CardioAssist: A Review of Deep Learning, Machine Learning and Explainable AI Approaches for Integrated Cardiology Decision Support

Author

Sayali Bhanage, Simrat Singh, Aanchal More, Prof. Jaspreet Kaur


Abstract

 Cardiovascular disease is the leading cause of death worldwide, and doctors who see a cardiac patient usually have to do three separate things: read an ECG, weigh a set of clinical risk factors, and check whether any prescribed drug could prolong the QT interval. Each task has its own body of machine-learning research, but very little of it asks how the three might sit in one tool that a clinician can actually use. This paper reviews the literature behind each task: deep learning for 12-lead ECG classification, explainability methods for ECG models, machine-learning risk estimation from clinical parameters, and QTc-based medication safety screening. We then describe how these pieces are planned to fit together in CardioAssist, a doctor-facing web platform that is still under development. The planned design pairs a ResNet50 ECG classifier with Grad-CAM heat maps, a Random Forest risk model built on the UCI Heart Disease data, and a QTc module that combines published clinical rules with a learned component. No performance results for the system are reported here. Instead we point out the gaps that motivate it: single-task tools, thin evidence on image-based ECG analysis, explanations that are rarely checked by clinicians, small tabular datasets that invite optimistic scores, and scarce external validation. We close with a staged evaluation plan.


Keywords

ECG classification, ResNet50, Grad-CAM, explainable AI, Random Forest, cardiovascular risk, QTc, clinical decision support, literature review



Full Text:

