Novel monitoring system for quantitative estimation of efficient medical treatment of diseases based on dielectric properties of blood samples
Abstract
A new system for monitoring the effects of radiation and chemotherapy on patients with cancer and some other severe diseases based on the changes in the dielectric characteristics of their blood samples before and after the treatment using a pre-organized system of knowledge on the cancer dynamics, statistical long-term data processing either in the individual or for different cancer types, novel mathematical models and computations on them for interpreting the measurement data is presented. The elaborated system allows accumulating, storing and retrieving data for primary and repeated data processing, the real time decision making on the efficiency/inefficiency of the treatment procedures, and planning future treatment procedures.
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Yu K., Beam A.L., Kohane I.S. Artificial intelligence in healthcare. Nature Biomedical Engineering. 2018. Vol. 2. P. 719–731.
Lynch C.J., Liston C., New machine-learning technologies for computer-aided diagnosis. Nature Medicine. 2018. Vol. 24. P. 1304–1305.
Girardi D., Küng J., Kleiser R., Sonnberger M., Johannes D.C., Holzinger T.A. Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research. Brain Informatics. 2016. Vol.3. P. 133–143.
Kizilova N. Diagnostics of Coronary Stenosis: Analysis of Arterial Blood Pressure and Mathematical Modeling. In: Biomedical Engineering Systems and Technologies. Springer Series on Communications in Computer and Information Science. Plantier, G., Schulz, T., Fred, A., Gamboa, H. (Eds.). 2015. P. 299-312.
Kizilova N. Electromagnetic properties of blood and its interaction with electromagnetic fields”. In: Advances in Medicine and Biology. Vol.137. L.V. Berhardt (ed.) NOVA Sci. Publ. 2019. P. 1-74, 2019.
Batyuk L., Kizilova N. Dielectric properties of red blood cells for cancer diagnostics and treatment. AS Cancer Biology. 2018. Vol. 2(10). P. 55–60.
Batyuk L., Kizilova N., Berest V. Investigation of antiradiation and anticancer efficiency of nanodiamonds on rat erythrocytes. In: Nanomaterials: Application & Properties, 2017. P. 04NB23.
Kingsley P., Taylor E.M. One Health: competing perspectives in an emerging field. Parasitology. 2017. Vol. 144(1). P. 7–14.
Zinnstag J., Schelling E., Waltner-Toews D., Whittaker M., Tanner M. (eds) One Health: the theory and practice of integrated health approaches. CAB International. 2015.
Azuaje F. Artificial intelligence for precision oncology: beyond patient stratification. Nature Precision Oncology. 2019. Vol. 3. P. 6-11, doi:10.1038/s41698-019-0078-1.
Rawat W., Wang Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Computation. 2017. Vol. 29. P. 2352–2449.
Forschner A., Keim U., Hofmann M., Spänkuch I., Lomberg D., Weide B., Tampouri I., Eigentler T., Fink C., Garbe C., Haenssle H.A. Diagnostic accuracy of dermatofluoroscopy in cutaneous melanoma detection: results of a prospective multicentre clinical study in 476 pigmented lesions. British Journal of Dermatology. 2018. Vol.179(2). P. 478-485.
Coudray N., Ocampo P.S., Sakellaropoulos T., Narula N., Snuderl M., Fenyö D., Moreira A.L., Razavian N., Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.Nature Medicine. 2018. Vol. 24. P. 1559–1567.
Yauney G., Shah P. Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. Proceedings of Machine Learning Research. 2018. Vol.85. P. 161–226.
Poplin R., Varadarajan A.V., Blumer K., Liu Y., McConnell M.V., Corrado G.S., Peng L., Webster D.R. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature. Biomedical Engineering. 2018. Vol.2. P.158–164.
Batyuk L., Kizilova N. Modeling of dielectric permittivity of the erythrocytes membrane as a three-layer model. In: Development trends in medical science and practice: the experience of countries of Eastern Europe and prospects of Ukraine, Riga, “Baltija Publishing”. 2018. P. 18–37.
