Selected Publications

  1. Sau, Arunashis et al. Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study. The Lancet Digital Health, Volume 6, Issue 11, e791 - e802. https://doi.org/10.1016/S2589-7500(24)00172-9.
  2. Pastika, L., Sau, A., Patlatzoglou, K. et al. Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease. npj Digit. Med. 7, 167 (2024). https://doi.org/10.1038/s41746-024-01170-0.
  3. Sau A, Ng FS. The emerging role of artificial intelligence enabled electrocardiograms in healthcare. BMJ Med. 2023 Jul 31;2(1):e000193. doi: 10.1136/bmjmed-2022-000193.
  4. Sau A, Ahmed A, Chen JY, Pastika L, Wright I, Li X, Handa B, Qureshi N, Koa-Wing M, Keene D, Malcolme-Lawes L, Varnava A, Linton NWF, Lim PB, Lefroy D, Kanagaratnam P, Peters NS, Whinnett Z, Ng FS, Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients, Eur Heart J Digit Health. 2023, ztad064, doi: 10.1093/ehjdh/ztad064.
  5. Sau A, Ibrahim S, Kramer DB, Waks JW, Qureshi N, Koa-Wing M, Keene D, Malcolme-Lawes L, Lefroy DC, Linton NWF, Lim PB, Varnava A, Whinnett ZI, Kanagaratnam P, Mandic D, Peters NS, Ng FS. Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia. Cardiovasc Digit Health J. 2023 Jan 31;4(2):60-67. doi: 10.1016/j.cvdhj.2023.01.004.
  6. Sau A, Ibrahim S, Ahmed A, Handa B, Kramer DB, Waks JW, Arnold AD, Howard JP, Qureshi N, Koa-Wing M, Keene D, Malcolme-Lawes L, Lefroy DC, Linton NWF, Lim PB, Varnava A, Whinnett ZI, Kanagaratnam P, Mandic D, Peters NS, Ng FS. Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms. Eur Heart J Digit Health. 2022 Aug 17;3(3):405-414. doi: 10.1093/ehjdh/ztac042.
  7. Sivanandarajah P, Wu H, Bajaj N, Khan S, Ng FS. Is machine learning the future for atrial fibrillation screening? Cardiovasc Digit Health J. 2022 May 16;3(3):136-145. doi: 10.1016/j.cvdhj.2022.04.001.
  8. Li X, Shi X, Handa BS, Sau A, Zhang B, Qureshi NA, Whinnett ZI, Linton NWF, Lim PB, Kanagaratnam P, Peters NS, Ng FS. Classification of Fibrillation Organisation Using Electrocardiograms to Guide Mechanism-Directed Treatments. Front Physiol. 2021 Nov 11;12:712454. doi: 10.3389/fphys.2021.712454.
  9. Shen CP, Freed BC, Walter DP, Perry JC, Barakat AF, Elashery ARA, Shah KS, Kutty S, McGillion M, Ng FS, Khedraki R, Nayak KR, Rogers JD, Bhavnani SP. Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator. J Am Heart Assoc. 2023 Apr 18;12(8):e026974. doi: 10.1161/JAHA.122.026974.
  10. Wu H, Patel KHK, Li X, Zhang B, Galazis C, Bajaj N, Sau A, Shi X, Sun L, Tao Y, Al-Qaysi H, Tarusan L, Yasmin N, Grewal N, Kapoor G, Waks JW, Kramer DB, Peters NS, Ng FS. A fully-automated paper ECG digitisation algorithm using deep learning. Sci Rep. 2022 Dec 5;12(1):20963. doi: 10.1038/s41598-022-25284-1.
  11. Li X, Patel KHK, Sun L, Peters NS, Ng FS. Neural networks applied to 12-lead electrocardiograms predict body mass index, visceral adiposity and concurrent cardiometabolic ill-health. Cardiovasc Digit Health J. 2021 Dec;2(6 Suppl):S1-S10. doi: 10.1016/j.cvdhj.2021.10.003.
  12. Sau A., Pastika L., Sieliwonczyk, E., Patlatzoglou K., Ribeiro A. H., McGurk K. A., Zeidaabadi B., Zhang H., Macierzanka K., Mandic D., Sabino E., Giatti L., Barreto S. M., do Valle Camel, L., Tzoulaki I., O'Regan D. P., Peters N. S., Ware J. S., Ribeiro A. L., … Ng FS. (2024a). Artificial Intelligence–Enabled Electrocardiogram for Mortality and Cardiovascular Risk Estimation: An Actionable, Explainable and Biologically Plausible Platform. https://doi.org/10.1101/2024.01.13.24301267.
  13. Sau A., Ribeiro, A. H., McGurk K. A., Pastika L., Bajaj N., Ardissino M., Chen J. Y., Wu H., Shi X., Hnatkova, K., Zheng, S., Britton, A., Shipley, M., Andršová, I., Novotný, T., Sabino, E., Giatti, L. Barreto, S. M., Waks J. W., … Ng FS. (2023a). Neural Network-Derived Electrocardiographic Features Have Prognostic Significance and Important Phenotypic and Genotypic Associations. https://doi.org/10.1101/2023.06.15.23291428.