Advances in the Endoscopic Diagnosis of Barrett Esophagus

Development and validation of artificial neural networks model for detection of Barrett's neoplasia: a multicenter pragmatic nonrandomized trial (with video)

BACKGROUND AND AIMS

The aim of this study was to develop and externally validate a computer-aided detection (CAD) system for the detection and localization of Barrett's neoplasia and assess its performance compared with that of general endoscopists in a statistically powered multicenter study by using real-time video sequences.

METHODS

In phase 1, the hybrid visual geometry group 16-SegNet model was trained by the use of 75,198 images and videos (96 patients) of neoplastic and 1,014,973 images and videos (65 patients) of nonneoplastic Barrett's esophagus. In phase 2, image-based validation was performed on a separate dataset of 107 images (20 patients) of neoplastic and 364 images (14 patients) of nonneoplastic Barrett's esophagus. In phase 3 (video-based external validation) we designed a real-time video-based study with 32 videos (32 patients) of neoplastic and 43 videos (43 patients) of nonneoplastic Barrett's esophagus from 4 European centers to compare the performance of the CAD model with that of 6 nonexpert endoscopists. The primary endpoint was the sensitivity of CAD diagnosis of Barrett's neoplasia.

RESULTS

In phase 2, CAD detected Barrett's neoplasia with sensitivity, specificity, and accuracy of 95.3%, 94.5%, and 94.7%, respectively. In phase 3, the CAD system detected Barrett's neoplasia with sensitivity, specificity, negative predictive value, and accuracy of 93.8%, 90.7%, 95.1%, and 92.0%, respectively, compared with the endoscopists' performance of 63.5%, 77.9%, 74.2%, and 71.8%, respectively (P < .05 in all parameters). The CAD system localized neoplastic lesions with accuracy, mean precision, and mean intersection over union of 100%, 0.62, and 0.54, respectively, when compared with at least 1 of the expert markings. The processing speed of the CAD detection and localization were 5 ms/image and 33 ms/image, respectively.

CONCLUSION

To our knowledge, this is the first study describing external (multicenter) validation of AI algorithms for the detection of Barrett's neoplasia on real-time endoscopic videos. The CAD system in this study significantly outperformed nonexpert endoscopists on real-time video-based assessment, achieving >90% sensitivity for neoplasia detection. This result needs to be validated during real-time endoscopic assessment.

A Novel Network With Parallel Resolution Encoders for the Diagnosis of Corneal Diseases

OBJECTIVE

To propose a deep-learning network for the diagnosis of two corneal diseases: Fuchs' endothlelial dystrophy and keratoconus, based on optical coherence tomography (OCT) images of the cornea.

METHODS

In this paper, we propose a novel network with parallel resolution-specific encoders and composite classification features to directly diagnose Fuchs' endothelial dystrophy and keratoconus using OCT images. Our proposed network consists of a multi-resolution input, multiple parallel encoders, and a composite of convolutional and dense features for classification. The purpose of using parallel resolution-specific encoders is to perform multi-resolution feature fusion. Also, using composite classification features enhances the dense feature learning. We implemented other related networks for comparison with our network and performed k-fold cross-validation on a dataset of 16,721 OCT images. We used saliency maps and sensitivity analysis to visualize our proposed network.

RESULTS

The proposed network outperformed other networks with an image classification accuracy of 0.91 and a scan classification accuracy of 0.94. The visualizations show that our network learned better features than other networks.

SIGNIFICANCE

The proposed methods can potentially be a step towards the early diagnosis of corneal diseases, which is necessary to prevent their progression, hence, prevent loss of vision.

Barrett esophagus: What to expect from Artificial Intelligence?

The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.

A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review

Endoscopy is the optimal choice of diagnosis of gastrointestinal (GI) diseases. Following the advancements made in medical technology, different kinds of novel endoscopy-methods have emerged. Although the significant progress in the penetration of endoscopic tools that have markedly improved the diagnostic rate of GI diseases, there are still some limitations, including instability of human diagnostic performance caused by intensive labor burden and high missed diagnosis rate of subtle lesions. Recently, artificial intelligence (AI) has been applied gradually to assist endoscopists in addressing these issues.

Future of diagnosing neoplasia in Barrett's esophagus: volumetric laser endomicroscopy

Esophageal adenocarcinoma (EAC) is one of the deadliest carcinoma faced by gastroenterologists. Any insult to esophagus that causes replacement of normal squamous epithelium with columnar intestinal epithelium is labelled as the initiating event of the metaplasia-neoplasia sequence. Barrett's esophagus is the precursor to EAC. Currently, endoscopically obtained biopsies are used to detect neoplastic changes in patients with Barrett's esophagus (BE); however, it is not cost effective and hence a better screening modality is needed. Volumetric laser endomicroscopy (VLE) has been under study for the past few years and has shown promising results to overcome the shortcoming faced in the biopsy samplings. It is a second-generation optical coherence tomography (OCT) that provides high-resolution cross-sectional imaging of the esophageal mucosa using near-infrared light. The principle is similar to endosonography, but image formation in OCT depends on variations in the reflection of light from different tissue layers rather than ultrasonic waves.

A guide to multimodal endoscopy imaging for gastrointestinal malignancy - an early indicator

Multimodality imaging is an essential aspect of endoscopic surveillance for the detection of neoplastic lesions, such as dysplasia or intramucosal cancer, because it improves the efficacy of endoscopic surveillance and therapeutic procedures in the gastrointestinal tract. This approach reveals mucosal abnormalities that cannot be detected by standard endoscopy. Currently, these imaging techniques are divided into those for primary detection and those for targeted imaging and characterization, the latter being used to visualize areas of interest in detail and permit histological evaluation. This Review outlines the use of virtual chromoendoscopy, narrow-band imaging, autofluorescence imaging, optical coherence tomography, confocal endomicroscopy and volumetric laser endomicroscopy as new imaging techniques for diagnostic investigation of the gastrointestinal tract. Insights into use of multimodal endoscopic imaging for early disease detection, in particular for pre-malignant lesions, in the oesophagus, stomach and colon are described.