Image Quality Analysis of Various Gastrointestinal Endoscopes: Why Image Quality Is a Prerequisite for Proper Diagnostic and Therapeutic Endoscopy

Reduced detection rate of artificial intelligence in images obtained from untrained endoscope models and improvement using domain adaptation algorithm

A training dataset that is limited to a specific endoscope model can overfit artificial intelligence (AI) to its unique image characteristics. The performance of the AI may degrade in images of different endoscope model. The domain adaptation algorithm, i.e., the cycle-consistent adversarial network (cycleGAN), can transform the image characteristics into AI-friendly styles. We attempted to confirm the performance degradation of AIs in images of various endoscope models and aimed to improve them using cycleGAN transformation. Two AI models were developed from data of esophagogastroduodenoscopies collected retrospectively over 5 years: one for identifying the endoscope models, Olympus CV-260SL, CV-290 (Olympus, Tokyo, Japan), and PENTAX EPK-i (PENTAX Medical, Tokyo, Japan), and the other for recognizing the esophagogastric junction (EGJ). The AIs were trained using 45,683 standardized images from 1,498 cases and validated on 624 separate cases. Between the two endoscope manufacturers, there was a difference in image characteristics that could be distinguished without error by AI. The accuracy of the AI in recognizing gastroesophageal junction was >0.979 in the same endoscope-examined validation dataset as the training dataset. However, they deteriorated in datasets from different endoscopes. Cycle-consistent adversarial network can successfully convert image characteristics to ameliorate the AI performance. The improvements were statistically significant and greater in datasets from different endoscope manufacturers [original → AI-trained style, increased area under the receiver operating characteristic (ROC) curve, P-value: CV-260SL → CV-290, 0.0056, P = 0.0106; CV-260SL → EPK-i, 0.0182, P = 0.0158; CV-290 → CV-260SL, 0.0134, P < 0.0001; CV-290 → EPK-i, 0.0299, P = 0.0001; EPK-i → CV-260SL, 0.0215, P = 0.0024; and EPK-i → CV-290, 0.0616, P < 0.0001]. In conclusion, cycleGAN can transform the diverse image characteristics of endoscope models into an AI-trained style to improve the detection performance of AI.

Endoscopy image enhancement method by generalized imaging defect models based adversarial training

Objective. Smoke, uneven lighting, and color deviation are common issues in endoscopic surgery, which have increased the risk of surgery and even lead to failure. Approach. In this study, we present a new physics model driven semi-supervised learning framework for high-quality pixel-wise endoscopic image enhancement, which is generalizable for smoke removal, light adjustment, and color correction. To improve the authenticity of the generated images, and thereby improve the network performance, we integrated specific physical imaging defect models with the CycleGAN framework. No ground-truth data in pairs are required. In addition, we propose a transfer learning framework to address the data scarcity in several endoscope enhancement tasks and improve the network performance. Main results. Qualitative and quantitative studies reveal that the proposed network outperforms the state-of-the-art image enhancement methods. In particular, the proposed method performs much better than the original CycleGAN, for example, the structural similarity improved from 0.7925 to 0.8648, feature similarity for color images from 0.8917 to 0.9283, and quaternion structural similarity from 0.8097 to 0.8800 in the smoke removal task. Experimental results of the proposed transfer learning method also reveal its superior performance when trained with small datasets of target tasks. Significance. Experimental results on endoscopic images prove the effectiveness of the proposed network in smoke removal, light adjustment, and color correction, showing excellent clinical usefulness.

Endoscopic resection of early gastric cancer: current status and new approaches

Endoscopic resection (ER) of early gastric cancer (EGC) has been an optimal treatment for selected patients. As endoscopic submucosal dissection (ESD) has been widely used for treatment of EGC, concerns have been asked to achieve curative resection for EGC while guaranteeing precise prediction of lymph node metastasis (LNM). Moreover, a new microscopic imaging for precise endoscopic diagnosis of EGC is introduced. This review covers the current status and new approaches of ER of EGC.

Effect of nursing intervention on gastrointestinal endoscopy in elderly patients with coronary heart disease

AIM: To evaluate the effect of nursing intervention on gastrointestinal endoscopy in elderly patients with coronary heart disease, in order to provide better nursing guidance for patients with coronary heart disease.

METHODS: One hundred and ten elderly patients with coronary heart disease who underwent gastrointestinal endoscopy at our hospital from June 2013 to June 2015 were randomly divided into either an intervention group or a conventional group, with 55 cases in each group. The conventional group received conventional nursing care during gastrointestinal endoscopy, and the intervention group received nursing intervention during gastrointestinal endoscopy. Nursing effects were observed in both groups.

RESULTS: The endoscopy examination time and clinical nursing satisfaction score differed significantly between the intervention group and conventional group (22.7 min ± 6.3 min vs 33.8 min ± 7.1 min, 92.5 ± 4.7 vs 77.9 ± 5.2, P < 0.05 for both). After nursing intervention, SAS and SDS scores were statistically significant between the intervention group and conventional group (23.6 ± 4.2 vs 36.2 ± 5.1, 22.1 ± 3.7 vs 37.2 ±3.3, P < 0.05). The rate of complications was significantly lower in the intervention group than in the conventional group (7.3% vs 20.0%, P < 0.05).

CONCLUSION: For elderly patients with coronary heart disease patients undergoing gastrointestinal endoscopy, implementation of full intervention is feasible, can shorten the examination time, alleviate psychological pressure of patients, reduce complications, and improve clinical nursing satisfaction.