ISSN 1214-0287 (on-line), ISSN 1214-021X (printed)
J Appl Biomed
Volume 11 (2013), No 2, p 47-58
DOI 10.2478/v10136-012-0031-x

Artificial neural networks in medical diagnosis

Filippo Amato, Alberto Lopez, Eladia Maria Pena-Mendez, Petr Vanhara, Ales Hampl, Josef Havel

Address: Josef Havel, Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00 Brno, Czech Republic
havel@chemi.muni.cz

Received 17th December 2012.
Published online 7th January 2013.

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SUMMARY
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples.

KEY WORDS
medical diagnosis; artificial intelligence; artificial neural networks; cancer; cardiovascular diseases; diabetes

REFERENCES
Ahmed F. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer. 4: 29, 2005.
[CrossRef] [PubMed]

Aleksander I, Morton H. An introduction to neural computing. Int Thomson Comput Press, London 1995.
[PubMed]

Alkim E, Gurbuz E, Kilic E. A fast and adaptive automated disease diagnosis method with an innovative neural network model. Neur Networks. 33: 88-96, 2012.
[CrossRef] [PubMed]

Amato F, Gonzalez-Hernandez J, Havel J. Artificial neural networks combined with experimental design: a "soft" approach for chemical kinetics. Talanta. 93: 72-78, 2012.
[CrossRef] [PubMed]

Arnold M. Non-invasive glucose monitoring. Curr Opin Biotech. 7: 46-49, 1996.
[CrossRef]

Atkov O, Gorokhova S, Sboev A, Generozov E, Muraseyeva E, Moroshkina S and Cherniy N. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. J Cardiol. 59: 190-194, 2012.
[CrossRef] [PubMed]

Barbosa D, Roupar D, Ramos J, Tavares A and Lima C. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. Biomed Eng Online. 11: 3, 2012.
[CrossRef] [PubMed]

Bartosch-Harlid A, Andersson B, Aho U, Nilsson J, Andersson R. Artificial neural networks in pancreatic disease. Br J Surg. 95: 817-826, 2008.
[CrossRef] [PubMed]

Barwad A, Dey P, Susheilia S. Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology. Cytometry B Clyn Cytom. 82: 107-111, 2012.
[CrossRef] [PubMed]

Basheer I, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth. 43: 3-31, 2000.
[CrossRef]

Bradley B. Finding biomarkers is getting easier. Ecotoxicology. 21: 631-636, 2012.
[CrossRef] [PubMed]

Brougham D, Ivanova G, Gottschalk M, Collins D, Eustace A, O'Connor R, Havel J. Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. J Biomed Biotechnol. 2011: 158094, 2011.
[CrossRef]

Catalogna M, Cohen E, Fishman S, Halpern Z, Nevo U, Ben-Jacob E. Artificial neural networks based controller for glucose monitoring during clamp test. PloS One. 7: e44587, 2012.
[CrossRef] [PubMed]

Chan K, Ling S, Dillon T, Nguyen H. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. Expert Syst Appl. 38: 9799-9808, 2011.
[CrossRef]

Dayhoff J, Deleo J. Artificial Neural Networks: Opening the Black Box. Cancer. 91: 1615-1635, 2001.
[CrossRef]

Dazzi D, Taddei F, Gavarini A, Uggeri E, Negro R, Pezzarossa A. The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method. J Diabet Complicat. 15: 80-87, 2001.
[CrossRef]

de Bruijn M, ten Bosch L, Kuik D, Langendijk J, Leemans C, Verdonck-de Leeuw I. Artificial neural network analysis to assess hypernasality in patients treated for oral or oropharyngeal cancer. Logoped Phoniatr Vocol. 36: 168-174, 2011.
[PubMed]

Dey P, Lamba A, Kumari S, Marwaha N. Application of an artificial neural network in the prognosis of chronic myeloid leukemia. Anal Quant Cytol Histol. 33: 335-339, 2012.
[PubMed]

El-Deredy W, Ashmore S, Branston N, Darling J, Williams S, Thomas D. Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks Cancer Res. 57: 4196-4199, 1997.
[PubMed]

Elveren E, Yumusak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J Med Syst. 35: 329-332, 2011.
[CrossRef] [PubMed]

Er O, Temurtas F, Tanrikulu A. Tuberculosis Disease Diagnosis Using Artificial Neur Networks. J Med Syst. 34: 299-302, 2008.
[PubMed]

Fedor P, Malenovsky I, Vanhara J, Sierka W, Havel J. Thrips (Thysanoptera) identification using artificial neural networks. Bull Entomol Res. 98: 437-447, 2008.
[CrossRef] [PubMed]

Fernandez de Canete J, Gonzalez-Perez S, Ramos-Diaz JC. Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. Comput Meth Progr Biomed. 106: 55-66, 2012.
[CrossRef] [PubMed]

Fernandez-Blanco E, Rivero D, Rabunal J, Dorado J, Pazos A, Munteanu C. Automatic seizure detection based on star graph topological indices. J Neurosci Methods. 209: 410-419, 2012.
[CrossRef] [PubMed]

Gannous AS, Elhaddad YR. Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques. WASET. 50: 124-128, 2011.

