IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS AND NEUROCOMPUTERS IN MEDICINE: FROM FANTASTIC IDEA TO INTELLIGENT SCREENING SYSTEMS

  • Dejan Živanović College of vocational studies for the education of preschool teachers and sport trainers, Subotica; Dept. of biomedical sciences, Banijska 67, 24000 Subotica
  • Jovan Javorac Dept. of biomedical sciences, College of vocational studies for the education of preschool teachers and sport trainers, Subotica, Clinic for granulomatous and interstitial lung diseases, Institute for pulmonary diseases of Vojvodina, Sremska Kamenica
  • Tijana Javorac „ZEGIN“ Pharmacies, Novi Sad
  • Maja Kralj „ZEGIN“ Pharmacies, Novi Sad
Keywords: artificial neural network, neurocomputers, medicine

Abstract

Artificial, informatical neural network is one of the developed forms of implementation of the artificial intelligence systems in various fields of human activity, and therefore in medicine, where it is primarily used in order to minimize the possibility of professional error and improve the qualitative and predictive analysis of complex medical and clinical data. Although welcomed with skepticism, the application of artificial intelligence systems in medicine in a short time led to a significant increase in quality of established health care, and later to the creation of opportunities for implementation of a large number of algorithmic models for early diagnosis and screening of a large number of malignant and chronic non-communicable diseases. The usage of artificially intelligent diagnostic systems in Serbia is not yet sufficiently represented, and the necessity of their rapid implementation in order to modernize the existing practice is indicated by numerous researches confirming the success of their application, especially in the field of preventive medicine, as well as in many clinical and scientific disciplines.

References

Milosavljević M. Veštačka inteligencija. Beograd: Univerzitet „Singidunum”, 2015.

Marinković J, Simić S, Božović Z, Dačić M, Kocev N. Mali rečnik informatike u medicini [monograph on the Internet]. Beograd: Institut za socijalnu medicinu, statistiku i istraživanja u zdravstvu, 1995 [cited 2018 Dec 24]. Available at: http://www.mfub.edu.rs/dotAsset/38234.pdf/.

Dačić M. Biomedicinska naučna informatika. 3. izd. Beograd: Naučna knjiga; 2006.

Marinković J, Babić D, Maksimović R, Stanisavljević D. Elementi računarske podrške u naučnim istraživanjima iz oblasti medicine. Srp Arh Celok Lek 1995; 123 Suppl 2:14-7.

Warren S, McCulloch WP. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5(4):115-33.

Yadav N, Yadav A, Kumar M. History of neural networks. In: An introduction to neural network methods for differential equations. Dordrecht: Springer, 2015: pp. 13-5.

Xhumari E, Manika P. Application of artificial neural networks in medicine. In: RTA-CSIT, 2016: pp. 155-7.

Gavran S. Veštačke neuronske mreže u istraživanju podataka: pregled i primena [master teza]. Beograd: Univerzitet u Beogradu,Matematički fakultet, 2016.

Vilović I, Nađ R, Šipuš Z. Predviđanje rasprostiranja elektromagnetskog polja u bežičnim komunikacijama zatvorenog prostora zasnovano na neuronskim mrežama. Naše more 2008; 55(1):59-68.

Amato F, Lopez A, Pena-Mendez EM, Vanhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed 2013; 11(2):47-58.

Al-Shayea QK. Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues 2011; 8(2):150-4.

Qayyum A, Anwar SM, Awais M, Majid M. Medical image retrieval using deep convolutional neural network. Neurocomputing 2017; 266:8-20.

Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Išgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE transactions on medical imaging 2016; 35(5):1252-61.

Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G. Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 2019; 291(1):196-202.

Beauchet O, Noublanche F, Simon R, Sekhon H, Chabot J, Levinoff EJ, et al. Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis? J Nut Health Aging 2018; 22(1):131-37.

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18:500-10.

Dukic I, Ellison J, Moore M, Collin N, Timoney A, Philip J. Predicting the success of extra-corporal shock wave lithotripsy (ESWL) in ureteric stone disease using pre-operative parameters and an artificial neural network. Eur Urol Suppl 2017; 16(7):e2560.

Zahar Đorđević M. Klasifikacija srčanih oboljenja pomoću neuronskih mreža [doktorska disertacija]. Kragujevac: Univerzitet u Kragujevcu - Fakultet inženjerskih nauka, 2014.

Awang MK, Siraj F. Utilization of an artificial neural network in the prediction of heart disease. International Journal of Bio-Science and Bio-Technology 2013; 5(4):159-65.

Ganesan N, Venkatesh K, Rama MA, Malathi Palani A. Application of neural networks in diagnosing cancer disease using demographic data. Int J Comput Appl 2010;1(26):76-85.

Chan KY, Ling SH, Dillion TS, Nguyen HT. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. Expert Syst Appl 2011; 38(8):9799-808.

Mehdy MM, Ng PY, Shair EF, Md Saleh NI, Gomes C. Artificial neural networks in image processing for early detection of breast cancer. Computational and Mathematical Methods in Medicine, 2017. Available at: https://doi.org/10.1155/2017/2610628

Roffman D, Hart G, Girardi M, Ko CJ, Deng J. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci Rep 2018; 8:1701.

Chowdhury DR, Chatterjee M, Samanta RK. An artificial neural network model for neonatal disease diagnosis. International Journal of Artificial Intelligence and Expert Systems 2011; 2(3):96-106.

Kumaravel N, Sridhar KS, Nithiyanandam N. Automatic diagnosis of heart diseases using neural network. In: Proceedings of the 15th Southern Biomedical Engineering Conference; 1996 Apr 04-06; Washington, USA. New York: IEEE Conference Publications, 1996.

Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017; 69(21):2657-64.

Atkov OY, Gorokhova SG, Sboev AG, Generozov EV, Muraseyeva EV, Moroshkina SY, Cherniy NN. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. J Cardiol 2012; 59(2):190-94.

Malan S. Use of ANN for diagnosis of myocardial infarction. In: Lele R, editor. Computers in medicine – Progres in medical informatics. New Delhi: Tata McGraw-Hill Education, 2005: pp. 255-6.

Heidari E, Sobati MA, Movahedirad S. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometrics and Intelligent Laboratory Systems 2016; 155:73-85.

Kakar A, Sheikh N, Ahmed B, Iqbal S, Rahman A, Kakar SA, et al. Systematic analysis and classification of cardiac rate variability using artificial neural network. International Journal of Advanced Computer Science and Application 2018; 9(11):746-50.

Pathak Y, Laghuvarapu S, Mehta S, Priyakumar U. Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules. ChemRxiv 2019 (Preprint). doi: 10.26434/chemrxiv.10282346.v1

Baskin II, Winkler D, Tetko IV. A renaissance of neural networks in drug discovery. Expert Opin Drug Dis 2016; 11(8):785-95.

Ekins S. The next era: deep learning in pharmaceutical research. Pharm Res 2016; 33(11):2594-603.

Published
2019-12-31
How to Cite
Živanović, D., Javorac, J., Javorac, T., & Kralj, M. (2019). IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS AND NEUROCOMPUTERS IN MEDICINE: FROM FANTASTIC IDEA TO INTELLIGENT SCREENING SYSTEMS. Health Care, 48(4), 43-50. https://doi.org/10.5937/ZZ1904043Z
Section
Review paper