Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation

Abstract

In this editorial we comment on the article by Chan et al. The study presents the most comprehensive comparative evaluation to date of deep learning models for multi-class segmentation of upper gastrointestinal diseases, leveraging a novel 3313-image, nine-class clinical dataset alongside the public EDD2020 benchmark. Their results demonstrate that hierarchical, pre-trained encoders (notably Swin-UMamba-D) deliver the highest segmentation accuracy, while SegFormer balances accuracy with computational efficiency, an important consideration for clinical deployment. Beyond raw performance metrics, the work confronts core translational barriers: Limited and biased datasets, lighting and imaging variability, boundary ambiguity, and multi-label complexity. This Editorial argues that the manuscript marks a pivotal shift from isolated technical advances toward clinically-minded validation of segmentation systems, and proposes a concrete agenda for the field to accelerate safe, generalizable, and ethically responsible adoption of automated endoscopic assistance.

Keywords: Gastrointestinal disease; Endoscopy; Automated segmentation; Deep learning; Multi-class model; Comparative study; Artificial intelligence

Core Tip: This study evaluates 17 advanced deep learning models, including convolutional neural network-, transformer-, and mamba-based architectures, for multi-class upper gastrointestinal disease segmentation. Swin-UMamba achieves the highest segmentation accuracy, while SegFormer balances efficiency and performance. Automated segmentation demonstrates significant clinical value by improving diagnostic precision, reducing missed diagnoses, streamlining treatment planning, and easing physician workload. Key challenges include lighting variability, vague lesion boundaries, multi-label complexities, and dataset limitations. Future directions, such as multi-modal learning, self-supervised techniques, spatio-temporal modeling, and rigorous clinical validation, are essential to enhance model robustness and ensure applicability in diverse healthcare settings for better patient outcomes.