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Fang Y, Xing X, Wang S, Walsh S, Yang G. Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19. Curr Opin Struct Biol 2024; 85:102778. [PMID: 38364679 DOI: 10.1016/j.sbi.2024.102778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/22/2024] [Accepted: 01/26/2024] [Indexed: 02/18/2024]
Abstract
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.
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Affiliation(s)
- Yingying Fang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Xiaodan Xing
- Bioengineering Department, Imperial College London, London W12 7SL, UK
| | - Shiyi Wang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK; Bioengineering Department, Imperial College London, London W12 7SL, UK; Imperial-X, Imperial College London, London W12 7SL, UK; Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK.
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Gautam A, Raghav P, Subramaniam V, Kumar S, Kumar S, Jain D, Verma A, Singh P, Singhal M, Gupta V, Rathore S, Iyengar S, Rathore S. Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study. Angiology 2024:33197231225286. [PMID: 38166442 DOI: 10.1177/00033197231225286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; -2000, 2000) in Bland-Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.
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Affiliation(s)
| | | | | | - Sunil Kumar
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Sudeep Kumar
- Department of Cardiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Dharmendra Jain
- Department of Cardiology, Banaras Hindu University, Varanasi, India
| | - Ashish Verma
- Department of Radiology, Banaras Hindu University, Varanasi, India
| | - Parminder Singh
- Department of Cardiology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Manphoul Singhal
- Department of Radiology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Vikash Gupta
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Srikanth Iyengar
- Department of Radiology, Frimley Park Hospital NHS Foundation Trust, Camberley, UK
| | - Sudhir Rathore
- Department of Cardiology, Frimley Park Hospital NHS Foundation Trust, Camberley, UK
- University of Surrey, Guildford, UK
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