Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey

TL;DR

This survey reviews AI-based methods for detecting signet ring cells, highlighting advancements, challenges, and future directions to improve diagnostic accuracy and clinical application.

Contribution

It provides a comprehensive categorization and analysis of AI algorithms for SRC detection, bridging the gap between research and clinical practice.

Findings

Unimodal image-based algorithms include classification, detection, segmentation.

Multi-modal algorithms combine various data types for improved analysis.

Current methods face challenges in clinical translation and robustness.

Abstract

Signet ring cells (SRCs), associated with a high propensity for peripheral metastasis and poor prognosis, critically influence surgical decision-making and outcome prediction. However, their detection remains challenging even for experienced pathologists. While artificial intelligence (AI)-based automated SRC diagnosis has gained increasing attention for its potential to enhance diagnostic efficiency and accuracy, existing methodologies lack systematic review. This gap impedes the assessment of disparities between algorithmic capabilities and clinical applicability. This paper presents a comprehensive survey of AI-driven SRC analysis from 2008 through June 2025. We systematically summarize the biological characteristics of SRCs and challenges in their automated identification. Representative algorithms are analyzed and categorized as unimodal or multi-modal approaches. Unimodal algorithms, encompassing image, omics, and text data, are reviewed; image-based ones are further subdivided into classification, detection, segmentation, and foundation model tasks. Multi-modal algorithms integrate two or more data modalities (images, omics, and text). Finally, by evaluating current methodological performance against clinical assistance requirements, we discuss unresolved challenges and future research directions in SRC analysis. This survey aims to assist researchers, particularly those without medical backgrounds, in understanding the landscape of SRC analysis and the prospects for intelligent diagnosis, thereby accelerating the translation of computational algorithms into clinical practice.