Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics

Abstract

Gastrointestinal (GI) cancers, particularly colorectal cancer, continue to be a major contributor to global cancer-related morbidity and mortality. Despite significant advancements in screening protocols and treatment strategies, early detection remains a clinical challenge due to the limitations of conventional diagnostic tools, which often suffer from inter-observer variability, limited sensitivity, and time-intensive procedures. In recent years the integration of artificial intelligence (AI), especially deep learning (DL) techniques, into medical diagnostics has opened new frontiers for enhancing detection accuracy, speed, and consistency across clinical domains. This review explores the transformative impact of DL-based AI models in detecting lower GI cancers, focusing on three key diagnostic modalities: Endoscopy; radiology; and histopathology. In endoscopic practice convolutional neural networks are used to detect and classify colorectal polyps in real-time, significantly reducing miss rates and aiding non-specialist endoscopists in decision-making. In radiology DL algorithms trained on computed tomography and magnetic resonance imaging data are valuable for automated lesion detection, segmentation, and staging, often outperforming conventional imaging. Histopathological analysis, traditionally reliant on manual examination, is now accelerated by DL models capable of processing whole-slide images to identify architectural distortions and cellular anomalies with high reproducibility and diagnostic accuracy. This review evaluates DL model performance, including sensitivity, specificity, and area under the curve and addresses technical and ethical challenges, including dataset diversity, interpretability, and integration into healthcare workflows. Ultimately, the convergence of AI and clinical medicine has the potential to improve diagnostic outcomes and personalized care for patients with lower GI cancers.

Keywords: Artificial intelligence; Gastrointestinal cancer; Endoscopy; Radiology; Histopathology; Computer-aided diagnosis

Core Tip: Artificial intelligence (AI), particularly deep learning, is revolutionizing gastrointestinal oncology by enhancing early detection, diagnostic precision, prognostication, and personalized treatment. Deep learning models such as convolutional neural networks improve polyp detection, automate tumor segmentation, and interpret histopathology with high accuracy. Emerging multimodal and explainable AI frameworks integrate imaging, molecular, and clinical data, fostering precision oncology. Despite challenges of data heterogeneity and generalizability, the synergy between AI and clinicians promises earlier diagnosis, individualized therapy, and improved outcomes in lower gastrointestinal cancers.