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Qin Y, Qin X, Zhang J, Guo X. Artificial intelligence: The future for multimodality imaging of right ventricle. Int J Cardiol 2024; 404:131970. [PMID: 38490268 DOI: 10.1016/j.ijcard.2024.131970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024]
Abstract
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Affiliation(s)
- Yuhan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaohan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaoxiao Guo
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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Zawadka M, Santonocito C, Dezio V, Amelio P, Messina S, Cardia L, Franchi F, Messina A, Robba C, Noto A, Sanfilippo F. Inferior vena cava distensibility during pressure support ventilation: a prospective study evaluating interchangeability of subcostal and trans‑hepatic views, with both M‑mode and automatic border tracing. J Clin Monit Comput 2024:10.1007/s10877-024-01177-8. [PMID: 38819726 DOI: 10.1007/s10877-024-01177-8] [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: 12/07/2023] [Accepted: 05/10/2024] [Indexed: 06/01/2024]
Abstract
The Inferior Vena Cava (IVC) is commonly utilized to evaluate fluid status in the Intensive Care Unit (ICU),with more recent emphasis on the study of venous congestion. It is predominantly measured via subcostal approach (SC) or trans-hepatic (TH) views, and automated border tracking (ABT) software has been introduced to facilitate its assessment. Prospective observational study on patients ventilated in pressure support ventilation (PSV) with 2 × 2 factorial design. Primary outcome was to evaluate interchangeability of measurements of the IVC and the distensibility index (DI) obtained using both M-mode and ABT, across both SC and TH. Statistical analyses comprised Bland-Altman assessments for mean bias, limits of agreement (LoA), and the Spearman correlation coefficients. IVC visualization was 100% successful via SC, while TH view was unattainable in 17.4% of cases. As compared to the M-mode, the IVC-DI obtained through ABT approach showed divergences in both SC (mean bias 5.9%, LoA -18.4% to 30.2%, ICC = 0.52) and TH window (mean bias 6.2%, LoA -8.0% to 20.4%, ICC = 0.67). When comparing the IVC-DI measures obtained in the two anatomical sites, accuracy improved with a mean bias of 1.9% (M-mode) and 1.1% (ABT), but LoA remained wide (M-mode: -13.7% to 17.5%; AI: -19.6% to 21.9%). Correlation was generally suboptimal (r = 0.43 to 0.60). In PSV ventilated patients, we found that IVC-DI calculated with M-mode is not interchangeable with ABT measurements. Moreover, the IVC-DI gathered from SC or TH view produces not comparable results, mainly in terms of precision.
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Affiliation(s)
- Mateusz Zawadka
- 2nd Department of Anaesthesiology and Intensive Care, Medical University of Warsaw, Warsaw, Poland.
| | - Cristina Santonocito
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Via S. Sofia N 78, 95123, Catania, Italy
| | - Veronica Dezio
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Via S. Sofia N 78, 95123, Catania, Italy
| | - Paolo Amelio
- School of Anaesthesia and Intensive Care, University "Magna Graecia", Catanzaro, Italy
| | - Simone Messina
- School of Anaesthesia and Intensive Care, University "Magna Graecia", Catanzaro, Italy
| | - Luigi Cardia
- Department of Human Pathology of Adult and Childhood "Gaetano Barresi", University of Messina, Messina, Italy
| | - Federico Franchi
- Cardiothoracic and Vascular Anesthesia and Intensive Care Unit, Department of Medical Science, Surgery and Neurosciences, University Hospital of Siena, 53100, Siena, Italy
| | - Antonio Messina
- Humanitas Clinical and Research Center - IRCCS, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, MI, Italy
| | - Chiara Robba
- Department of Surgical Science and Diagnostic Integrated, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alberto Noto
- Department of Human Pathology of Adult and Childhood "Gaetano Barresi", University of Messina, Messina, Italy
- Division of Anesthesia and Intensive Care, Policlinico "G. Martino", Messina, Italy
| | - Filippo Sanfilippo
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Via S. Sofia N 78, 95123, Catania, Italy.
- Department of Surgery and Medical-Surgical Specialties, University of Catania, Catania, Italy.
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [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: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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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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Pu H, Wang C, Yu T, Chen X, Li G, Zhu D, Pan X, Wang Y. A synergistic strategy based on active hydroxymethyl amine compounds and fucoidan for bioprosthetic heart valves with enhancing anti-coagulation and anti-calcification properties. Int J Biol Macromol 2024; 266:130715. [PMID: 38462108 DOI: 10.1016/j.ijbiomac.2024.130715] [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: 12/08/2023] [Revised: 02/04/2024] [Accepted: 03/05/2024] [Indexed: 03/12/2024]
Abstract
With an aging population, the patients with valvular heart disease (VHD) are growing worldwide, and valve replacement is a primary choice for these patients with severe valvular disease. Among them, bioprosthetic heart valves (BHVs), especially BHVs trough transcatheter aortic valve replacement, are widely accepted by patients on account of their good hemodynamics and biocompatibility. Commercial BHVs in clinic are prepared by glutaraldehyde cross-linked pericardial tissue with the risk of calcification and thrombotic complications. In the present study, a strategy combines improved hemocompatibility and anti-calcification properties for BHVs has been developed based on a novel non-glutaraldehyde BHV crosslinker hexakis(hydroxymethyl)melamine (HMM) and the anticoagulant fucoidan. Besides the similar mechanical properties and enhanced component stability compared to glutaraldehyde crosslinked PP (G-PP), the fucoidan modified HMM-crosslinked PPs (HMM-Fu-PPs) also exhibit significantly enhanced anticoagulation performance with a 72 % decrease in thrombus weight compared with G-PP in ex-vivo shunt assay, along with the superior biocompatibility, satisfactory anti-calcification properties confirmed by subcutaneous implantation. Owing to good comprehensive performance of these HMM-Fu-PPs, this simple and feasible strategy may offer a great potential for BHV fabrication in the future, and open a new avenue to explore more N-hydroxymethyl compound based crosslinker with excellent performance in the field of biomaterials.
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Affiliation(s)
- Hongxia Pu
- National Engineering Research Center for Biomaterials and College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Canyu Wang
- National Engineering Research Center for Biomaterials and College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Tao Yu
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu, China
| | - Xiaotong Chen
- National Engineering Research Center for Biomaterials and College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Gaocan Li
- National Engineering Research Center for Biomaterials and College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Da Zhu
- Department of Structure Heart Center, Fuwai Yunnan Cardiovascular Hospital, Kunming, China
| | - Xiangbin Pan
- Department of Structure Heart Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Structure Heart Center, Fuwai Yunnan Cardiovascular Hospital, Kunming, China.
| | - Yunbing Wang
- National Engineering Research Center for Biomaterials and College of Biomedical Engineering, Sichuan University, Chengdu, China.
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Demirel C, Winter MP, Nitsche C, Koschatko S, Jantsch C, Mascherbauer K, Halavina K, Heitzinger G, Dona C, Dannenberg V, Spinka G, Koschutnik M, Andreas M, Hengstenberg C, Bartko PE. Mixed aortic valve disease: association with paravalvular leak and reduced survival after transcatheter aortic valve replacement. Eur Heart J Cardiovasc Imaging 2024; 25:718-726. [PMID: 38236149 DOI: 10.1093/ehjci/jeae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 12/29/2023] [Indexed: 01/19/2024] Open
Abstract
AIMS Transcatheter aortic valve replacement (TAVR) revolutionized the therapy of severe aortic stenosis (AS) with rising numbers. Mixed aortic valve disease (MAVD) treated by TAVR is gaining more interest, as those patients represent a more complex cohort as compared with isolated AS. However, concerning long-term outcome for this cohort only, limited data are available. The aim of the study is to assess the prevalence of MAVD in TAVR patients, investigate its association with paravalvular regurgitation (PVR), and analyse its impact on long-term mortality after TAVR. METHODS AND RESULTS We conducted a registry-based cohort study using the Vienna TAVR registry, enrolling patients who underwent TAVR at Medical University of Vienna between January 2007 and May 2020 with available transthoracic echocardiography before and after TAVR (n = 880). Data analysis included PVR incidence and long-term survival outcomes. A total of 647 (73.52%) out of 880 patients had ≥ mild aortic regurgitation next to severe AS. MAVD was associated with PVR compared with isolated AS with an odds ratio of 2.06, 95% confidence interval (CI): 1.51-2.81 (P = <0.001). More than mild PVR after TAVR (n = 168 out of 880: 19.09%) was related to higher mortality compared with the absence of PVR with a hazard ratio (HR) of 1.33, 95% CI: 1.05- 1.67 (P = 0.016). MAVD patients developing ≥ mild PVR after TAVR were also associated with higher mortality compared with the absence of PVR with an HR of 1.30 and 95% CI: 1.04-1.62 (P = 0.022). CONCLUSION MAVD is prevalent among TAVR patients and presents unique challenges, with increased PVR risk and worse outcomes compared with isolated AS. Long-term survival for MAVD patients, not limited to those developing PVR post-TAVR, is compromised. Earlier intervention before the occurrence of structural myocardial damage or surgical valve replacement might be a potential workaround to improve outcomes.
