A fuzzy system for helping medical diagnosis of malformations of cortical development

Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.

[1]  Yeung Yam,et al.  Reduction of fuzzy rule base via singular value decomposition , 1999, IEEE Trans. Fuzzy Syst..

[2]  M. J. Lee,et al.  Location of the primary motor cortex in schizencephaly. , 1999, AJNR. American journal of neuroradiology.

[3]  Hong Yan,et al.  Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition , 1996, Advances in Fuzzy Systems - Applications and Theory.

[4]  André Palmini,et al.  Classification issues in malformations caused by abnormalities of cortical development. , 2002, Neurosurgery clinics of North America.

[5]  Madan M. Gupta,et al.  Approximate reasoning in decision analysis , 1982 .

[6]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[7]  Y. Wu,et al.  Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. , 1993, Radiology.

[8]  Lakhmi C. Jain,et al.  Recent advances in intelligent paradigms and applications , 2003 .

[9]  W. Baxt.  Application of artificial neural networks to clinical medicine , 1995, The Lancet.

[10]  Silvia Alayón Miranda.  Diseño de sistemas borrosos recurrentes mediante estrategias evolutivas y su aplicación al análisis de señales y reconocimiento de patrones , 2003 .

[11]  John A. Swets,et al.  Evaluation of diagnostic systems : methods from signal detection theory , 1982 .

[12]  A J Barkovich,et al.  Syndromes of bilateral symmetrical polymicrogyria. , 1999, AJNR. American journal of neuroradiology.

[13]  Jerald E. Maiers.  Fuzzy Set Theory and Medicine: The First Twenty Years and Beyond. , 1985 .

[14]  P Evrard,et al.  A classification scheme for malformations of cortical development. , 1996, Neuropediatrics.

[15]  William B. Dobyns,et al.  Radiologic classification of malformations of cortical development , 2001, Current opinion in neurology.

[16]  O Salonen,et al.  Electromagnetic function of polymicrogyric cortex in congenital bilateral perisylvian syndrome , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

[17]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[18]  J. Pruvo,et al.  Cerebellar cortical dysplasia: MR findings in a complex entity. , 2000, AJNR. American journal of neuroradiology.

[19]  E A Robertson,et al.  Use of receiver operating characteristic curves to evaluate the clinical performance of analytical systems. , 1981, Clinical chemistry.

[20]  Lorenzo Moreno Ruiz,et al.  Cytological image analysis with a genetic fuzzy finite state machine , 2005, Comput. Methods Programs Biomed..

[21]  F. Gomide,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[22]  James F. Martin.  Fuzzy control in anesthesia , 2008, Journal of Clinical Monitoring.

[23]  Friedrich Steimann,et al.  On the use and usefulness of fuzzy sets in medical AI , 2001, Artif. Intell. Medicine.

[24]  Friedrich G Woermann,et al.  Functional organization of the brain with malformations of cortical development , 2003, Annals of neurology.

[25]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[26]  P Ellen Grant,et al.  Bilateral frontoparietal polymicrogyria: Clinical and radiological features in 10 families with linkage to chromosome 16 , 2003, Annals of neurology.

[27]  W. Dobyns,et al.  X-linked malformations of cortical development. , 2000, American journal of medical genetics.

[28]  J. Kacprzyk,et al.  An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis , 2003 .

[29]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[30]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .