artificial intelligence in cardiology

Nguyen A, Yosinski J, Clune J. In the case of harm of the patients, this accountability gap can affect their autonomy and violate their rights (96). (2019) 25:223. 34. The FDA has three levels of clearance for AI/ML based algorithms. - Artificial intelligence is a computer science field that studies the problem of building agents which take the best possible course of action in a specific situation. MLP is used in speech recognition, machine translation and complex classification. Jetley S, Lord NA, Namhoon L, Torr PHS. , Gersh BJ, Bhatt DL. 2022;1(1):003. 5 As the quantity of individual patient data grows to include external automatic monitoring of streaming data from IoT and other complex biomedical sources, cardiologists will need to Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). The classification CNN achieved accuracy between 0.989 and 0.998, whilst the quality control CNN achieved accuracies of 0.861 for 2-chamber, 0.806 for 3-chamber, and 0.859 for 4-chamber views. Sethi Y, Patel N, Kaka N, Kaiwan O, Kar J, Moinuddin A, Goel A, Chopra H, Cavalu S. J Clin Med. is supported by UCL Hospitals (University College London) NIHR (National Institute for Health Research) Biomedical Research Centre, and the EU/EFPIA (European Union/European Federation of Pharmaceutical Industries and Associations) Innovative Medicines Initiative 2 Joint Undertaking [emailprotected] (116074). It can rapidly identify subtle changes in EF and aid the precise diagnosis of CVD in real time (54). Folkert W Asselbergs, Alan G Fraser, Artificial intelligence in cardiology: the debate continues, European Heart Journal - Digital Health, Volume 2, Issue 4, December 2021, Pages 721726, https://doi.org/10.1093/ehjdh/ztab090. It is inspired by the complexity of the human brain in handling data and generating patterns, used for decision making (1). Kalscheur MM, Kipp RT, Tattersall MC, Mei C, Buhr KA, DeMets DL, et al. AI Magaz. It incorporates the ML methods are applied in CTAs, to maximise information extraction via image acquisition, and improve diagnostic accuracy and prognostic outcomes via precision risk stratification. Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, von Arx S, et al. doi: 10.1016/j.cjca.2021.11.003, 21. Schlapfer J, Wellens HJ. J Med Internet Res. National Defense Medical Center - Department of Artificial Intelligence and Internet of Things. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Purpose To compare the diagnostic accuracy of radiologists with different levels of experience with and without assistance of AI in CT evaluation for COVID-19 pneumonia and to develop an optimized B S Can J Cardiol. doi: 10.1109/BMEI.2010.5639619, 76. 107. The Author(s) 2021. At the European Society of Cardiology (ESC) Congress 2021, a Great Debate considered the question Artificial Intelligence in Cardiology: a Marriage Made in Heaven or Hell?. , Tschpe C, de Boer RA. Some AI systems have been built with continuous updates, but this could potentially result to drift with time. Sinus rhythm (SR), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), and premature atrial contraction (PAC) were classified with accuracy of 100, 98.66, 100, 99.66, and 100, respectively (35). Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The Future is Already Here. Can J Cardiol. Once implemented it is self-directed, thus eliminating manual human interaction. official website and that any information you provide is encrypted Would you like email updates of new search results? This study demonstrates the future potential for high quality automated cine CMR analysis from the scanner to report (72). It can extract important results from vast amount of data through iterative processing (10). Artificial intelligence applications in cardiovascular research are increasingly becoming more popular over the last decade (Figure 2). A quickly evolving field, artificial intelligence (AI) offers endless possibilities with Northwestern Medicine leading the way. The opposing arguments distil into some key questions. Keeping up-to-date with the scientific literature or even guidelines is almost impossible, considering their volume; AI could help to find and analyse the wealth of data available in the public domain, thereby supporting researchers and healthcare professionals to provide the best care according to the latest evidence.2 Artificial intelligence can optimize logistics and operations in a hospital, increase efficiency, and reduce the administrative burden on healthcare professionals and physicians,3 for example by automatic labelling using natural language