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Affiliation(s)
- Caglayan Demirel
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Max Paul Winter
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Christian Nitsche
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Sophia Koschatko
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Charlotte Jantsch
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Katharina Mascherbauer
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Kseniya Halavina
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Gregor Heitzinger
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Carolina Dona
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Varius Dannenberg
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Georg Spinka
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Matthias Koschutnik
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Martin Andreas
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
| | - Christian Hengstenberg
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
| | - Philipp E Bartko
- Department of Internal Medicine II, Clinical Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20A, 1090 Vienna, Austria
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Vaid A, Duong SQ, Lampert J, Kovatch P, Freeman R, Argulian E, Croft L, Lerakis S, Goldman M, Khera R, Nadkarni GN. Local large language models for privacy-preserving accelerated review of historic echocardiogram reports. J Am Med Inform Assoc 2024:ocae085. [PMID: 38687616 DOI: 10.1093/jamia/ocae085] [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: 12/18/2023] [Revised: 02/28/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVES The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency. MATERIALS AND METHODS Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model's performance was evaluated against ground-truth answers created by faculty cardiologists. RESULTS The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM's inherent limitations, such as misinterpreting numbers or hallucinations. CONCLUSION The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.
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Affiliation(s)
- Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Son Q Duong
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Division of Pediatric Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Joshua Lampert
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Patricia Kovatch
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Robert Freeman
- Department of Population Health Science and Policy, Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Edgar Argulian
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Lori Croft
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Stamatios Lerakis
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Martin Goldman
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, United States
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
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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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Gu Z, He X, Yu P, Jia W, Yang X, Peng G, Hu P, Chen S, Chen H, Lin Y. Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model. Artif Intell Med 2024; 150:102822. [PMID: 38553162 DOI: 10.1016/j.artmed.2024.102822] [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: 06/22/2023] [Revised: 01/28/2024] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. OBJECTIVE This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. METHODS Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset "Chinese Stroke Clinical Records" (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined "CliRoberta" through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. RESULTS Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. CONCLUSION Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.
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Affiliation(s)
- Zhanzhong Gu
- School of Electrical and Data Engineering, University of Technology Sydney, NSW, 2007, Australia.
| | - Xiangjian He
- School of Electrical and Data Engineering, University of Technology Sydney, NSW, 2007, Australia; School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
| | - Ping Yu
- School of Computing and Information Technology, University of Wollongong, NSW, 2522, Australia
| | - Wenjing Jia
- School of Electrical and Data Engineering, University of Technology Sydney, NSW, 2007, Australia
| | - Xiguang Yang
- School of Electrical and Data Engineering, University of Technology Sydney, NSW, 2007, Australia
| | - Gang Peng
- Intergenepharm Pty Ltd, Sydney, NSW, 2000, Australia
| | - Penghui Hu
- Department of Oncology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shiyan Chen
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hongjie Chen
- Department of Traditional Chinese Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yiguang Lin
- Department of Traditional Chinese Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, China; School of Life Sciences, University of Technology Sydney, NSW, 2007, Australia
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10
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Shiokawa N, Izumo M, Shimamura T, Kurosaka Y, Sato Y, Okamura T, Akashi YJ. Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice. J Clin Med 2024; 13:1861. [PMID: 38610628 PMCID: PMC11012797 DOI: 10.3390/jcm13071861] [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: 03/13/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (-2.41 mm and -1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.
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Affiliation(s)
- Noriko Shiokawa
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Masaki Izumo
- Department of Cardiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (Y.S.); (Y.J.A.)
| | - Toshio Shimamura
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Yui Kurosaka
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Yukio Sato
- Department of Cardiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (Y.S.); (Y.J.A.)
| | - Takanori Okamura
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Yoshihiro Johnny Akashi
- Department of Cardiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (Y.S.); (Y.J.A.)
