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Classification techniques for non-invasive recognition of liver fibrosis stage

Krawczyk B., Woźniak M., Orczyk T., Porwik P., Musialik J., Błońska-Fajfrowska B.

Journal of Medical Informatics & Technologies

2012

Vol. 20

121--127

Contemporary medicine should provide high quality diagnostic services while at the same time remaining as comfortable as possible for a patient. Therefore novel non-invasive disease recognition methods are becoming one of the key issues in the health services domain. Analysis of data from such examinations opens an interdisciplinary bridge between the medical research and artificial intelligence. The paper presents application of machine learning techniques to biomedical data coming from indirect examination method of the liver fibrosis stage. Presented approach is based on a common set of non-invasive blood test results. The performance of four different compound machine learning algorithms, namely Bagging, Boosting, Random Forest and Random Subspaces, is examined and grid search method is used to find the best setting of their parameters. Extensive experimental investigations, carried out on a dataset collected by authors, show that automatic methods achieve a satisfactory level of the fibrosis level recognition and may be used as a real-time medical decision support system for this task.

Simple and non-invasive liver fibrosis stage prediction method

Orczyk T., Pałys M., Porwik P., Musialik J., Błońska-Fajfrowska B.

Journal of Medical Informatics & Technologies

2011

Vol. 17

227--231

In this paper a simple and non-expensive indirect fibrosis stage prediction method is described. Presented method is non-invasive and is based on the results of the generic blood tests. The method is based on a statistical analysis of wide range of blood tests results supported with the experience of hepatologists.

Quantitative examination of liver tissue ultrasound elastograms

Przytulska M., Gierbliński I., Kulikowski J., Skoczylas K.

Biocybernetics and Biomedical Engineering

2011

Vol. 31, no. 4

75-85

Methods of computer-aided statistical analysis of ultrasound elastograms are presented. An approach consisting in initial segmentation of elastograms visualizing low-elasticity segments distribuition in the tissue of an examined biological organ and in statistical analysis of this distribution is described. Satisfactory correlation between the values of same statistics and medical specialists' description of human liver elastograms was observed. The ways of continuation of the works aimed at improvement of the elastograms-based diagnostic methods are suggested.