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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [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: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Yoon M, Park JJ, Hur T, Hua CH, Hussain M, Lee S, Choi DJ. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. INTERNATIONAL JOURNAL OF HEART FAILURE 2024; 6:11-19. [PMID: 38303917 PMCID: PMC10827704 DOI: 10.36628/ijhf.2023.0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 02/03/2024]
Abstract
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Affiliation(s)
- Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Joo Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Taeho Hur
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Musarrat Hussain
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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Bhadrvathi Ghouse PS, Mane PR, Thankappan Sumangala S, Kumar Puttur V, Pathan S, Jhunjhunwala VK, Ali T. A Compact Dual-Band Millimeter Wave Antenna for Smartwatch and IoT Applications with Link Budget Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 24:103. [PMID: 38202965 PMCID: PMC10781004 DOI: 10.3390/s24010103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
Advancement in smartwatch sensors and connectivity features demands low latency communication with a wide bandwidth. ISM bands below 6 GHz are reaching a threshold. The millimeter-wave (mmWave) spectrum is the solution for future smartwatch applications. Therefore, a compact dual-band antenna operating at 25.5 and 38 GHz is presented here. The characteristics mode theory (CMT) aids the antenna design process by exciting Mode 1 and 2 as well as Mode 1-3 at their respective bands. In addition, the antenna structure generates two traverse modes, TM10 and TM02, at the lower and higher frequency bands. The antenna measured a bandwidth (BW) of 1.5 (25-26.5 GHz) and 2.5 GHz (37-39.5 GHz) with a maximum gain of 7.4 and 7.3 dBi, respectively. The antenna performance within the watch case (stainless steel) showed a stable |S11| with a gain improvement of 9.9 and 10.9 dBi and a specific absorption rate (SAR) of 0.063 and 0.0206 W/kg, respectively, at the lower and higher bands. The link budget analysis for various rotation angles of the watch indicated that, for a link margin of 20 dB, the antenna can transmit/receive 1 Gbps of data. However, significant fading was noticed at certain angles due to the shadowing effect caused by the watch case itself. Nonetheless, the antenna has a workable bandwidth, a high gain, and a low SAR, making it suitable for smartwatch and IoT applications.
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Affiliation(s)
- Parveez Shariff Bhadrvathi Ghouse
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (P.S.B.G.); (P.R.M.); (V.K.P.)
| | - Pallavi R. Mane
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (P.S.B.G.); (P.R.M.); (V.K.P.)
| | - Sangeetha Thankappan Sumangala
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (S.T.S.); (S.P.)
| | - Vasanth Kumar Puttur
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (P.S.B.G.); (P.R.M.); (V.K.P.)
| | - Sameena Pathan
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (S.T.S.); (S.P.)
| | - Vikash Kumar Jhunjhunwala
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Tanweer Ali
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (P.S.B.G.); (P.R.M.); (V.K.P.)
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Jung YM, Kang S, Son JM, Lee HS, Han GI, Yoo AH, Kwon JM, Park CW, Park JS, Jun JK, Lee MS, Lee SM. Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy. Am J Obstet Gynecol MFM 2023; 5:101184. [PMID: 37863197 DOI: 10.1016/j.ajogmf.2023.101184] [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: 07/07/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear. OBJECTIVE This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy. STUDY DESIGN This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05. RESULTS Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively. CONCLUSION The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.
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Affiliation(s)
- Young Mi Jung
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee); Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea (Drs Jung and S Lee); Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Sora Kang
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Jeong Min Son
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Hak Seung Lee
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Ga In Han
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Ah-Hyun Yoo
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Joon-Myoung Kwon
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Chan-Wook Park
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee)
| | - Joong Shin Park
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee)
| | - Jong Kwan Jun
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee)
| | - Min Sung Lee
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee).
| | - Seung Mi Lee
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee); Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea (Drs Jung and S Lee); Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee); Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University, Seoul, Korea (Dr S Lee).
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Ghomrawi HMK, O'Brien MK, Carter M, Macaluso R, Khazanchi R, Fanton M, DeBoer C, Linton SC, Zeineddin S, Pitt JB, Bouchard M, Figueroa A, Kwon S, Holl JL, Jayaraman A, Abdullah F. Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy. NPJ Digit Med 2023; 6:148. [PMID: 37587211 PMCID: PMC10432429 DOI: 10.1038/s41746-023-00890-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/01/2023] [Indexed: 08/18/2023] Open
Abstract
When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.
