Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study.

Journal: Angiology
Published Date: May 1, 2025

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; < .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.

Authors

  • Ashish Gautam
    KardioLabs AI, Jacksonville, FL, USA.
  • Prashant Raghav
    KardioLabs AI, Jacksonville, FL, USA.
  • Vijay Subramaniam
    University of Waterloo, Waterloo, ON, Canada.
  • Sunil Kumar
    School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 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
    Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Parminder Singh
    Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India.
  • Manphoul Singhal
    Department of Radiology, Post Graduate Institute of Medical Education and Research, Chandigarh, India.
  • Vikash Gupta
    Laboratory for Augmented Intelligence in Imaging of the Department of Radiology, The Ohio State University College of Medicine, United States.
  • Samir Rathore
    KardioLabs AI, 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.

Keywords

External Resources

Popular Topics
Recent Journals