Havel J, Pena E, Rojas-Hernandez A, Doucet J, Panaye A. Neural networks for optimization of high-performance capillary zone electrophoresis methods. J Chromatogr A. 793: 317-329, 1998.
[CrossRef]

Ho W-H, Lee K-T, Chen H-Y, Ho T-W, Chiu H-C. Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network. PloS One. 7: e29179, 2012.
[CrossRef] [PubMed]

Karabulut E, Ibrikci T. Effective diagnosis of coronary artery disease using the rotation forest ensemble method. J Med Syst. 36: 3011-3018, 2012.
[CrossRef] [PubMed]

Kheirelseid E, Miller N, Chang K, Curran C, Hennessey E, Sheehan M, Newell J, Lemetre C, Balls G, Kerin M. miRNA expressions in rectal cancer as predictors of response to neoadjuvant chemoradiation therapy. Int J Colorectal Dis. 2012.
[CrossRef]

Leon BS, Alanis AY, Sanchez E, Ornelas-Tellez F, Ruiz-Velazquez E. Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients. J Franklin I. 349: 1851-1870, 2012.
[CrossRef]

Li Y, Rauth AM, Wu XY. Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks. Eur J Pharm Sci. 24: 401-410, 2005.
[CrossRef] [PubMed]

Mazurowski M, Habas P, Zurada J, Lo J, Baker J, Tourassi G. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural networks. 21: 427-436, 2008.
[CrossRef] [PubMed]

Michalkova V, Valigurova A, Dindo M, Vanhara J. Larval morphology and anatomy of the parasitoid Exorista larvarum (Diptera: Tachinidae), with an emphasis on cephalopharyngeal skeleton and digestive tract. J Parasitol. 95: 544-554, 2009.
[CrossRef] [PubMed]

Molga E, van Woezik B, Westerterp K. Neural networks for modelling of chemical reaction systems with complex kinetics: oxidation of 2-octanol with nitric acid. Chem Eng Process. 39: 323-334, 2000.
[CrossRef]

Mortazavi D, Kouzani A, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 54: 299-320, 2012a.
[CrossRef] [PubMed]

Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 54: 299-320, 2012b.
[CrossRef] [PubMed]

Murarikova N, Vanhara J, Tothova A, Havel J. Polyphasic approach applying artificial neural networks, molecular analysis and postabdomen morphology to West Palaearctic Tachina spp. (Diptera, Tachinidae). Bull Entomol Res. 101: 165-175, 2010.
[CrossRef] [PubMed]

Narasingarao M, Manda R, Sridhar G, Madhu K, Rao A. A clinical decision support system using multilayer perceptron neural network to assess well being in diabetes. J Assoc Physicians India. 57: 127-133, 2009.
[PubMed]

Ozbay Y. A new approach to detection of ECG arrhythmias: Complex discrete wavelet transform based complex valued artificial neural network. J Med Syst. 33: 435-445, 2009.
[CrossRef] [PubMed]

Pace F, Savarino V. The use of artificial neural network in gastroenterology: the experience of the first 10 years. Eur J Gastroenterol Hepatol. 19: 1043-1045, 2007.
[CrossRef] [PubMed]

Rodriguez Galdon B, Penna-Mendez E, Havel J, Rodriguez Rodriguez E, Diaz Romero C. Cluster Analysis and Artificial Neural Networks Multivariate Classification of Onion Varieties. J Agric Food Chem: 11435-11440, 2010.
[CrossRef]

Saghiri M, Asgar K, Boukani K, Lotfi M, Aghili H, Delvarani A, Karamifar K, Saghiri A, Mehrvarzfar P, Garcia-Godoy F. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 45: 257-265, 2012.
[CrossRef] [PubMed]

Shankaracharya, Odedra D, Samanta S, Vidyarthi A. Computational intelligence in early diabetes diagnosis: a review. Rev Diabet Stud. 7: 252-262, 2010.
[CrossRef] [PubMed]

Siristatidis C, Chrelias C, Pouliakis A, Katsimanis E, Kassanos D. Artificial neural networks in gyneacological diseases: Current and potential future applications. Med Sci Monit. 16: 231-236, 2010.
[PubMed]

Spelt L, Andersson B, Nilsson J, Andersson R. Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review. Eur J Surg Oncol. 38: 16-24, 2012.
[CrossRef] [PubMed]

Strike P, Michaeloudis A, Green AJ. Standardizing clinical laboratory data for the development of transferable computer-based diagnostic programs. Clin Chem. 32: 22-29, 1986.
[PubMed]

Szolovits P, Patil RS, Schwartz W. Artificial Intelligence in Medical Diagnosis. Ann Intern Med. 108: 80-87, 1988.
[PubMed]

Tate A, Underwood J, Acosta D, Julia-Sape M, Majos C, Moreno-Torres A, Howe F, van der Graaf M, Lefournier V, Murphy M, Loosemore A, Ladroue C et al. Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR Biomed. 19: 411-434, 2006.
[CrossRef] [PubMed]

Thakur A, Mishra V, Jain S. Feed forward artificial neural network: tool for early detection of ovarian cancer. Sci Pharm. 79: 493-505, 2011.
[CrossRef] [PubMed]

Trajanoski Z, Regittnig W, Wach P. Simulation studies on neural predictive control of glucose using the subcutaneous route. Comput Meth Progr Biomed. 56: 133-139, 1998.
[CrossRef]

Uguz H. A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. J Med Syst. 36: 61-72, 2012.
[CrossRef] [PubMed]

Verikas A, Bacauskiene M. Feature selection with neural networks. Pattern Recogn Lett. 23: 1323-1335, 2002.
[CrossRef]

Wilding P, Morgan M, Grygotis A, Shoffner M, Rosato E. Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. Cancer Lett. 77: 145-153, 1994.
[CrossRef]

Yan H, Zheng J, Jiang Y, Peng C, Xiao S. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. Appl Soft Comput. 8: 1105-1111, 2008.
[CrossRef]

Zupan J, Gasteiger J. Neural networks in chemistry and drug design. Wiley VCH, Weinheim, 380 p. 1999.
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