberto Vieira Botelho
- Instituto do Coração do Triângulo (ICT), Uberlândia, MG - Brazil.,International Telemedical Systems do Brasil (ITMS), Uberlândia, MG - Brazil
| | | | - Ludhmila Abrahão Hajjar
- Instituto do Coração (InCor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (USP), São Paulo, SP - Brazil
| | | | | | | | - Bárbara Campos Abreu Marino
- Hospital Madre Teresa, Belo Horizonte, MG - Brazil.,Pontifícia Universidade Católica de Minas Gerais (PUCMG), Belo Horizonte, MG - Brazil
| | - Bruna Stella Zanotto
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brazil.,Instituto de Avaliação de Tecnologias em Saúde (IATS), Porto Alegre, RS - Brazil
| | - Bruno Ramos Nascimento
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brazil
| | | | | | - Daniela Matos Arrowsmith Cook
- Hospital Pró-Cardíaco, Rio de Janeiro, RJ - Brazil.,Hospital Copa Star, Rio de Janeiro, RJ - Brazil.,Hospital dos Servidores do Estado do Rio de Janeiro, Rio de Janeiro, RJ - Brazil
| | | | - Erito Marques de Souza Filho
- Universidade Federal Fluminense (UFF), Rio de Janeiro, RJ - Brazil.,Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ - Brazil
| | | | - Fabio Gandour
- Universidade de Brasília (UnB), Brasília, DF - Brazil
| | | | | | | | - Iran Castro
- Instituto de Cardiologia do Rio Grande do Sul, Porto Alegre, RS - Brazil.,Fundação Universitária de Cardiologia, Porto Alegre, RS - Brazil
| | | | | | | | - Marcelo Souza Hadlich
- Fleury Medicina e Saúde, Rio de Janeiro, RJ - Brazil.,Rede D'Or, Rio de Janeiro, RJ - Brazil.,Unimed-Rio, Rio de Janeiro, RJ - Brazil
| | | | - Maria Beatriz Alkmim
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brazil.,Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brazil
| | | | | | | | | | | | - Osvaldo Simonelli
- Conselho Regional de Medicina do Estado de São Paulo, São Paulo, SP - Brazil.,Instituto Paulista de Direito Médico e da Saúde (IPDMS), Ribeirão Preto, SP - Brazil
| | | | - Priscila Raupp da Rosa
- Hospital Israelita Albert Einstein, São Paulo, SP - Brazil.,Hospital Sírio Libanês, São Paulo, SP - Brazil
| | | | | | | | | | - Silvio Henrique Barberato
- CardioEco-Centro de Diagnóstico Cardiovascular, Curitiba, PR - Brazil.,Quanta Diagnóstico e Terapia, Curitiba, PR - Brazil
| | | | | |
Collapse
|
37
|
Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 2020; 11:1760. [PMID: 32273514 PMCID: PMC7145824 DOI: 10.1038/s41467-020-15432-4] [Citation(s) in RCA: 198] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 02/17/2020] [Indexed: 11/08/2022] Open
Abstract
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.
Collapse
|
38
|
Faita F. Deep learning in Emergency Medicine: Recent contributions and methodological challenges. EMERGENCY CARE JOURNAL 2020. [DOI: 10.4081/ecj.2020.8573] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.
Collapse
|
39
|
Ribeiro AL, Oliveira GMMD. Toward a Patient-Centered, Data-Driven Cardiology. Arq Bras Cardiol 2020; 112:371-373. [PMID: 30994714 PMCID: PMC6459428 DOI: 10.5935/abc.20190069] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
|
40
|
Roman M. Are neural networks the ultimate risk prediction models in patients at high risk of acute myocardial infarction? Eur J Prev Cardiol 2020; 27:2045-2046. [PMID: 31992062 DOI: 10.1177/2047487319890972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Marius Roman
- Department of Cardiovascular Sciences and National Institute for Health Research, University of Leicester, UK
| |
Collapse
|
41
|
Goto S, Goto S. Application of Neural Networks to 12-Lead Electrocardiography - Current Status and Future Directions. Circ Rep 2019; 1:481-486. [PMID: 33693089 PMCID: PMC7897559 DOI: 10.1253/circrep.cr-19-0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The 12-lead electrocardiogram (ECG) is a fast, non-invasive, powerful tool to diagnose or to evaluate the risk of various cardiac diseases. The vast majority of arrhythmias are diagnosed solely on 12-lead ECG. Initial detection of myocardial ischemia such as myocardial infarction (MI), acute coronary syndrome (ACS) and effort angina is also dependent upon 12-lead ECG. ECG reflects the electrophysiological state of the heart through body mass, and thus contains important information on the electricity-dependent function of the human heart. Indeed, 12-lead ECG data are complex. Therefore, the clinical interpretation of 12-lead ECG requires intense training, but still is prone to interobserver variability. Even with rich clinically relevant data, non-trained physicians cannot efficiently use this powerful tool. Furthermore, recent studies have shown that 12-lead ECG may contain information that is not recognized even by well-trained experts but which can be extracted by computer. Artificial intelligence (AI) based on neural networks (NN) has emerged as a strong tool to extract valuable information from ECG for clinical decision making. This article reviews the current status of the application of NN-based AI to the interpretation of 12-lead ECG and also discusses the current problems and future directions.
Collapse
Affiliation(s)
- Shinichi Goto
- Department of Cardiology, Keio University School of Medicine Tokyo Japan.,Department of Medicine (Cardiology), Tokai University School of Medicine Isehara Japan
| | - Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine Isehara Japan
| |
Collapse
|
42
|
The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16214087. [PMID: 31652898 PMCID: PMC6862180 DOI: 10.3390/ijerph16214087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 11/26/2022]
Abstract
Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index (EDWINC) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.
Collapse
|
43
|
Junsawang P, Phimoltares S, Lursinsap C. Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity. PLoS One 2019; 14:e0220624. [PMID: 31498787 PMCID: PMC6733468 DOI: 10.1371/journal.pone.0220624] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 07/20/2019] [Indexed: 11/18/2022] Open
Abstract
Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.
Collapse
Affiliation(s)
- Prem Junsawang
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Suphakant Phimoltares
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- * E-mail:
| | - Chidchanok Lursinsap
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| |
Collapse
|
44
|
Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier. ALGORITHMS 2019. [DOI: 10.3390/a12060118] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.
Collapse
|