Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination

Table 3 Applications of convolutional neural networks for lesion segmentation and disease severity assessment in gastrointestinal diseases

Data	Architecture	Application	Key findings	Ref.
Double-balloon endoscopy images	Deep CNN with lesion segmentation and severity scoring	Automated detection and severity grading of Crohn’s ulcers from double-balloon endoscopy	The model accurately segmented ulcers and assessed their severity, enabling objective Crohn’s Disease monitoring. Outperformed traditional scoring methods and showed potential to reduce inter-observer variability	[125]
Capsule endoscopy images	CNN segmentation network trained on angiodysplasias	Automated segmentation of vascular lesions in small bowel images	Provided accurate lesion boundary identification for angiodysplasias, facilitating hemorrhage risk stratification. Supports quicker and more consistent diagnosis compared to manual review	[126]
Colon capsule endoscopy	CNN trained to detect blood and mucosal lesions	Simultaneous detection and segmentation of bleeding and mucosal abnormalities	Enabled precise localization of lesions and bleeding points in colon capsule footage. Improved lesion coverage and reduced diagnostic delay	[127]
Endoscopic images	CNN trained to grade UC severity	Automated assessment of UC severity from colonoscopy	Model achieved expert-level grading accuracy across multiple severity stages. Significantly reduced inter-observer bias, suggesting suitability for clinical trial endpoints	[128]
Colonoscopy images	CNN-based model for pattern recognition	Differentiation of Crohn’s disease vs UC from colonoscopy	The system accurately distinguished UC and Crohn’s disease patterns, providing real-time decision support for disease type classification	[114]
Endoscopic images	Deep learning classifier for depth prediction	Predicting submucosal invasion in gastric neoplasms	Model reliably estimated invasion depth, reducing unnecessary surgical intervention. Useful in pre-treatment risk stratification	[129]
Endoscopic images	CNN for multi-feature prediction	Prediction of early gastric cancer, invasion depth, and differentiation	AI model outperformed experts in cancer invasion depth and differentiation. Enabled non-invasive yet accurate diagnosis during endoscopy	[130]
Capsule endoscopy images	Deep neural network tailored to stricture detection	Detection of Crohn’s-related intestinal strictures	The model improved the detection of strictures, which are often missed in manual review. Accelerates diagnosis and may guide therapeutic decisions	[131]
Confocal laser endomicroscopy images	CNN trained for mucosal healing assessment	Confirmation of mucosal healing in Crohn’s disease	Enabled fine-grained assessment of healing vs inflammation. Provided high-resolution insight for assessing treatment efficacy	[132]
Endoscopic images	AI-assisted classification model	UC disease activity scoring using Mayo classification	Provided consistent and reproducible activity scores. Reduced assessment variability, improving clinical and research utility	[133]
Endoscopic images	CNN vs human graders comparison	Grading UC severity from colonoscopy	CNN showed equal or superior performance to human reviewers in grading UC. Suggested for use in high-throughput settings or trials	[134]
Capsule endoscopy videos	CNN for quantitative feature extraction	Quantitative analysis of celiac disease lesions	Model extracted and quantified villous atrophy and mucosal abnormalities. Offered an objective metric to monitor disease progression	[135]
Conventional endoscopy images	CNN trained on histological labels	Predicting invasion depth of gastric cancer	CNN-assisted depth prediction provided decision support for therapy planning. Could reduce need for unnecessary biopsies	[136]

CNN: Convolutional neural network; UC: Ulcerative colitis; AI: Artificial intelligence.