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<p>COVID-19 has been shown to have long-lasting effects on lung function, resulting in dysfunctional upper and lower respiratory tracts. Thus, they are causing abnormal oxygen levels in the body that leads to diseases which cause an increase in the heart rate and long-term heart weakness. Such effects may lead to multiorgan failures. There are extensive studies on the impact of lung disease on heart function. Numerous models have been developed to estimate cardiac damage caused by lung dysfunction. These findings can be extended to COVID-19 patients, allowing physicians to take preventative measures to avoid future cardiac complications early. A transfer learning model that evaluates learning from various currently available deep learning architectures that link lung diseases to cardiac anomalies. The present study creates a model for estimating future cardiac disorders associated with the progression of lung conditions in COVID-19 patients. The model is cross-validated on over 85 patients by cross-referencing their cardiac reports post-COVID. Observations indicate that the proposed model is 97% accurate in estimating cardiac anomalies and can be used for real-time clinical purposes. In addition, a scalable model that can estimate the effects of lung and heart anomalies on other organs, such as the brain, kidneys, intestine, etc., is proposed. This will aid physicians in obtaining a rough estimate of the affected organs, facilitating a more accurate clinical diagnosis.</p>

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Estimation of COVID-19 secondary effects on cardiac anomalies using deep transfer learning approach

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • Narendra Kumar Rout,
  • S Gopal Krishna Patro,
  • Nirjharinee Parida,
  • Ayodeji Olalekan Salau,
  • Debabrata Dansana,
  • Ganapati Panda

摘要

COVID-19 has been shown to have long-lasting effects on lung function, resulting in dysfunctional upper and lower respiratory tracts. Thus, they are causing abnormal oxygen levels in the body that leads to diseases which cause an increase in the heart rate and long-term heart weakness. Such effects may lead to multiorgan failures. There are extensive studies on the impact of lung disease on heart function. Numerous models have been developed to estimate cardiac damage caused by lung dysfunction. These findings can be extended to COVID-19 patients, allowing physicians to take preventative measures to avoid future cardiac complications early. A transfer learning model that evaluates learning from various currently available deep learning architectures that link lung diseases to cardiac anomalies. The present study creates a model for estimating future cardiac disorders associated with the progression of lung conditions in COVID-19 patients. The model is cross-validated on over 85 patients by cross-referencing their cardiac reports post-COVID. Observations indicate that the proposed model is 97% accurate in estimating cardiac anomalies and can be used for real-time clinical purposes. In addition, a scalable model that can estimate the effects of lung and heart anomalies on other organs, such as the brain, kidneys, intestine, etc., is proposed. This will aid physicians in obtaining a rough estimate of the affected organs, facilitating a more accurate clinical diagnosis.