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Application of CT coronary flow reserve fraction based on deep learning in coronary artery diagnosis of coronary heart disease complicated with diabetes mellitus

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Abstract

Coronary heart disease is a heart disease caused by coronary atherosclerosis, which seriously endangers human life and health. More and more studies have shown that diabetes is one of the main pathogenic factors of coronary heart disease and has an important relationship with coronary heart disease. At present, the mainstream treatment of coronary heart disease complicated with diabetes mellitus is the use of coronary angiography, which is the gold standard of treatment at present. However, it still has a certain risk, and most of the postoperative complications, the improvement method is to use FFR evaluation standard. At present, there are few special researches on this aspect. Therefore, this paper proposes the application research of FFR based on deep learning in the diagnosis of coronary heart disease complicated with diabetes mellitus. Through the core theoretical research on coronary heart disease complicated with diabetes mellitus and FFR, this paper analyzes that the existing coronary angiography is still in the development stage, which has a positive effect on the treatment of coronary heart disease and diabetes mellitus. On this basis, combined with FFR can play a better therapeutic effect. The second part is the establishment method of the related comparative experiment. This experiment adopts real coronary heart disease complicated with diabetes cases, through the way of random grouping, each group of 41 people, one group was FFRCT group, the other was FFRQCA group, and in order to ensure the experimental effect, a unified evaluation index is established. In the third part, the comparative analysis of the experimental methods of angina pectoris was carried out. Through the analysis of experimental data, it is shown that the safety, postoperative complications control and comprehensive treatment effect of this method are significantly improved compared with the traditional methods.

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Acknowledgements

This work was supported by People’s Livelihood Science and Technology Project of Qingdao (No.19-6-1-26-nsh); Key Fund of Department of Cardiology, Shandong University Qilu Hospital (Qingdao) (QDKY2019ZD04), 2022 People’s Livelihood Science and Technology Project of Qingdao (Application of DEEPVESSEL FFR in coronary artery heart disease complicated with diabetes mellitus), Qingdao Key Health Discipline Developent Fund.

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Author notes

  1. Zhaoping Wang and Hongji Yin co-first author. Suhua Zhang and Yingcui Wang Co-corresponding author.

Authors and Affiliations

  1. Department of Emergency, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, 266035, China

    Zhaoping Wang & Hongji Yin

  2. Internal Medicine Department, Qingdao Municipal Hospital (Group), Qingdao No. 9 People’s Hospital, Qingdao, 266035, China

    Wei Jing

  3. Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, 266035, China

    Hui Sun, Ming Ru & Yingcui Wang

  4. Department of Geriatrics, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, 266035, China

    Suhua Zhang

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  1. Zhaoping Wang
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  2. Hongji Yin
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  3. Wei Jing
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  4. Hui Sun
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  6. Suhua Zhang
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Correspondence to Suhua Zhang or Yingcui Wang.

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Wang, Z., Yin, H., Jing, W. et al. Application of CT coronary flow reserve fraction based on deep learning in coronary artery diagnosis of coronary heart disease complicated with diabetes mellitus. Neural Comput & Applic 34, 6763–6772 (2022). https://doi.org/10.1007/s00521-021-06070-y

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