The Argos project: The development of a computer‐aided detection system to improve detection of Barrett's neoplasia on white light endoscopy

Abstract

Background Computer-aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia. Aim To develop a CAD system using endoscopic images of Barrett's neoplasia. Methods White light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non-dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images. The overlap area of at least four delineations was labelled as the ‘sweet spot’. The area with at least one delineation was labelled as the ‘soft spot’. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave-one-out cross-validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red-flag indication score). Results Accuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red-flagged the soft spot in 100 and 90%, respectively. Conclusion This uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real-time automated detection of Barrett's neoplasia.

Artificial Intelligence in Gastrointestinal Endoscopy

“…43 De Groof et al continued work on CAD development for Barrett's esophagus detection. 44 Prospective collection of white-light imaging from 40 Barrett's and 20 non-dysplastic patients were delineated by six experts for suspected areas of Barrett's. Areas with >50% overlap by the experts were the "sweet spot" and with one expert identifying dysplasia the "soft spot" used to train the CAD.…”

“…With a mean time of 1.051 s/image, the algorithm rapidly identified Barrett' s esophagus in white-light imaging. 44 Our research group described the development of a high-functioning CNN to identify early neoplasia within Barrett' s esophagus. Trained on 916 images of early esophageal neoplasia or T1 adenocarcinoma confirmed by biopsy from 65 patients, this image set was combined with an equal number of Barrett's esophagus without high-grade dysplasia to create a training set of images, with 458 images separated for validation.…”

Artificial intelligence technique in detection of early esophageal cancer

“…But the improvement still not satisfied endoscopists. This situation stimulated development of CAD system for early neoplastic lesions in BE based on supervised ML[ 14 , 15 ]. However, it was still difficult for this system to locate BE-related early neoplastic lesions and to select biopsy sites.…”

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future

“…The system delineated the soft spot and indicated the preferred biopsy location (red-flagged the area) in 100% and 90% of cases, respectively. The total time needed by the algorithm to analyze all images and delineate lesions was 61.8 s [ 65 ]. This research adds new advances toward real-time automated recognition of Barrett’s neoplasia.…”