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11
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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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12
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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13
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Bradley AJ, Ghawanmeh M, Govi AM, Covas P, Panjrath G, Choi AD. Emerging Roles for Artificial Intelligence in Heart Failure Imaging. Heart Fail Clin 2023; 19:531-543. [PMID: 37714592 DOI: 10.1016/j.hfc.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.
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Affiliation(s)
- Andrew J Bradley
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
| | - Malik Ghawanmeh
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ashley M Govi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Pedro Covas
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Gurusher Panjrath
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/PanjrathG
| | - Andrew D Choi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/AChoiHeart
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14
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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15
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Cheng CY, Wu CC, Chen HC, Hung CH, Chen TY, Lin CHR, Chiu IM. Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography. Front Cardiovasc Med 2023; 10:1195235. [PMID: 37600054 PMCID: PMC10436508 DOI: 10.3389/fcvm.2023.1195235] [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: 03/28/2023] [Accepted: 07/13/2023] [Indexed: 08/22/2023] Open
Abstract
Objectives The aim of this study was to develop a deep-learning pipeline for the measurement of pericardial effusion (PE) based on raw echocardiography clips, as current methods for PE measurement can be operator-dependent and present challenges in certain situations. Methods The proposed pipeline consisted of three distinct steps: moving window view selection (MWVS), automated segmentation, and width calculation from a segmented mask. The MWVS model utilized the ResNet architecture to classify each frame of the extracted raw echocardiography files into selected view types. The automated segmentation step then generated a mask for the PE area from the extracted echocardiography clip, and a computer vision technique was used to calculate the largest width of the PE from the segmented mask. The pipeline was applied to a total of 995 echocardiographic examinations. Results The proposed deep-learning pipeline exhibited high performance, as evidenced by intraclass correlation coefficient (ICC) values of 0.867 for internal validation and 0.801 for external validation. The pipeline demonstrated a high level of accuracy in detecting PE, with an area under the receiving operating characteristic curve (AUC) of 0.926 (95% CI: 0.902-0.951) for internal validation and 0.842 (95% CI: 0.794-0.889) for external validation. Conclusion The machine-learning pipeline developed in this study can automatically calculate the width of PE from raw ultrasound clips. The novel concepts of moving window view selection for image quality control and computer vision techniques for maximal PE width calculation seem useful in the field of ultrasound. This pipeline could potentially provide a standardized and objective approach to the measurement of PE, reducing operator-dependency and improving accuracy.
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Affiliation(s)
- Chi-Yung Cheng
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Cheng-Ching Wu
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Cardiology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- Division of Cardiology, Department of Internal Medicine, E-Da Cancer Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Huang-Chung Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | | | - Tien-Yu Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Chun-Hung Richard Lin
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - I-Min Chiu
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
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16
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Cocchieri R, van de Wetering B, Baan J, Driessen A, Riezebos R, van Tuijl S, de Mol B. The evolution of technical prerequisites and local boundary conditions for optimization of mitral valve interventions-Emphasis on skills development and institutional risk performance. Front Cardiovasc Med 2023; 10:1101337. [PMID: 37547244 PMCID: PMC10402900 DOI: 10.3389/fcvm.2023.1101337] [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: 11/17/2022] [Accepted: 03/29/2023] [Indexed: 08/08/2023] Open
Abstract
This viewpoint report describes how the evolution of transcatheter mitral valve intervention (TMVI) is influenced by lessons learned from three evolutionary tracks: (1) the development of treatment from mitral valve surgery (MVS) to transcutaneous procedures; (2) the evolution of biomedical engineering for research and development resulting in predictable and safe clinical use; (3) the adaptation to local conditions, impact of transcatheter aortic valve replacement (TAVR) experience and creation of infrastructure for skills development and risk management. Thanks to developments in computer science and biostatistics, an increasing number of reports regarding clinical safety and effectiveness is generated. A full toolbox of techniques, devices and support technology is now available, especially in surgery. There is no doubt that the injury associated with a minimally invasive access reduces perioperative risks, but it may affect the effectiveness of the treatment due to incomplete correction. Based on literature, solutions and performance standards are formulated with an emphasis in technology and positive outcome. Despite references to Heart Team decision making, boundary conditions such as hospital infrastructure, caseload, skills training and perioperative risk management remain underexposed. The role of Biomedical Engineering is exclusively defined by the Research and Development (R&D) cycle including the impact of human factor engineering (HFE). Feasibility studies generate estimations of strengths and safety limitations. Usability testing reveals user friendliness and safety margins of clinical use. Apart from a certification requirement, this information should have an impact on the definition of necessary skills levels and consequent required training. Physicians Preference Testing (PPT) and use of a biosimulator are recommended. The example of the interaction between two Amsterdam heart centers describes the evolution of a professional ecosystem that can facilitate innovation. Adaptation to local conditions in terms of infrastructure, referrals and reimbursement, appears essential for the evolution of a complete mitral valve disease management program. Efficacy of institutional risk management performance (IRMP) and sufficient team skills should be embedded in an appropriate infrastructure that enables scale and offers complete and safe solutions for mitral valve disease. The longstanding evolution of mitral valve therapies is the result of working devices embedded in an ecosystem focused on developing skills and effective risk management actions.