Download Paper PDF


References


  1. A. Roth et al., "Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study," J. Am. Coll. Cardiol., vol. 76, no. 25, pp. 2982–3021, 2020.
  2. Prabhakaran, P. Jeemon, and A. Roy, "Cardiovascular diseases in India: Current epidemiology and future directions," Circulation, vol. 133, no. 16, pp. 1605–1620, 2016.
  3. Kawamoto, C. J. Houlihan, E. A. Balas, and D. F. Lobach, "Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success," BMJ, vol. 330, no. 7494, p. 765, 2005.
  4. T. Sutton et al., "An overview of clinical decision support systems: Benefits, risks, and strategies for success," npj Digit. Med., vol. 3, Art. no. 17, 2020.
  5. Hong, Y. Zhou, J. Shang, C. Xiao, and J. Sun, "Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review," Comput. Biol. Med., vol. 122, Art. no. 103801, 2020.
  6. C. Siontis, P. A. Noseworthy, Z. I. Attia, and P. A. Friedman, "Artificial intelligence-enhanced electrocardiography in cardiovascular disease management," Nat. Rev. Cardiol., vol. 18, pp. 465–478, 2021.
  7. Tjoa and C. Guan, "A survey on explainable artificial intelligence (XAI): Toward medical XAI," IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 11, pp. 4793–4813, 2021.
  8. Pan and W. J. Tompkins, "A real-time QRS detection algorithm," IEEE Trans. Biomed. Eng., vol. BME-32, no. 3, pp. 230–236, 1985.
  9. Kiranyaz, T. Ince, and M. Gabbouj, "Real-time patient-specific ECG classification by 1-D convolutional neural networks," IEEE Trans. Biomed. Eng., vol. 63, no. 3, pp. 664–675, 2016.
  10. R. Acharya et al., "A deep convolutional neural network model to classify heartbeats," Comput. Biol. Med., vol. 89, pp. 389–396, 2017.
  11. Y. Hannun et al., "Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network," Nat. Med., vol. 25, pp. 65–69, 2019.
  12. Rajpurkar, A. Y. Hannun, M. Haghpanahi, C. Bourn, and A. Y. Ng, "Cardiologist-level arrhythmia detection with convolutional neural networks," arXiv:1707.01836, 2017.
  13. H. Ribeiro et al., "Automatic diagnosis of the 12-lead ECG using a deep neural network," Nat. Commun., vol. 11, Art. no. 1760, 2020.
  14. I. Attia et al., "An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction," Lancet, vol. 394, no. 10201, pp. 861–867, 2019.
  15. Mincholé and B. Rodriguez, "Artificial intelligence for the electrocardiogram," Nat. Med., vol. 25, pp. 22–23, 2019.
  16. Wagner et al., "PTB-XL, a large publicly available electrocardiography dataset," Sci. Data, vol. 7, Art. no. 154, 2020.
  17. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals," Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
  18. Strodthoff, P. Wagner, T. Schaeffter, and W. Samek, "Deep learning for ECG analysis: Benchmarks and insights from PTB-XL," IEEE J. Biomed. Health Inform., vol. 25, no. 5, pp. 1519–1528, 2021.
  19. Mehari and N. Strodthoff, "Self-supervised representation learning from 12-lead ECG data," Comput. Biol. Med., vol. 141, Art. no. 105114, 2022.
  20. A. Perez Alday et al., "Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020," Physiol. Meas., vol. 41, no. 12, Art. no. 124003, 2020.
  21. Zheng et al., "A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients," Sci. Data, vol. 7, Art. no. 48, 2020.
  22. B. Moody and R. G. Mark, "The impact of the MIT-BIH arrhythmia database," IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45–50, 2001.
  23. H. Khan, M. Hussain, and M. K. Malik, "ECG images dataset of cardiac and COVID-19 patients," Data Brief, vol. 34, Art. no. 106762, 2021.
  24. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, "Heart Disease," UCI Machine Learning Repository, 1988. [Online]. Available: https://archive.ics.uci.edu/dataset/45/heart+disease
  25. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "ImageNet: A large-scale hierarchical image database," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2009, pp. 248–255.
  26. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2014, pp. 3320–3328.
  27. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.
  28. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, "Transfusion: Understanding transfer learning for medical imaging," in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2019, pp. 3347–3357.
  29. Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," Nat. Mach. Intell., vol. 1, pp. 206–215, 2019.
  30. Simonyan, A. Vedaldi, and A. Zisserman, "Deep inside convolutional networks: Visualising image classification models and saliency maps," arXiv:1312.6034, 2013.
  31. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, "Learning deep features for discriminative localization," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 2921–2929.
  32. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual explanations from deep networks via gradient-based localization," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 618–626.
  33. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian, "Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks," in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), 2018, pp. 839–847.
  34. T. Ribeiro, S. Singh, and C. Guestrin, "“Why should I trust you?”: Explaining the predictions of any classifier," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2016, pp. 1135–1144.
  35. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2017, pp. 4765–4774.
  36. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim, "Sanity checks for saliency maps," in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2018, pp. 9505–9515.
  37. B. D’Agostino Sr. et al., "General cardiovascular risk profile for use in primary care: The Framingham Heart Study," Circulation, vol. 117, no. 6, pp. 743–753, 2008.
  38. C. Goff Jr. et al., "2013 ACC/AHA guideline on the assessment of cardiovascular risk," Circulation, vol. 129, no. 25, suppl. 2, pp. S49–S73, 2014.
  39. Detrano et al., "International application of a new probability algorithm for the diagnosis of coronary artery disease," Am. J. Cardiol., vol. 64, no. 5, pp. 304–310, 1989.
  40. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  41. Grinsztajn, E. Oyallon, and G. Varoquaux, "Why do tree-based models still outperform deep learning on typical tabular data?" in Proc. NeurIPS Datasets Benchmarks Track, 2022.
  42. Mohan, C. Thirumalai, and G. Srivastava, "Effective heart disease prediction using hybrid machine learning techniques," IEEE Access, vol. 7, pp. 81542–81554, 2019.
  43. . Ali et al., "A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion," Inf. Fusion, vol. 63, pp. 208–222, 2020.
  44. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, "On calibration of modern neural networks," in Proc. Int. Conf. Mach. Learn. (ICML), PMLR 70, 2017, pp. 1321–1330.
  45. M. Roden, "Drug-induced prolongation of the QT interval," N. Engl. J. Med., vol. 350, no. 10, pp. 1013–1022, 2004.
  46. C. Bazett, "An analysis of the time-relations of electrocardiograms," Heart, vol. 7, pp. 353–370, 1920.
  47. Luo, K. Michler, P. Johnston, and P. W. Macfarlane, "A comparison of commonly used QT correction formulae: The effect of heart rate on the QTc of normal ECGs," J. Electrocardiol., vol. 37, suppl., pp. 81–90, 2004.
  48. S. Fridericia, "Die Systolendauer im Elektrokardiogramm bei normalen Menschen und bei Herzkranken," Acta Med. Scand., vol. 53, pp. 469–486, 1920.
  49. Sagie, M. G. Larson, R. J. Goldberg, J. R. Bengtson, and D. Levy, "An improved method for adjusting the QT interval for heart rate (the Framingham Heart Study)," Am. J. Cardiol., vol. 70, no. 7, pp. 797–801, 1992.
  50. M. Rautaharju et al., "AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: Part IV," Circulation, vol. 119, no. 10, pp. e241–e250, 2009.
  51. G. Priori et al., "2015 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death," Eur. Heart J., vol. 36, no. 41, pp. 2793–2867, 2015.
  52. L. Woosley, C. W. Heise, and K. A. Romero, "QTdrugs list," CredibleMeds, AZCERT. [Online]. Available: https://www.crediblemeds.org
  53. E. Tisdale et al., "Development and validation of a risk score to predict QT interval prolongation in hospitalized patients," Circ. Cardiovasc. Qual. Outcomes, vol. 6, no. 4, pp. 479–487, 2013.
  54. G. Postema and A. A. M. Wilde, "The measurement of the QT interval," Curr. Cardiol. Rev., vol. 10, no. 3, pp. 287–294, 2014.
  55. Paszke et al., "PyTorch: An imperative style, high-performance deep learning library," in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2019, pp. 8024–8035.
  56. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
  57. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," J. Big Data, vol. 6, Art. no. 60, 2019.
  58. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," in Proc. Int. Conf. Learn. Represent. (ICLR), 2015.
  59. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. M. Moons, "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement," Ann. Intern. Med., vol. 162, no. 1, pp. 55–63, 2015.
  60. Liu et al., "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis," Lancet Digit. Health, vol. 1, no. 6, pp. e271–e297, 2019.
  61. J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, and D. King, "Key challenges for delivering clinical impact with artificial intelligence," BMC Med., vol. 17, Art. no. 195, 2019.
  62. J. Topol, "High-performance medicine: The convergence of human and artificial intelligence," Nat. Med., vol. 25, pp. 44–56, 2019.
  63. Ministry of Law and Justice, Government of India, "The Digital Personal Data Protection Act, 2023," Act No. 22 of 2023.
  64. Lewis et al., "Retrieval-augmented generation for knowledge-intensive NLP tasks," in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2020, pp. 9459–9474.

 

 

 

 

 

 

 

 

Share your valuable work from Social Media Buttons