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Nedadur R, Bhatt N, Lui T, Chu MWA, McCarthy PM, Kline A. The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery. Can J Cardiol 2024:S0828-282X(24)00586-5. [PMID: 39098601 DOI: 10.1016/j.cjca.2024.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 07/29/2024] [Accepted: 07/29/2024] [Indexed: 08/06/2024] Open
Abstract
Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery utilizing immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery, from the vantage point of a patient's journey. Applications of AI include pre-operative risk assessment, intraoperative planning, post-operative patient care and out-patient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and underrepresented avenue for future utilization of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize problems, to ensure problem-driven solutions and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicenter repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient utilization of our clinical expertise toward high-level decision making and technical prowess.
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Affiliation(s)
- Rashmi Nedadur
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
| | - Nitish Bhatt
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada
| | - Tom Lui
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
| | | | - Patrick M McCarthy
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
| | - Adrienne Kline
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
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2
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Sulague RM, Beloy FJ, Medina JR, Mortalla ED, Cartojano TD, Macapagal S, Kpodonu J. Artificial intelligence in cardiac surgery: A systematic review. World J Surg 2024. [PMID: 39019775 DOI: 10.1002/wjs.12265] [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: 01/30/2024] [Accepted: 06/14/2024] [Indexed: 07/19/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery. METHODS A systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized. RESULTS A total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (n = 48), followed by support vector machine (n = 33), logistic regression (n = 32), and eXtreme Gradient Boosting (n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes. CONCLUSION AI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision-making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.
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Affiliation(s)
- Ralf Martz Sulague
- Graduate School of Arts and Sciences, Georgetown University, Washington, District of Columbia, USA
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | | | | | | | | | | | - Jacques Kpodonu
- Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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3
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Pozza A, Zanella L, Castaldi B, Di Salvo G. How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease? J Clin Med 2024; 13:2996. [PMID: 38792537 PMCID: PMC11122569 DOI: 10.3390/jcm13102996] [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: 04/09/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.
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Affiliation(s)
- Alice Pozza
- Paediatric Cardiology Unit, Department of Women’s and Children’s Health, University of Padua, 35122 Padova, Italy; (A.P.)
| | - Luca Zanella
- Heart Surgery, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
- Cardiac Surgery Unit, Department of Cardiac-Thoracic-Vascular Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Biagio Castaldi
- Paediatric Cardiology Unit, Department of Women’s and Children’s Health, University of Padua, 35122 Padova, Italy; (A.P.)
| | - Giovanni Di Salvo
- Paediatric Cardiology Unit, Department of Women’s and Children’s Health, University of Padua, 35122 Padova, Italy; (A.P.)
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4
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Sengupta PP, Kluin J, Lee SP, Oh JK, Smits AIPM. The future of valvular heart disease assessment and therapy. Lancet 2024; 403:1590-1602. [PMID: 38554727 DOI: 10.1016/s0140-6736(23)02754-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/15/2023] [Accepted: 12/06/2023] [Indexed: 04/02/2024]
Abstract
Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.
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Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA; Cardiovascular Services, Robert Wood Johnson University Hospital, New Brunswick, NJ, USA.
| | - Jolanda Kluin
- Department of Cardiothoracic Surgery, Erasmus MC Rotterdam, Thorax Center, Rotterdam, Netherlands
| | - Seung-Pyo Lee
- Department of Internal Medicine, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthal I P M Smits
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
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5
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Tsai AY, Carter SR, Greene AC. Artificial intelligence in pediatric surgery. Semin Pediatr Surg 2024; 33:151390. [PMID: 38242061 DOI: 10.1016/j.sempedsurg.2024.151390] [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] [Indexed: 01/21/2024]
Abstract
Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.
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Affiliation(s)
- Anthony Y Tsai
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States.
| | - Stewart R Carter
- Division of Pediatric Surgery, University of Louisville School of Medicine, Louisville, KY, United States
| | - Alicia C Greene
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States
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Burattini M, Lo Muzio FP, Hu M, Bonalumi F, Rossi S, Pagiatakis C, Salvarani N, Fassina L, Luciani GB, Miragoli M. Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights. Sci Rep 2024; 14:1782. [PMID: 38245558 PMCID: PMC10799933 DOI: 10.1038/s41598-024-52081-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/12/2024] [Indexed: 01/22/2024] Open
Abstract
The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol.
