Predicting Heart Failure. Группа авторов

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      21 21 Al Maadeed, S., Kunhoth, S., Bouridane, A., and Peyret, R. (2017). Multispectral imaging and machine learning for automated cancer diagnosis. 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017. https://doi.org/10.1109/IWCMC.2017.7986547.

      22 22 Masetic, Z.and Subasi, A. (2016). Congestive heart failure detection using random forest classifier. Computer Methods and Programs in Biomedicine 130. https://doi.org/10.1016/j.cmpb.2016.03.020.

      23 23 Molinari, F., Meiburger, K.M., Saba, L., Rajendra Acharya, U., Ledda, M., Nicolaides, A., and Suri, J.S. (2012). Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. Ultrasonics 52 (7). https://doi.org/10.1016/j.ultras.2012.03.005.

      24 24 Nagaraj, Y., Teja, A.H.S., and Narasimhadhan, A.V. (2019). Automatic segmentation of intima media complex in carotid ultrasound images using support vector machine. Arabian Journal for Science and Engineering 44 (4). https://doi.org/10.1007/s13369-018-3549-8.

      25 25 Biswas, M., Saba, L., Chakrabartty, S., Khanna, N.N., Song, H., Suri, H.S., Sfikakis, P.P., Mavrogeni, S., Viskovic, K., Laird, J.R., Cuadrado-Godia, E., Nicolaides, A., Sharma, A., Viswanathan, V., Protogerou, A., Kitas, G., Pareek, G., Miner, M., and Suri, J.S. (2020). Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment. Computers in Biology and Medicine 123. https://doi.org/10.1016/j.compbiomed.2020.103847.

      26 26 Biswas, M., Kuppili, V., Araki, T., Edla, D.R., Godia, E.C., Saba, L., Suri, H.S., Omerzu, T., Laird, J.R., Khanna, N.N., Nicolaides, A., and Suri, J.S. (2018). Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort. Computers in Biology and Medicine 98. https://doi.org/10.1016/j.compbiomed.2018.05.014.

      27 27 Menchón-Lara, R.M., Sancho-Gómez, J.L., and Bueno-Crespo, A. (2016). Early-stage atherosclerosis detection using deep learning over carotid ultrasound images. Applied Soft Computing Journal 49. https://doi.org/10.1016/j.asoc.2016.08.055.

      28 28 Li, D., Zhang, J., Zhang, Q., and Wei, X. (2017). Classification of ECG signals based on 1D convolution neural network. 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services, Healthcom 2017, 2017-December. https://doi.org/10.1109/HealthCom.2017.8210784.

      29 29 Rajput, J.S., Sharma, M., Tan, R.S., and Acharya, U.R. (2020). Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank. Computers in Biology and Medicine 123. https://doi.org/10.1016/j.compbiomed.2020.103924.

      30 30 Eltrass, A.S., Tayel, M.B., and Ammar, A.I. (2021). A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomedical Signal Processing and Control 65. https://doi.org/10.1016/j.bspc.2020.102326.

      31 31 Moridani, M.K., Abdi Zadeh, M., and Shahiazar Mazraeh, Z. (2019). An efficient automated algorithm for distinguishing normal and abnormal ECG signal. IRBM 40 (6). https://doi.org/10.1016/j.irbm.2019.09.002.

      32 32 Van Den Oever, L.B., Cornelissen, L., Vonder, M., Xia, C., Van Bolhuis, J.N., Vliegenthart, R., Veldhuis, R.N.J., De Bock, G.H., Oudkerk, M., and Van Ooijen, P.M.A. (2020). Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium. European Journal of Radiology 129. https://doi.org/10.1016/j.ejrad.2020.109114.

      33 33 Van Assen, M., Martin, S.S., Varga-Szemes, A., Rapaka, S., Cimen, S., Sharma, P., Sahbaee, P., De Cecco, C.N., Vliegenthart, R., Leonard, T.J., Burt, J.R., and Schoepf, U.J. (2021). Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study. European Journal of Radiology 134. https://doi.org/10.1016/j.ejrad.2020.109428.

      34 34 Zhang, N., Yang, G., Zhang, W., Wang, W., Zhou, Z., Zhang, H., Xu, L., and Chen, Y. (2021). Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. European Journal of Radiology 134. https://doi.org/10.1016/j.ejrad.2020.109420.

      35 35 Dekker, M., Waissi, F., Bank, I.E.M., Isgum, I., Scholtens, A.M., Velthuis, B.K., Pasterkamp, G., De Winter, R.J., Mosterd, A., Timmers, L., and De Kleijn, D.P.V. (2021). The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from 82Rb-PET/CT myocardial perfusion imaging. International Journal of Cardiology 329. https://doi.org/10.1016/j.ijcard.2020.12.079.

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