STAGING OF BARRETT’S NEOPLASIA USING ARTIFICIAL NEURAL NETWORKS, PROOF OF CONCEPT STUDY

Aims Endoscopic differentiation between intra-mucosal and submucosal Barrett’s neoplasia has several important implications but remains challenging even for expert endoscopists. Recent studies demonstrated promising results on AI-assisted detection of Barrett’s neoplasia, but data on AI-assisted staging is limited. We aimed to develop and validate an AI system for classification of Barrett’s neoplasia into intra-mucosal or submucosal, and compare its performance to expert endoscopists.

Methods The model, based on VGG-16 architecture, was trained on 117 images of prospectively collected and annotated Barrett’s neoplastic lesions. Rotation and random flip were used for data augmentation. The ground truth was the histological staging of endoscopically resected specimens performed by two pathologists with expertise in Barrett’s neoplasia. Images comprised of WLI, enhanced imaging, and magnification views. The model was designed to classify images as either intra-mucosal (pT1a) or submucosal (pT1b).Performance of the AI system was compared to a group of three experts.

Results

Table 1

Metric	AI model	Experts (n=3)
Accuracy	70.9%	73.3%
Sensitivity	72.5%	63.3%
Specificity	65.7%	83.3%

The AI model was tested on an independent dataset of 90 images. Accuracy, sensitivity and specificity and AUC of the AI model in differentiating between intra-mucosal and submucosal neoplasia was 70.9%,72.5%,65.7%, and 0.781 respectively. Mean accuracy, sensitivity and specificity of experts were 73.3%, 63.3% and 83.3% respectively. Processing speed of the AI system was 5 ms/image.

Fig. 1

Conclusions This study demonstrates the feasibility of AI-assisted staging of Barrett’s neoplasia on endoscopic images. The AI model’s performance was comparable to that of experts. More work is needed to further develop this early model and validate its use on real-time video sequences.

Publication History

Article published online:
14 April 2022