Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis.
OBJECTIVE
To investigate the role of artificial 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 artificial 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 specificity 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 specificity 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 significant compared to T&T-IS.


CONCLUSIONS
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.
Lahner
Edith
E
Department of Digestive and Liver Disease, University La Sapienza, Ospedale Sant'Andrea, Via di Grottarossa 1035, Roma 00189, Italy.
Intraligi
Marco
M
Buscema
Massimo
M
Centanni
Marco
M
Vannella
Lucy
L
Grossi
Enzo
E
Annibale
Bruno
B
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005757:Gastritis, Atrophic / Q000473:pathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D013959:Thyroid Diseases / Q000473:pathology*