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Affiliation(s)
- Margherita Burattini
- Department of Surgery, Dentistry and Maternity, University of Verona, Verona, Italy
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Francesco Paolo Lo Muzio
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Deutsches Herzzentrum Der Charité, Department of Cardiology, Angiology and Intensive Care Medicine, Berlin, Germany
| | - Mirko Hu
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Flavia Bonalumi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Stefano Rossi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Christina Pagiatakis
- Humanitas Research Hospital, IRCCS, Rozzano (Milan), Italy
- Department of Biotechnology and Life Sciences, University of Insubria, Varese, Italy
| | - Nicolò Salvarani
- Humanitas Research Hospital, IRCCS, Rozzano (Milan), Italy
- Institute of Genetic and Biomedical Research (IRGB), UOS of Milan, National Research Council of Italy, Milan, Italy
| | - Lorenzo Fassina
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - Michele Miragoli
- Department of Medicine and Surgery, University of Parma, Parma, Italy.
- Humanitas Research Hospital, IRCCS, Rozzano (Milan), Italy.
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Jacquemyn X, Kutty S, Manlhiot C. The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot. CJC PEDIATRIC AND CONGENITAL HEART DISEASE 2023; 2:440-452. [PMID: 38161675 PMCID: PMC10755786 DOI: 10.1016/j.cjcpc.2023.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/24/2023] [Indexed: 01/03/2024]
Abstract
Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.
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Affiliation(s)
- Xander Jacquemyn
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Shelby Kutty
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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8
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Mendiola Pla M, Berrettoni S, Lee FH, Rozzi G, Marrano F, Gross RT, Evans A, Wendell DC, Lezberg P, Burattini M, Paolo lo Muzio F, Fassina L, Milano CA, Bang ML, Bowles DE, Miragoli M. Video analysis of ex vivo beating hearts during preservation on the TransMedics® organ care system. Front Cardiovasc Med 2023; 10:1216917. [PMID: 37408655 PMCID: PMC10318359 DOI: 10.3389/fcvm.2023.1216917] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/23/2023] [Indexed: 07/07/2023] Open
Abstract
Background Reliable biomarkers for assessing the viability of the donor hearts undergoing ex vivo perfusion remain elusive. A unique feature of normothermic ex vivo perfusion on the TransMedics® Organ Care System (OCS™) is that the donor heart is maintained in a beating state throughout the preservation period. We applied a video algorithm for an in vivo assessment of cardiac kinematics, video kinematic evaluation (Vi.Ki.E.), to the donor hearts undergoing ex vivo perfusion on the OCS™ to assess the feasibility of applying this algorithm in this setting. Methods Healthy donor porcine hearts (n = 6) were procured from Yucatan pigs and underwent 2 h of normothermic ex vivo perfusion on the OCS™ device. During the preservation period, serial high-resolution videos were captured at 30 frames per second. Using Vi.Ki.E., we assessed the force, energy, contractility, and trajectory parameters of each heart. Results There were no significant changes in any of the measured parameters of the heart on the OCS™ device over time as judged by linear regression analysis. Importantly, there were no significant changes in contractility during the duration of the preservation period (time 0-30 min, 918 ± 430 px/s; time 31-60 min, 1,386 ± 603 px/s; time 61-90 min, 1,299 ± 617 px/s; time 91-120 min, 1,535 ± 728 px/s). Similarly, there were no significant changes in the force, energy, or trajectory parameters. Post-transplantation echocardiograms demonstrated robust contractility of each allograft. Conclusion Vi.Ki.E. assessment of the donor hearts undergoing ex vivo perfusion is feasible on the TransMedics OCS™, and we observed that the donor hearts maintain steady kinematic measurements throughout the duration.
