Expanding indications for ESD: mucosal disease (upper and lower gastrointestinal tract)

Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal Cancer

The aim of this study was to evaluate the diagnostic value of artificial intelligence algorithm combined with ultrasound endoscopy in early esophageal cancer and precancerous lesions by comparing the examination of conventional endoscopy and artificial intelligence algorithm combined with ultrasound endoscopy, and by comparing the real-time diagnosis of endoscopy and the ultrasonic image characteristics of artificial intelligence algorithm combined with endoscopic detection and pathological results. 120 cases were selected. According to the inclusion and exclusion criteria, 80 patients who met the criteria were selected and randomly divided into two groups: endoscopic examination combined with ultrasound imaging based on intelligent algorithm processing (cascade region-convolutional neural network (Cascade RCNN) model algorithm group) and simple use of endoscopy group (control group). This study shows that the ultrasonic image of artificial intelligence algorithm is effective, and the detection performance is better than that of endoscopic detection. The results are close to the gold standard of doctor recognition, and the detection time is greatly shortened, and the recognition time is shortened by 71 frames per second. Compared with the traditional convolutional neural network (CNN) algorithm, the accuracy and recall of image analysis and segmentation using feature pyramid network are increased. The detection rates of CNN model, Cascade RCNN model, and endoscopic detection alone in early esophageal cancer and precancerous lesions are 56.3% (45/80), 88.8% (71/80), and 44.1% (35/80), respectively. The detection rate of Cascade RCNN model and CNN model was higher than that of endoscopy alone, and the difference was statistically significant (P < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of Cascade RCNN model were higher than those of CNN model, which was close to the gold standard for physician identification. This provided a reference basis for endoscopic ultrasound identification of early upper gastrointestinal cancer or other gastrointestinal cancers.

Underwater hybrid endoscopic submucosal dissection in a rectal polyp: a case report of a new application of "underwater endoscopy"

BACKGROUND

Hybrid endoscopic submucosal dissection (ESD) has been described as an alternative to traditional ESD. This technique is less time consuming, but the en bloc resection rate is lower than in ESD. Similar to endoscopic mucosal resection, the underwater technique could improve preliminary disadvantages of hybrid ESD.

CASE REPORT

We attempted a mixture resection technique of Hybrid ESD with underwater endoscopic mucosal resection (EMR). Using this approach, an underwater hybrid ESD was successfully performed without adverse events on a 71 year old woman with a 20 mm adenoma. The histologic analysis identified a tubulovillious adenoma with high grade dysplasia and tumor free margins.

DISCUSSION

Underwater hybrid ESD could be an alternative to ESD. Moreover the modification of the "underwater method" provides a suitable way to overcome the technical drawbacks of the hybrid ESD.