processing4 or by scheduling patients according to their forecasted attendance.5 In the near future, these applications will be extended with conversational AI that will reduce the time spent on electronic health records by automation of clinical notes and ordering. The CardioCube voice application enhances paperless medical history taking, in an outpatient cardiology clinic in Los Angeles. Multiple different CNN architectures have been created over the last few years. To learn more, visit The Apple Heart Study showed that the utilisation of smartphones was effective in identifying patients with subclinical paroxysmal AF. (2021) 28:598605. Circulation. AI algorithms have been broadly used for diagnosis from an image, image segmentation and reconstruction, image quality control, patient prognostication, phenogrouping, and gaining of scientific insight. DL based MRI reconstruction is based on a model which learns the factors of the reconstruction procedure beforehand, so that it can be applied to all new data as a simple operation. The use of fluoroscopy has been a Data from 9,572 patients undergoing anatomical (4,734) or functional (4,838) imaging were used to create a topological presentation of the study populations, based on 57 pre-randomisation variables. This process was repeated using different scenarios and Dr. Gehi received other correct answers. Another recent development, which aims to mitigate the famous issue of black-box AI methodologies, is explainable AI (XAI). ACM, all-cause mortality; ACS, acute coronary syndrome; AI, artificial intelligence; AF, atrial fibrillation; AHA, American heart association; ALVD, asymptomatic left ventricular dysfunction; AMI, acute myocardial infarction; AUC, area under the curve; CACS, coronary artery calcium scoring; CAD, coronary artery disease; CNN, convolutional neural network; CRT, cardiac resynchronisation therapy; CT, computerised tomography; CTA, cardiac computed tomography angiography; CTP, computed tomography myocardial perfusion; CVD, cardiovascular disease; DD, diastolic dysfunction; DL, deep learning; DUN, deep unified network; ECG, electrocardiogram; ECR, early coronary revascularisation; EF, ejection fraction; EHR, electronic health record; ESC, European society cardiology; FAI, fat attenuation index; FDA, food and drug administration; FNNN, feed forward neural network; HCM, hypertrophic cardiomyopathy; HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HR, heart rate; ICA, invasive coronary angiography; IoT, internet of things; LBBB, left bundle branch block; LR, logistic regression; LS, longitudinal strain; LV, left ventricle; LVDF, Left ventricular diastolic function; LVEDV, left ventricle end diastolic volume; LVEF, left ventricular ejection fraction; LVESV, left ventricle end systolic volume; LVH, left ventricular hypertrophy; LVM, left ventricle mass; MACE, major adverse cardiac event; ML, machine learning; MLP, multiplayer perceptron; MNN, modular neural network; MPI, myocardial perfusion imaging; MR, mitral regurgitation; PAH, pulmonary arterial hypertension; PCI, percutaneous coronary intervention; PVC, premature ventricular contraction; RBBB, right bundle branch block; RBFN, radial basis function network; RCT, randomised controlled trial; RF, random forests; RNN, recurrent neural network; ROC, receiver operator characteristic; RV, right ventricle; RVEDV, right ventricle end diastolic volume; RVESV, right ventricle end systolic volume; SPECT, single-photon emission computed tomography; SR, sinus rhythm; SVM, support vector machine; TPD, total perfusion deficit. There are different types of activation functions, depending on the input values (14). The definitions are laid out in two scopes. , Powers B, Vogeli C, Mullainathan S. Matheny An important example consists of the relationship between the presence and extend of LGE and adverse outcomes, in patients with HCM. Artificial intelligence empowers primary care physicians and non-cardiologists by providing automated electrocardiographic (ECG) diagnoses that can guide decisions whether to treat or to refer for specialist cardiological care.8 Algorithms can detect not only ischaemia or arrhythmias but also ECG signs of diminished ejection fraction, heart valve disease, or risk for atrial fibrillation.9 In future, this knowledge will not be limited to healthcare professionals but extended to individuals using smartphone applications.10, Nowadays, numerous types of data from different sources are available to physicians. Triggiani V, Lisco G, Renzulli G, Frasoldati A, Guglielmi R, Garber J, Papini E. Front Endocrinol (Lausanne). Eur Heart J. (1992). Beyond Information, 1st Edn. (1985). Perioperative intelligence provides a framework for developing useful AI application for perioperative medicine ( Figure 2 ). Artificial intelligence algorithms can be exceptionally capable but they are fundamentally stupid. Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security. The availability of large-volume data from electronic health records (EHRs), mobile health devices and imaging data enables the rapid development of AI algorithms in medicine. Cardiac resynchronisation therapy (CRT) is fundamental to the management of symptomatic HF with left ventricular systolic dysfunction and intraventricular conduction delay (reduced EF and wide QRS complex). The Radial Basis Function Network (RBFN) is based on the radial basis function (activation function), which is included in the hidden layer. Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, et al. Each human observer must be trained separately, which limits the scalability of new methods. 110. A AI will be a part of every cardiologists daily routine to provide the opportunity for effective phenotyping of patients and design of predictive models for different diseases. (Ed.) The widespread use of Big Data in the field of AI, does not come without challenges. (2022). Early identification of ALVD and commencement of treatment, can prevent its progression to symptomatic HF and reduce mortality. NPJ Digit Med. Figure 1. Neural networks are classified depending on their structure, data flow, neurons used and their density. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. Zhao et al., used a support vector machine (SVM) and identified five common arrythmias from ECG tracings of a large dataset. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. 74. 2nd ed. EchoNet-Dynamic was created by using 10.030 apical four-chamber echocardiogram videos during training of the model. IEEE Access. Front Cardiovasc Med. AI is able to use very complex nonparametric models from a vast amount of data in comparison to simple parametric models requiring a suitable-sized data set used in statistics (10). The CNNs architecture is inspired by neurons in human and animal brains. Web2020 19 May. An official website of the United States government. A proposal for the dartmouth summer research proiect on articial intelligence. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. doi: 10.1109/CVPR.2015.7298640, 100. J Cardiovasc Magn Reson. Six convolutional blocks (convolution, batch normalisation, Relu, max pooling) extracted temporal features, one convolutional block (convolution, batch normalisation, Relu) extracted spatial features and two fully connected layers (fully connected, batch normalisation, Relu, dropout) regressed the features to a softmax activated output. A report for the National Academy of Medicine in the USA concluded that The challenges are unrealistic expectations, biased and non-representative data, inadequate prioritization of equity and inclusion, the risk of exacerbating health care disparities, low levels of trust, uncertain regulatory and tort environments, and inadequate evaluation before scaling narrow AI.32 Proponents and enthusiasts should not inflate expectations but ensure that research addresses the right questions. E The COREMD project (Coordinating Research and Evidence for Medical Devices) which is led by the ESC will develop recommendations for European regulators concerning the approval of AI algorithms as medical devices.33. Med Sci Monit. Kagiyama N, Piccirilli M, Yanamala N, Shrestha S, Farjo PD, Casaclang-Verzosa G, et al. Zhou D-X. The electrocardiogram (ECG) is considered the first-line non-invasive diagnostic investigation for the evaluation of cardiovascular pathology. An inexpensive, non-invasive test such as AI screening from ECG data, can be a powerful future tool for screening asymptomatic individuals (76). ML models were trained from 33 pre-implant clinical features, to predict 15-year ACM. The current available non-invasive diagnostic tests which detect coronary artery stenosis or stress-induced myocardial ischemia, are unable to detect these unstable non-obstructive plaques. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Traditional prediction models have limitations, including variations among the validation cohorts, a small number of predictors, and the absence of important variables. Pediatr Cardiol. J Nucl Cardiol. Hu L-H, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, et al. Patients with these ECG characteristics have greater benefit on reduction of mortality and readmissions after receiving CRT. (2018) 11:165463. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. Challen Nat Med. Hassanin M, Anwar S, Radwan I, Khan F, Mian A. MA (2018) 392:92939. doi: 10.1093/eurheartj/ehaa640, 91. arXiv [Preprint]. Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology. Again, the features of the data chosen to train the algorithms are chosen by the investigators and the AI-driven application can replicate the investigators preconceptions and biases (96). Che et al., added GAN-generated synthetic data to real patient data, leading to an improved CNN-based risk prediction model (23). This AI technologies can utilise such personal data, without obtaining the proper consent of the data subject or handle it in a manner personal information is revealed. Artificial intelligence (AI), described as the ability of a digital computer to perform tasks commonly associated with intelligent beings ( Copeland, 2020 ), is not a new concept. Each stream had an encoder-decoder U-net architecture. Artificial intelligence (AI) is quickly becoming a buzz word in medicine. The absence of a familiar logic behind its output, might lead the clinician who is interpreting it to pause. A ML model was used to design a prognostic algorithm which detected HF exacerbation and predicted rehospitalisation after a HF admission (95). A random forest algorithm consists of the output of multiple decision trees, to reach a single result. Miami, FL: (2013). Conventionally, patients eligible for CRT implantation, should have an ECG morphology with LBBB and QRS duration 150 ms. Additionally, the diagnosis that used the ML approach was less time consuming (within 10 s) and had less variability (69). The study showed that the ML algorithm was superior to the conventional risk prediction score, in both the moderate and high risk for CAD groups (59). Semigran (2020) 13:e006513. Applications of Artificial Intelligence in Cardiology. However, its interpretation can be time-consuming and challenging at times. doi: 10.1016/j.jacc.2006.08.045, 48. So, for this debate, what is the appropriate analogy? Hopkins CB, Suleman J, Cook C. An artificial neural network for the electrocardiographic diagnosis of left ventricular hypertrophy. SVM classifiers were used for classification and determination of the severity of mitral regurgitation (MR), a common valve disease. This can lead to a completely different prediction for the image the neural network analyses. Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. , Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Fletcher K01 HL124045/HL/NHLBI NIH HHS/United States. Eur J Prev Cardiol. Benjamens S, Dhunnoo P, Mesko B. WebArtificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. doi: 10.1161/CIRCULATIONAHA.117.030583, 42. Machine learning has enabled physicians to use data from ECGs and draw specific echocardiography results, without the use of the echocardiogram. doi: 10.1109/ICMLA.2013.158, 79. HL LHeureux A, Grolinger K, Elyamany HF, Capretz MAM. *Correspondence: Dunja Aksentijevic, d.aksentijevic@qmul.ac.uk, Frontiers in Cardiovascular Medicine: Rising Stars 2022, View all The first pre-processing step excluded still images. Exciting examples include: Early risk prediction of conditions such as embolic stroke Heart monitoring and arrhythmia detection in smart clothing projects based on a textile computing platform Of course, there is not one AI or ML but a variety of methods for performing supervised and unsupervised analyses that are more or less transparent in their operations, and so statements (including some used in this debate!) EMMA's artificial intelligence system uses sensors to measure muscle stiffness and calculate the acupoints in each person's body. The ASSIST tool was tested in the rest 20% of the PROMISE population and in an external validation cohort (from the SCOT-HEART trial), undergoing anatomical or functional testing as first assessment. DL is comprised of deep neural networks. Circulation. Effect size is essentially the quantification of the size of difference between two groups. Stress testing decision Support Tool), was developed using data from the PROMISE (PROspective Multicentre Imaging Study for Evaluation of Chest pain) trial. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Summary of the main arguments presented in the debate, set against the exponential growth of papers listed on Pubmed relating to machine learning (ML), since the terms artificial intelligence and ML were first included. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. ML incorporation to CMR, can lead to a more efficient scanning and accurate interpretation process. A UL1 TR000067/TR/NCATS NIH HHS/United States, U54 CA189201/CA/NCI NIH HHS/United States, R01 DK098242/DK/NIDDK NIH HHS/United States. Guidelines should also be developed for the purpose of evaluation of the products performance and the detection of deficits over time (106). The AI algorithms are complex, not always understood by their programmer, can generate surprisingly different results from what was expected and can lead to a change in the purpose, through the learning and development process. Since ML models learn on high dimensional correlations which exceed the interpretive abilities of humans, the rationale behind algorithmically produced outcomes which affect decision making for patients, can remain unjustifiable. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. doi: 10.1109/ACCESS.2019.2912200, 14. Fax: (919) 966-1743, Dr. Gehi Puts AI Intelligence System to the Test. The overall classification accuracy was 74.4%, with precisions of 78.79, 87.5, and 65.85% for ascertaining the healthy group, HF-prone group and HF group, respectively. They are available either on smart speakers or on smartphones. (Vol. There are no new data associated with this article. Posted: 17 Mar 2023, National Defense Medical Center - Division of Cardiology, National Defense Medical Center - Department of Artificial Intelligence and Internet of Things, National Defense Medical Center - Department of Medical Informatics, National Defense Medical Center - Department of Otolaryngology-Head and Neck Surgery, National Defense Medical Center - Division of Nephrology, National Defense Medical Center - School of Medicine. One can say that the ability to lead a private life, could be jeopardised (96). Atrial fibrillation detection can be a difficult task as the current diagnostic methods (pulse palpation, ECG, ambulatory Holter monitoring) all have limitations. Bus Horiz. (2018). More than 20 others cover CT and ultrasound systems or their reconstruction algorithms that are also used by cardiology. Whilst lower performance systems such as ML learning are more understandable, higher performance models such as DL techniques are difficult to comprehend even from the engineers or data scientists who created the algorithms, since they are directly created from data. Eur Heart J. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. 2019. Copyright 2023 European Society of Cardiology. Published by Oxford University Press on behalf of the European Society of Cardiology. Search for other works by this author on: School of Medicine, Cardiff University, University Hospital of Wales, Cardiovascular Imaging and Dynamics, Katholieke Universiteit Leuven. Please enable it to take advantage of the complete set of features! Generative adversarial networks in cardiology. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Zhao Q, Zhang L. ECG feature extraction and classification using wavelet transform and support vector machines. Curr Cardiol Rep. (2018) 20:139. doi: 10.1007/s11886-018-1074-8, 16. 8.4 and 2.4% of the population had indication for moderate and high-risk CAD, respectively. Artificial intelligence (AI) has sparked remarkable progress in various aspects of technology from speech recognition to automated driving. Various methods are being proposed for defence against such attacks, but none has been proven safe enough yet (27). The use of ML for CAD prediction aims to create risk stratification models that are more accurate and cost and time efficient in clinical practice, compared to conventional models (58). Stonier T. (1992). Dorado-Daz PI, Sampedro-Gmez J, Vicente-Palacios V, Snchez PL. Authors have opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet. There is now emerging evidence that AI may support diagnostics in electrophysiology by automating common clinical tasks or aiding complex tasks using deep neural networks that are superior to currently implemented computerised algorithms. It included data from 420,000 participants, with a median follow up time of 117 days. This integrated research program will occur in parallel with the advanced classes in which students do computationally enabled research advised by faculty in heart disease and computer science. Reclassification based on more precise phenotyping is needed to improve outcomes.12 A shift from a one-size fits all to a more data-driven approach will identify those patients who will benefit most from particular therapies. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. The following are key points to remember from this state-of-the art review on artificial intelligence (AI) to enhance clinical value across the spectrum of cardiovascular