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Affiliation(s)
| | | | - Jan Baan
- Amsterdam University Center, Technical University Eindhoven, Amsterdam, Netherlands
| | - Antoine Driessen
- Amsterdam University Center, Technical University Eindhoven, Amsterdam, Netherlands
| | | | | | - Bas de Mol
- LifeTec Group BV, Eindhoven, Netherlands
- Amsterdam University Center, Technical University Eindhoven, Amsterdam, Netherlands
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17
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Sanfilippo F, La Via L, Dezio V, Amelio P, Genoese G, Franchi F, Messina A, Robba C, Noto A. Inferior vena cava distensibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on mechanically ventilated patients. Intensive Care Med Exp 2023; 11:40. [PMID: 37423948 DOI: 10.1186/s40635-023-00529-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/03/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND Variation of inferior vena cava (IVC) is used to predict fluid-responsiveness, but the IVC visualization with standard sagittal approach (SC, subcostal) cannot be always achieved. In such cases, coronal trans-hepatic (TH) window may offer an alternative, but the interchangeability of IVC measurements in SC and TH is not fully established. Furthermore, artificial intelligence (AI) with automated border detection may be of clinical value but it needs validation. METHODS Prospective observational validation study in mechanically ventilated patients with pressure-controlled mode. Primary outcome was the IVC distensibility (IVC-DI) in SC and TH imaging, with measurements taken both in M-Mode or with AI software. We calculated mean bias, limits of agreement (LoA), and intra-class correlation (ICC) coefficient. RESULTS Thirty-three patients were included. Feasibility rate was 87.9% and 81.8% for SC and TH visualization, respectively. Comparing imaging from the same anatomical site acquired with different modalities (M-Mode vs AI), we found the following IVC-DI differences: (1) SC: mean bias - 3.1%, LoA [- 20.1; 13.9], ICC = 0.65; (2) TH: mean bias - 2.0%, LoA [- 19.3; 15.4], ICC = 0.65. When comparing the results obtained from the same modality but from different sites (SC vs TH), IVC-DI differences were: (3) M-Mode: mean bias 1.1%, LoA [- 6.9; 9.1], ICC = 0.54; (4) AI: mean bias 2.0%, LoA [- 25.7; 29.7], ICC = 0.32. CONCLUSIONS In patients mechanically ventilated, AI software shows good accuracy (modest overestimation) and moderate correlation as compared to M-mode assessment of IVC-DI, both for SC and TH windows. However, precision seems suboptimal with wide LoA. The comparison of M-Mode or AI between different sites yields similar results but with weaker correlation. Trial registration Reference protocol: 53/2022/PO, approved on 21/03/2022.
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Affiliation(s)
- Filippo Sanfilippo
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy.