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Affiliation(s)
| | - Silvia Berrettoni
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Franklin H. Lee
- Department of Surgery, Duke University Medical Center, Durham, NC, United States
| | - Giacomo Rozzi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Federica Marrano
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Ryan T. Gross
- Department of Surgery, Duke University Medical Center, Durham, NC, United States
| | - Amy Evans
- Perfusion Services, Duke University Medical Center, Durham, NC, United States
| | - David C. Wendell
- Duke Cardiovascular Magnetic Resonance Center, Duke University Medical Center, Durham, NC, United States
| | | | - Margherita Burattini
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Department of Surgical Sciences, Dentistry, and Maternity, University of Verona, Verona, Italy
| | | | - Lorenzo Fassina
- Department of Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Carmelo A. Milano
- Department of Surgery, Duke University Medical Center, Durham, NC, United States
| | - Marie-Louise Bang
- Institute of Genetic and Biomedical Research (IRGB), National Research Council (CNR), Milan Unit, Milan, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
| | - Dawn E. Bowles
- Department of Surgery, Duke University Medical Center, Durham, NC, United States
| | - Michele Miragoli
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
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Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, Sharma R, Huang H, Chopra H, Khandaker MU, Lashin MMA, Hamd ZY, Emran TB. Artificial Intelligence in Pediatric Cardiology: A Scoping Review. J Clin Med 2022; 11:jcm11237072. [PMID: 36498651 PMCID: PMC9738645 DOI: 10.3390/jcm11237072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/22/2022] [Accepted: 11/26/2022] [Indexed: 12/05/2022] Open
Abstract
The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
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Affiliation(s)
- Yashendra Sethi
- PearResearch, Dehradun 248001, India
- Department of Medicine, Government Doon Medical College, Dehradun 248001, India
| | - Neil Patel
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Nirja Kaka
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Ami Desai
- Department of Medicine, SMIMER Medical College, Surat 395010, India
| | - Oroshay Kaiwan
- PearResearch, Dehradun 248001, India
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Mili Sheth
- Department of Medicine, GMERS Gandhinagar, Gandhinagar 382012, India
| | - Rupal Sharma
- Department of Medicine, Government Medical College, Nagpur 440003, India
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, India
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Malaysia
| | - Maha M. A. Lashin
- Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
| | - Zuhal Y. Hamd
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
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10
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Special Issue “New Perspectives in Cardiovascular Surgery”. J Clin Med 2022; 11:jcm11154535. [PMID: 35956150 PMCID: PMC9369964 DOI: 10.3390/jcm11154535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 02/01/2023] Open
Abstract
In recent decades, cardiovascular surgery has been making great strides in the field of medicine [...]
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11
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Mumtaz H, Saqib M, Ansar F, Zargar D, Hameed M, Hasan M, Muskan P. The future of Cardiothoracic surgery in Artificial intelligence. Ann Med Surg (Lond) 2022; 80:104251. [PMID: 36045824 PMCID: PMC9422274 DOI: 10.1016/j.amsu.2022.104251] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/23/2022] Open
Abstract
Humans' great and quick technological breakthroughs in the previous decade have undoubtedly influenced how surgical procedures are executed in the operating room. AI is becoming incredibly influential for surgical decision-making to help surgeons make better projections about the implications of surgical operations by considering different sources of data such as patient health conditions, disease natural history, patient values, and finance. Although the application of artificial intelligence in healthcare settings is rapidly increasing, its mainstream application in clinical practice remains limited. The use of machine learning algorithms in thoracic surgery is extensive, including different clinical stages. By leveraging techniques such as machine learning, computer vision, and robotics, AI may play a key role in diagnostic augmentation, operative management, pre-and post-surgical patient management, and upholding safety standards. AI, particularly in complex surgical procedures such as cardiothoracic surgery, may be a significant help to surgeons in executing more intricate surgeries with greater success, fewer complications, and ensuring patient safety, while also providing resources for robust research and better dissemination of knowledge. In this paper, we present an overview of AI applications in thoracic surgery and its related components, including contemporary projects and technology that use AI in cardiothoracic surgery and general care. We also discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. It is vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible. This review is a great addition to literature, keeping practicing and aspiring surgeons up to date on the most recent advances in AI and cardiothoracic surgery. This literature review tells about the role of Artificial Intelligence in Cardiothoracic Surgery. Discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. Vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible.
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Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation. SENSORS 2021; 22:s22010249. [PMID: 35009792 PMCID: PMC8749724 DOI: 10.3390/s22010249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/20/2021] [Accepted: 12/27/2021] [Indexed: 12/04/2022]
Abstract
Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.
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