Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms

Ragnarsdóttir, Hanna; Manduchi, Laura; Michel, Holger; Laumer, Fabian; Wellmann, Sven; Özkan, Ece; Vogt, Julia E.

doi:10.1007/978-3-031-16788-1_32

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…Many studies have focused on the effects of AI on pulmonary hypertension, from the prediction of this rare pathology in adults [ 147 , 150 , 155 , 161 , 162 ] or children [ 151 , 163 , 165 ] to the prediction of survival [ 154 , 167 ] or risk in patients with pulmonary hypertension [ 156 ], diagnosis of pulmonary hypertension [ 149 , 152 , 153 , 156 , 157 , 158 , 160 , 169 ], and the treatment of this disease [ 148 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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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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Section: Literature Reviewmentioning

confidence: 99%

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

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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“…The aims of these articles were diverse and included the automatic calculation of the LV volume and ejection fraction 28,35 ; the automatic segmentation of cardiac structures as LV/RV, myocardium, interventricular septal, and posterior LV wall 39,51,55 ; and classification approaches. Classification was investigated in the context of cardiac disorders such as mitral valve diseases, 48 myocardial injury, 50 coronary artery disease (CAD), 34,54 cardiomyopathy, 33 congenital heart disease, 40 and HF 32 ; even in newborn 49 for the presence of intracardiac devices (eg, catheters, pacemaker, and defibrillator leads) or motion 18 ; for severe left atrial dilation and LV hypertrophy 43 ; and for CMR image view. 27 In order to improve the interpretability of the segmentation/prediction/classification results, these articles generally focused on DL-based XAI methods as class activation mapping or Grad-CAM, SmoothGrad, saliency maps, testing with concept activation vectors, and guided backpropagation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Grad-CAM was used in 7 cardiac imaging studies, either for classification 18,34,40,48–50 or segmentation. 51 The latter in particular proposed a new interpretable CNN model (fast and accurate echocardiographic automatic segmentation based on U-Net) that integrates U-net architecture and transfer learning (from Visual Geometry Group 19) to segment 2-dimensional echocardiography of 88 patients into 3 regions (LV, interventricular septal, and posterior LV wall).…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, Pérez-Pelegrıó et al 28 developed a new explainable approach that combines class activation mapping with U-net to automatically estimate the LV volume in end diastole and obtain the result in the form of a segmentation mask without segmentation labels to train the algorithm. Grad-CAM was used in 7 cardiac imaging studies, either for classification 18,34,40,[48][49][50] or segmentation. 51 The latter in particular proposed a new interpretable CNN model (fast and accurate echocardiographic automatic segmentation based on U-Net) that integrates U-net architecture and transfer learning (from Visual Geometry Group 19) to segment 2-dimensional echocardiography of 88 patients into 3 regions (LV, interventricular septal, and posterior LV wall).…”

Section: Literature Reviewmentioning

confidence: 99%

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Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

Salih

Galazzo

Gkontra

et al. 2023

Circ: Cardiovascular Imaging

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Artificial intelligence applications have shown success in different medical and health care domains, and cardiac imaging is no exception. However, some machine learning models, especially deep learning, are considered black box as they do not provide an explanation or rationale for model outcomes. Complexity and vagueness in these models necessitate a transition to explainable artificial intelligence (XAI) methods to ensure that model results are both transparent and understandable to end users. In cardiac imaging studies, there are a limited number of papers that use XAI methodologies. This article provides a comprehensive literature review of state-of-the-art works using XAI methods for cardiac imaging. Moreover, it provides simple and comprehensive guidelines on XAI. Finally, open issues and directions for XAI in cardiac imaging are discussed.

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A review of evaluation approaches for explainable AI with applications in cardiology

Salih,

Galazzo,

Gkontra

et al. 2024

Artif Intell Rev

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Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

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Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms

Cited by 4 publications

References 28 publications

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

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

Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

A review of evaluation approaches for explainable AI with applications in cardiology

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