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy.
| | - Luigi La Via
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy
| | - Veronica Dezio
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy
| | - Paolo Amelio
- School of Anaesthesia and Intensive Care, University "Magna Graecia", Catanzaro, Italy
| | - Giulio Genoese
- Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy
| | - Federico Franchi
- Anesthesia and Intensive Care Unit, University Hospital of Siena, University of Siena, Siena, Italy
| | - Antonio Messina
- Humanitas Clinical and Research Center, IRCCS, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, MI, Italy
| | - Chiara Robba
- Department of Surgical Science and Diagnostic Integrated, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alberto Noto
- Department of Human Pathology of the Adult and Evolutive Age "Gaetano Barresi", Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy
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18
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Prandi FR, Lerakis S, Belli M, Illuminato F, Margonato D, Barone L, Muscoli S, Chiocchi M, Laudazi M, Marchei M, Di Luozzo M, Kini A, Romeo F, Barillà F. Advances in Imaging for Tricuspid Transcatheter Edge-to-Edge Repair: Lessons Learned and Future Perspectives. J Clin Med 2023; 12:jcm12103384. [PMID: 37240489 DOI: 10.3390/jcm12103384] [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: 03/22/2023] [Revised: 04/20/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Severe tricuspid valve (TV) regurgitation (TR) has been associated with adverse long-term outcomes in several natural history studies, but isolated TV surgery presents high mortality and morbidity rates. Transcatheter tricuspid valve interventions (TTVI) therefore represent a promising field and may currently be considered in patients with severe secondary TR that have a prohibitive surgical risk. Tricuspid transcatheter edge-to-edge repair (T-TEER) represents one of the most frequently used TTVI options. Accurate imaging of the tricuspid valve (TV) apparatus is crucial for T-TEER preprocedural planning, in order to select the right candidates, and is also fundamental for intraprocedural guidance and post-procedural follow-up. Although transesophageal echocardiography represents the main imaging modality, we describe the utility and additional value of other imaging modalities such as cardiac CT and MRI, intracardiac echocardiography, fluoroscopy, and fusion imaging to assist T-TEER. Developments in the field of 3D printing, computational models, and artificial intelligence hold great promise in improving the assessment and management of patients with valvular heart disease.
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Affiliation(s)
- Francesca Romana Prandi
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
- Department of Cardiology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Stamatios Lerakis
- Department of Cardiology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Martina Belli
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
- Cardiovascular Imaging Unit, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Federica Illuminato
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Davide Margonato
- Cardiovascular Imaging Unit, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Lucy Barone
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Saverio Muscoli
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Marcello Chiocchi
- Department of Diagnostic Imaging and Interventional Radiology, Tor Vergata University, 00133 Rome, Italy
| | - Mario Laudazi
- Department of Diagnostic Imaging and Interventional Radiology, Tor Vergata University, 00133 Rome, Italy
| | - Massimo Marchei
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Marco Di Luozzo
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Annapoorna Kini
- Department of Cardiology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Francesco Romeo
- Department of Departmental Faculty of Medicine, Unicamillus-Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy
| | - Francesco Barillà
- Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
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19
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Sanfilippo F, La Via L, Dezio V, Santonocito C, Amelio P, Genoese G, Astuto M, Noto A. Assessment of the inferior vena cava collapsibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on healthy volunteers. Intensive Care Med Exp 2023; 11:15. [PMID: 37009935 PMCID: PMC10068684 DOI: 10.1186/s40635-023-00505-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/22/2023] [Indexed: 04/04/2023] Open
Abstract
PURPOSE Assessment of the inferior vena cava (IVC) respiratory variation may be clinically useful for the estimation of fluid-responsiveness and venous congestion; however, imaging from subcostal (SC, sagittal) region is not always feasible. It is unclear if coronal trans-hepatic (TH) IVC imaging provides interchangeable results. The use of artificial intelligence (AI) with automated border tracking may be helpful as part of point-of-care ultrasound but it needs validation. METHODS Prospective observational study conducted in spontaneously breathing healthy volunteers with assessment of IVC collapsibility (IVCc) in SC and TH imaging, with measures taken in M-mode or with AI software. We calculated mean bias and limits of agreement (LoA), and the intra-class correlation (ICC) coefficient with their 95% confidence intervals. RESULTS Sixty volunteers were included; IVC was not visualized in five of them (n = 2, both SC and TH windows, 3.3%; n = 3 in TH approach, 5%). Compared with M-mode, AI showed good accuracy both for SC (IVCc: bias - 0.7%, LoA [- 24.9; 23.6]) and TH approach (IVCc: bias 3.7%, LoA [- 14.9; 22.3]). The ICC coefficients showed moderate reliability: 0.57 [0.36; 0.73] in SC, and 0.72 [0.55; 0.83] in TH. Comparing anatomical sites (SC vs TH), results produced by M-mode were not interchangeable (IVCc: bias 13.9%, LoA [- 18.1; 45.8]). When this evaluation was performed with AI, such difference became smaller: IVCc bias 7.7%, LoA [- 19.2; 34.6]. The correlation between SC and TH assessments was poor for M-mode (ICC = 0.08 [- 0.18; 0.34]) while moderate for AI (ICC = 0.69 [0.52; 0.81]). CONCLUSIONS The use of AI shows good accuracy when compared with the traditional M-mode IVC assessment, both for SC and TH imaging. Although AI reduces differences between sagittal and coronal IVC measurements, results from these sites are not interchangeable.