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Affiliation(s)
- Hassan M K Ghomrawi
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Services and Outcomes Research, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Global Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medicine (Rheumatology), Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michela Carter
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | | | - Rushmin Khazanchi
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Christopher DeBoer
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Samuel C Linton
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Suhail Zeineddin
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - J Benjamin Pitt
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Megan Bouchard
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Angie Figueroa
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Soyang Kwon
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jane L Holl
- Department of Neurology and Center for Healthcare Delivery Science and Innovation, Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Arun Jayaraman
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Physical Therapy and Human Movement Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Fizan Abdullah
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Center for Global Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Avenue, Box 63, Chicago, IL, 60611, USA.
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Martínez-Sellés M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis 2023; 10:jcdd10040175. [PMID: 37103054 PMCID: PMC10145690 DOI: 10.3390/jcdd10040175] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 04/28/2023] Open
Abstract
Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.
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Affiliation(s)
- Manuel Martínez-Sellés
- Cardiology Department, Hospital General Universitario Gregorio Marañón, Calle Doctor Esquerdo, 46, 28007 Madrid, Spain
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Europea, Villaviciosa de Odón, 28670 Madrid, Spain
- Facultad de Medicina, Universidad Complutense, 28040 Madrid, Spain
| | - Manuel Marina-Breysse
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- IDOVEN Research, 28013 Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Myocardial Pathophysiology Area, 28029 Madrid, Spain
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020828. [PMID: 36679626 PMCID: PMC9865666 DOI: 10.3390/s23020828] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/27/2022] [Accepted: 01/09/2023] [Indexed: 06/02/2023]
Abstract
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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8
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Javaid A, Zghyer F, Kim C, Spaulding EM, Isakadze N, Ding J, Kargillis D, Gao Y, Rahman F, Brown DE, Saria S, Martin SS, Kramer CM, Blumenthal RS, Marvel FA. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. Am J Prev Cardiol 2022; 12:100379. [PMID: 36090536 PMCID: PMC9460561 DOI: 10.1016/j.ajpc.2022.100379] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/21/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
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9
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Kim KH, Kwon JM, Pereira T, Attia ZI, Pereira NL. Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application? Curr Cardiol Rep 2022; 24:1547-1555. [PMID: 36048306 DOI: 10.1007/s11886-022-01776-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to interpret data and make clinical decisions. The aim of this review is to describe the contemporary state of AI and digital health applied to cardiomyopathies as well as to define a potential pivotal role of its application by physicians in clinical practice. RECENT FINDINGS Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). Even with mild left ventricular (LV) dysfunction, AI-ECG screening for amyloidosis, hypertrophic cardiomyopathy, or dilated cardiomyopathy is now feasible. Introduction of AI-ECG in routine clinical care has resulted in higher detection of LV systolic dysfunction; however, clinical research on a broader scale with diverse populations is necessary and ongoing. In the area of cardiac-imaging, AI automatically assesses the thickness and characteristics of myocardium to differentiate cardiomyopathies, but research on its prognostic capability has yet to be conducted. AI is also being applied to cardiomyopathy genomics, especially to predict pathogenicity of variants and identify whether these variants are clinically actionable. While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still in its infancy, an ever-growing clinical research strategy will ascertain the clinical utility of these AI tools to help improve diagnosis of and outcomes in cardiomyopathies. We also need to standardize the tools used to monitor the performance of AI-based systems which can then be used to expedite decision-making and rectify any hidden biases. Given its potential important role in clinical practice, healthcare providers need to familiarize themselves with the promise and limitations of this technology.
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Affiliation(s)
- Kyung-Hee Kim
- Internal Medicine, Department of Cardiology, Incheon Sejong Hospital, Incheon, South Korea.,Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Joon-Myung Kwon
- Department of Critical Care and Emergency Medicine, Incheon Sejong Hospital, Incheon, South Korea.,Medical Research Team, Medical AI, Co, Seoul, South Korea
| | - Tara Pereira
- Artificial Intelligence Development, Center for Digital Health, Mayo Clinic, Rochester, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Naveen L Pereira
- Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. .,Department of Molecular Pharmacology and Therapeutics, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
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Patel N, Agamy S, Ahmad M. Correspondence to the European Heart Journal-digital health in response to the paper by Attia et al. 2022. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:499. [PMID: 36710889 PMCID: PMC9779798 DOI: 10.1093/ehjdh/ztac053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Moving towards vertically integrated artificial intelligence development. NPJ Digit Med 2022; 5:143. [PMID: 36104535 PMCID: PMC9474277 DOI: 10.1038/s41746-022-00690-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/31/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractSubstantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable “AI factory” (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects.
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