Quantitative analysis of red color sign in the endoscopic evaluation of esophageal varices

Artificial intelligence in the diagnosis of cirrhosis and portal hypertension

Clinically significant portal hypertension is associated with an increased risk of developing gastroesophageal varices and hepatic decompensation. Hepatic venous pressure gradient measurement and esophagogastroduodenoscopy are the gold-standard methods for assessing clinically significant portal hypertension and gastroesophageal varices, respectively. However, invasiveness, cost, and feasibility limit their widespread use, especially if repeated and serial evaluations are required to assess the efficacy of pharmacotherapy. Artificial intelligence describes a range of techniques that allow machines to perform tasks typically thought to require human reasoning and problem-solving skills. Artificial intelligence has made great strides in the field of medicine, and is also involved in portal hypertension diagnosis. Artificial intelligence tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time. This review focuses on the recent advances in artificial intelligence for the noninvasive diagnosis of portal hypertension and gastroesophageal varices and monitoring of risk assessment of its complications in clinical practice.

Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using a deep convolutional neural network: a multicenter retrospective study (with video)

BACKGROUND AND AIMS

Rupture of gastroesophageal varices is the most common fatal adverse event of cirrhosis. EGD is considered the criterion standard for diagnosis and risk stratification of gastroesophageal variceal bleeding. The aim of this study was to train and validate a real-time deep convolutional neural network (DCNN) system, named ENDOANGEL, for diagnosing gastroesophageal varices and predicting the risk of rupture.

METHODS

After training with 8566 images of endoscopic gastroesophageal varices from 3021 patients and 6152 images of normal esophagus/stomach from 3168 patients, ENDOANGEL was also tested with independent images and videos. It was also compared with endoscopists in several aspects.

RESULTS

ENDOANGEL, in contrast with endoscopists, displayed higher accuracy of 97.00% and 92.00% in terms of detecting esophageal varices (EVs) and gastric varices (GVs) in an image contest (97.00% vs 93.94% , P < .01; 92.00% vs 84.43%, P < .05). It also surpassed endoscopists for red color signs of EVs and red spots of GVs (84.21% vs 73.45%, P < .01; 85.26% vs 77.52%, P < .05). Moreover, ENDOANGEL achieved comparable performance in the determination of size, form, color, and bleeding signs. ENDOANGEL also had good performance in making treatment suggestions. With regard to predicting risk factors in multicenter videos, ENDOANGEL showed great stability.

CONCLUSIONS

Our data suggest that DCNNs were precise in detecting both EVs and GVs and performed excellently in uncovering the endoscopic risk factors of gastroesophageal variceal bleeding. Thus, the application of DCNNs will assist endoscopists in evaluating gastroesophageal varices more objectively and precisely. (Clinical trial registration number: ChiCTR1900023970.).

Flexible imaging color enhancement improves visibility of transnasal endoscopic images in diagnosing esophageal varices: a multicenter prospective blinded study

OBJECTIVE

To clarify the usefulness of transnasal esophagogastroduodenoscope (N-EGD) with all flexible imaging color enhancement (FICE) patterns (0-9) for the diagnosis of esophageal varices (EV).

METHODS

A total of 50 patients with EV were examined during the same period by N-EGD without sedation and by peroral endoscopy (O-EGD) with sedation. The visibility of treatment scars, microvessels, recurrent EV and red color sign (RCS) were measured. Visibility was evaluated by five blinded observers (poor-good: 0-10), and the effect of both endoscopic examinations on the cardiorespiratory function of the patients was measured.

RESULTS

The image scores for O-EGD and N-EGD with ordinary mode and N-EGD with FICE were 7.3 ± 1.2, 6.1 ± 1.0 and 6.9 ± 1.0 for treatment scars; 7.2 ± 1.4, 6.2 ± 1.2 and 7.3 ± 1.0 for microvessels; 7.2 ± 1.2, 6.1 ± 1.0 and 7.1 ± 1.0 for recurrent EV and 7.2 ± 1.3, 6.1 ± 1.3, and 7.2 ± 1.2 for RCS, respectively (intraclass correlation coefficients > 0.6). When FICE patterns 2, 6 and 9 were used, visibility was much improved. Ten minutes after insertion, the double product values in the N-EGD with the FICE group were significantly better (P < 0.05).

CONCLUSIONS

N-EGD with FICE is very useful for the diagnosis of EV and can improve the visibility of N-EGD on each lesion to the same level as that of O-EGD. N-EGD with FICE does not require sedation and may reduce the risk of hepatic encephalopathy.