Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping

Ho, Eric S.; Z, Ding

doi:10.1016/j.artmed.2022.102342

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…They employed Grad-CAM technology to provide further evidence of the self-learning capability of the model, and the visual results obtained were evaluated by expert pathologists. Ho and Ding [39] conducted research on stroke, building a CNNbased binary classifier to distinguish ECGs with stroke symptoms. They also leveraged Grad-CAM to aid model interpretation by illuminating subtle ECG patterns recognized by the model.…”

Section: Analysis and Discussionmentioning

confidence: 99%

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Reviewing CAM-Based Deep Explainable Methods in Healthcare

Tang,

Chen,

Ren

et al. 2024

Applied Sciences

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The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases—PubMed, Science Direct, and Web of Science—and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field.

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Section: Analysis and Discussionmentioning

confidence: 99%

“…Figure 7. The accuracy of each study[16][17][18][20][21][22][23][24][25][26][27][28][29][30][31][32][33][35][36][37][38][39][40][41][42][43]45,47,48,[50][51][52][53][54][55][56]58,59]. …”

mentioning

confidence: 99%

Reviewing CAM-Based Deep Explainable Methods in Healthcare

Tang,

Chen,

Ren

et al. 2024

Applied Sciences

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“…Example 1. Class activation mapping tool to visualise where exactly the DL model focusses on making a decision on a test set [382]. Te shoulder classifcation task is chosen as an example (see Figure 15).…”

Section: Ethicsmentioning

confidence: 99%

Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

Alzubaidi,

Al-Sabaawi,

Bai

et al. 2023

International Journal of Intelligent Systems

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Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

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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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Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping

Cited by 11 publications

References 17 publications

Reviewing CAM-Based Deep Explainable Methods in Healthcare

Reviewing CAM-Based Deep Explainable Methods in Healthcare

Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

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

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