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| 1 | Artifi cial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis显示文摘AIM: To investigate the role of artifi cial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artifi cial neural networks (ANNs) using a data optimisation procedure (standard ANNs,T&T-IS protocol,TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specifi city of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy,sensitivity and specifi city of 74.7% and 75.8%,78.8% and 81.8%,and 70.5% and 69.9%,respectively. The increase of sensitivity of the TWIST protocol was statistically signifi cant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status. | Edith Lahner Marco Intraligi Massimo Buscema Marco Centanni Lucy Vannella Enzo Grossi Bruno Annibale | 2008 | World Journal of Gastroenterology2008，14，4： | 6 |
| 2 | Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis显示文摘AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.METHODS: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG.Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3:input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5:use of only serological variables.RESULTS: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%,respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were,respectively, 97.7% and 94.5%.CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA,may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders. | Edith Lahner Enzo Grossi Marco Intraligi Massimo Buscema Vito D Corleto Gianfranco Delle Fave Bruno Annibale | 2005 | World Journal of Gastroenterology2005，11，37： | 4 |
| 3 | Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machinelearning workflow显示文摘An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery.The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací,Minas Gerais,Brazil.The proposed workflow includes subset feature selection,learning,and estimation algorithms.Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil(e.g.0.5 exceedance for pixels:75%clay-rich Nitisols,15%iron-rich Latosols,and 1%quartz-rich Arenosols)and vegetation(e.g.0.5 exceedance for pixels:4%Aspen-like trees,7%Blackberry-like trees,0%live grass,and 2%dead grass).The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages,and correctly classifies the clay-rich Nitisols and grasses on the intervening hills.These classifications are independently corroborated visually(Google Earth)and quantitatively(random soil samples and crossplots of field spectra).Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist,and the under representation of classified grass in some dry areas of the Hyperion image.These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints. | Michael J.Friedel Massimo Buscema Luiz Eduardo Vicente Fabio Iwashita Andréa Koga-Vicente | 2018 | International Journal of Digital Earth2018，11，7： | 0 |
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