Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

BACKGROUND AND AIMS: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to assess H. pylori infection by using endoscopic videos in real time. METHODS: Endoscopic data were retrospectively obtained from Zhejiang Cancer Hospital (ZJCH) for the development, validation, and testing of the system. Stored videos from ZJCH were used for assessing and comparing the performance of IDEA-HP with that of endoscopists. Prospective consecutive patients undergoing esophagogastroduodenoscopy were enrolled to assess the applicability of clinical practice. The urea breath test was used as the gold standard for diagnosing H. pylori infection. RESULTS: In 100 videos, IDEA-HP achieved a similar overall accuracy of assessing H. pylori infection to that of experts (84.0% vs. 83.6% [P = 0.729]). Nevertheless, the diagnostic accuracy (84.0% vs. 74.0% [P<0.001]) and sensitivity (82.0% vs. 67.2% [P<0.001]) of IDEA-HP were significantly higher than those of the beginners. In 191 prospective consecutive patients, IDEA-HP achieved accuracy, sensitivity, and specificity of 85.3% (95% CI: 79.0%-89.3%), 83.3% (95% CI: 72.8%-90.5%), and 85.8% (95% CI: 77.7%-91.4%), respectively. CONCLUSIONS: Our results show that IDEA-HP has great potential for assisting endoscopists in assessing H. pylori infection status during actual clinical work.

Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.

BACKGROUND: Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools. AIMS: To develop an artificial intelligence (AI)-assisted EGD system able to automatically monitor blind spots in real-time. METHODS: An AI-based system, called the Intelligent Detection Endoscopic Assistant (IDEA), was developed using a deep convolutional neural network (DCNN) and long short-term memory (LSTM). The performance of IDEA for recognition of gastric sites in images and videos was evaluated. Primary outcomes included diagnostic accuracy, sensitivity, and specificity. RESULTS: A total of 170,297 images and 5779 endoscopic videos were collected to develop the system. As the test group, 3100 EGD images were acquired to evaluate the performance of DCNN in recognition of gastric sites in images. The sensitivity, specificity, and accuracy of DCNN were determined as 97.18%,99.91%, and 99.83%, respectively. To assess the performance of IDEA in recognition of gastric sites in EGD videos, 129 videos were used as the test group. The sensitivity, specificity, and accuracy of IDEA were 96.29%,93.32%, and 95.30%, respectively. CONCLUSIONS: IDEA achieved high accuracy for recognition of gastric sites in real-time. The system can be applied as a powerful assistant tool for monitoring blind spots during EGD.