Abstract
Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy.Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS), to determine a hypothetical methodology for texture classification of closely-related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e. 1.5-tesla (1.5T), 3-tesla (3T)), rotational planes (i.e. coronal, sagittal and axial), and signal weighting (i.e. spin-lattice, spin-spin, relaxation, proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, grey matter, and white matter. Various textures analysis methods were tested. The KBS performed automatic data pre-processing and semi-automatic classification of ROIs.Results: After testing 3456 ROIs, statistical binary classification revealed that combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79-100), specificity (both 100%, 95% CI: 99.79-100) and Fisher coefficient (≈E+4, ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.
Highlights
Medicine is not an exact science but an applied, interdisciplinary field1
linear discriminant algorithms (LDA) and nonlinear discriminant algorithms (NDA) in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test
That was why we proposed the approach to have the observer as the direct trainer of a knowledge-based systems (KBS) designed as a customizable, conceptual framework
Summary
Medicine is not an exact science but an applied, interdisciplinary field. the time to produce physicians specialized in radiology is very long. Physicians continue to expand their acquired knowledge with years of evidence-based practice. A framework is a system which implements the process of abstraction ― i.e. a technique where developers make computer modeling/programming simpler to understand, use and apply. Such a KBS should not be designed as “a hammer to drive a nail” but as an abstraction system with generic functionality ― which can be changed with user-written codes and customized for unlimited applications. Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy
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