Computerized Diagnosis of Helicobacter pylori Infection and Associated Gastric Inflammation from Endoscopic Images by Refined Feature Selection Using a Neural Network

Background and Study Aim: We investigated whether analysis of endoscopic images using a refined feature selection with neural network (RFSNN) technique could predict Helicobacter pylori-related gastric histological features.
Patients and Methods: A total of 104 dyspeptic patients were prospectively enrolled for panendoscopy and gastric biopsy for histological evaluation using the updated Sydney system. The endoscopic images of each patient were analyzed to obtain 84 image parameters. The significant image parameters from 30 randomly selected patients (15 with and 15 without H. pylori infection) associated with histological features were used to develop the RFSNN model. This was then used to test the sensitivity and specificity of the image parameters obtained from the remaining 74 patients for the prediction of the presence of H. pylori infection and related histological features.
Results: The RFSNN technique had a sensitivity of 85.4 % and a specificity of 90.9 % for the detection of H. pylori infection. Moreover, RFSNN was highly accurate (> 80 %) in predicting the presence of gastric atrophy, intestinal metaplasia and the severity of H. pylori-related gastric inflammation.
Conclusions: RFSNN is an effective computerized technique for assessing the presence of H. pylori infection and related gastric inflammation and precancerous lesions. By using RFSNN to analyze endoscopic images, a comprehensive evaluation of the stomach may be done, thus avoiding the need for invasive but localized biopsy sampling for histological examination.

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