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Affiliation(s)
- Filippo Sanfilippo
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy.
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy.
| | - Luigi La Via
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy
| | - Veronica Dezio
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy
| | - Cristina Santonocito
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy
| | - Paolo Amelio
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy
| | - Giulio Genoese
- Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy
| | - Marinella Astuto
- Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy
- School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy
| | - Alberto Noto
- Department of Human Pathology of the Adult and Evolutive Age "Gaetano Barresi", Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy
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20
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The Role of Artificial Intelligence in Echocardiography. J Imaging 2023; 9:jimaging9020050. [PMID: 36826969 PMCID: PMC9962859 DOI: 10.3390/jimaging9020050] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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21
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Hidalgo EM, Wright L, Isaksson M, Lambert G, Marwick TH. Current Applications of Robot-Assisted Ultrasound Examination. JACC Cardiovasc Imaging 2023; 16:239-247. [PMID: 36648034 DOI: 10.1016/j.jcmg.2022.07.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/06/2022] [Accepted: 07/21/2022] [Indexed: 11/06/2022]
Abstract
Despite advances in miniaturization and automation, the need for expert acquisition of a full echocardiogram, including Doppler, has restricted access in remote areas. Recent developments in robotics, teleoperation, and upgraded telecommunications infrastructure may provide a solution to this deficiency. Robot-assisted teleoperated ultrasound examination can aid medical diagnosis in remote locations and may improve health inequalities between rural and urban settings. This review aimed to analyze the status of teleoperated robotic systems for ultrasound examinations, evaluate clinical and preclinical applications, identify limitations, and outline future directions for clinical use. Overall, robot-assisted teleoperated ultrasound is feasible and safe in the reported clinical and preclinical studies, with the robots able to follow the hand movements performed by sonographers and researchers from a distance or in local networks. Moreover, multiple types of ultrasound examinations have been performed in remote areas, with a high success rate nearly comparable to that of conventional sonography. The studies showed that although a low-bandwidth link can be used to control a robot, the bandwidth requirements for real-time transmission of video and ultrasound images are significantly higher. Furthermore, if haptic feedback is implemented, the bandwidth requirements are increased. Haptically enabled systems that improve robotic control are necessary for accelerating the introduction to clinical use. Haptic feedback and enhanced front-end interface control for remote users are vital aspects required for clinical application. The incorporation of artificial intelligence through either aiding in window acquisition (knowledge of anatomical landmarks to adjust scanning planes) or through measurement and disease identification is yet to be researched. However, it has the potential to lead to dramatic advances. A new generation of robots is in development, and several projects in the preclinical stage reveal a promising future to overcome the shortage of health professionals in remote areas.
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Affiliation(s)
- Edgar M Hidalgo
- Department of Mechanical Engineering and Product Design Engineering, Swinburne University of Technology, Melbourne, Australia
| | - Leah Wright
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Mats Isaksson
- Department of Mechanical Engineering and Product Design Engineering, Swinburne University of Technology, Melbourne, Australia
| | - Gavin Lambert
- Department of Mechanical Engineering and Product Design Engineering, Swinburne University of Technology, Melbourne, Australia; Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia
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22
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Edwards LA, Feng F, Iqbal M, Fu Y, Sanyahumbi A, Hao S, McElhinney DB, Ling XB, Sable C, Luo J. Machine Learning for Pediatric Echocardiographic Mitral Regurgitation Detection. J Am Soc Echocardiogr 2023; 36:96-104.e4. [PMID: 36191670 DOI: 10.1016/j.echo.2022.09.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 09/23/2022] [Accepted: 09/24/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND Echocardiography-based screening for valvular disease in at-risk asymptomatic children can result in early diagnosis. These screening programs, however, are resource intensive and may not be feasible in many resource-limited settings. Automated echocardiographic diagnosis may enable more widespread echocardiographic screening, early diagnosis, and improved outcomes. In this feasibility study, the authors sought to build a machine learning model capable of identifying mitral regurgitation (MR) on echocardiography. METHODS Echocardiograms were labeled by clip for view and by frame for the presence of MR. The labeled data were used to build two convolutional neural networks to perform the stepwise tasks of classifying the clips (1) by view and (2) by the presence of any MR, including physiologic, in parasternal long-axis color Doppler views. The view classification model was developed using 66,330 frames, and model performance was evaluated using a hold-out testing data set with 45 echocardiograms (11,730 frames). The MR detection model was developed using 938 frames, and model performance was evaluated using a hold-out testing data set with 42 echocardiograms (182 frames). Metrics to evaluate model performance included accuracy, precision, recall, F1 score (average of precision and recall, ranging from 0 to 1, with 1 suggesting perfect precision and recall), and receiver operating characteristic analysis. RESULTS For the parasternal long-axis view with color Doppler, the view classification convolutional neural network achieved an F1 score of 0.97. The MR detection convolutional neural network achieved testing accuracy of 0.86 and an area under the receiver operating characteristic curve of 0.91. CONCLUSIONS A machine learning model is capable of discerning MR on transthoracic echocardiography. This is an encouraging step toward machine learning-based diagnosis of valvular heart disease on pediatric echocardiography.
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Affiliation(s)
- Lindsay A Edwards
- Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington
| | - Fei Feng
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Mehreen Iqbal
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Yong Fu
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Amy Sanyahumbi
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | - Shiying Hao
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Palo Alto, California
| | - Doff B McElhinney
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Palo Alto, California
| | - X Bruce Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, California
| | - Craig Sable
- Department of Pediatrics, Children's National Health System, Washington, District of Columbia
| | - Jiajia Luo
- Biomedical Engineering Department, Peking University, Beijing, China.
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23
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Otto CM. Heartbeat: knowledge gaps for non-bacterial thrombotic endocarditis. Heart 2022; 108:1583-1585. [PMID: 36162848 DOI: 10.1136/heartjnl-2022-321875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Catherine M Otto
- Division of Cardiology, University of Washington, Seattle, Washington, USA
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24
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Tsang W, Nedadur R. Risk Assessment in Secondary Mitral Regurgitation: A Step Toward Clarity? JACC. ADVANCES 2022; 1:100074. [PMID: 38938398 PMCID: PMC11198685 DOI: 10.1016/j.jacadv.2022.100074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
- Wendy Tsang
- Division of Cardiology, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Rashmi Nedadur
- Division of Cardiovascular Surgery, University of Toronto, Toronto, Canada
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25
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Austin DE, Lee DS, Wang CX, Ma S, Wang X, Porter J, Wang B. Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure. Int J Cardiol 2022; 365:78-84. [PMID: 35868354 DOI: 10.1016/j.ijcard.2022.07.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/26/2022] [Accepted: 07/17/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints. METHODS Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration. RESULTS Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI,0.769, 1.139) and at 30 days (-0.026; 95% CI, -0.374, 0.322 and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches. CONCLUSIONS Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.
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Affiliation(s)
| | - Douglas S Lee
- ICES, Institute for Clinical Evaluative Sciences; Peter Munk Cardiac Centre of University Health Network; Ted Roger Centre for Heart Research.
| | - Chloe X Wang
- ICES, Institute for Clinical Evaluative Sciences; Division of Vascular Surgery, University Health Network
| | - Shihao Ma
- ICES, Institute for Clinical Evaluative Sciences; Department of Computer Science, University of Toronto; Vector Institute of Artificial Intelligence
| | - Xuesong Wang
- ICES, Institute for Clinical Evaluative Sciences
| | - Joan Porter
- ICES, Institute for Clinical Evaluative Sciences
| | - Bo Wang
- ICES, Institute for Clinical Evaluative Sciences; Peter Munk Cardiac Centre of University Health Network; Department of Computer Science, University of Toronto; Vector Institute of Artificial Intelligence; Division of Vascular Surgery, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
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