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

Table 4 Convolutional neural network-based real-time artificial intelligence support and workflow integration in gastrointestinal endoscopy

Data	Architecture	Application	Key findings	Ref.
Gastroscopy images	Real-time anatomical classification with CNN	Real-time anatomical recognition during gastroscopy procedures	CNN accurately classified anatomical positions in real-time, reducing mislabeling and improving procedural documentation. Enabled smoother workflow integration with minimal latency	[150]
Gastroscopy videos	Frame-wise CNN classification	Automated disease detection during endoscopy video review	The system provided real-time frame classification, improving detection of GI abnormalities in long video sequences and assisting in efficient case triage	[151]
White-light endoscopy	CNN for real-time Helicobacter pylori status	Immediate assessment of Helicobacter pylori infection during endoscopy	Real-time feedback from the CNN allowed on-the-spot therapeutic decision-making and eliminated delays caused by biopsy processing	[113]
Esophagogastroduodenoscopy videos	CNN-based detection system integrated with endoscope	Real-time detection of gastroesophageal varices	The system was validated across multiple centers and significantly reduced miss rates of varices in real-time, enhancing early intervention	[152]
Colonoscopic images	Deep CNN classifier for anatomical site recognition	Automated real-time anatomical classification of colon images	CNN correctly labeled anatomical sites, reducing reliance on user memory and ensuring consistent documentation. Improved novice performance	[153]
Upper GI endoscopy	CNN detection assistant in randomised controlled trial	Detection of gastric neoplasms during routine procedures	A deep learning-based system reduced miss rate of gastric neoplasms significantly in a randomized controlled trial. Validated real-world clinical benefit	[154]
Upper GI anatomy images	Multi-task CNN model	Simultaneous detection of anatomical landmarks and structures	Model performed both classification and segmentation, improving navigation and assisting less experienced users in orientation	[155]
Esophagogastroduodenoscopy images	CNN trained on large anatomical dataset	Real-time classification of esophagogastroduodenoscopy anatomy	CNN classified images with expert-level accuracy, aiding report generation and training in real-time	[141]
GI endoscopy videos	CNN vs global feature comparison	Real-time disease detection efficiency benchmarking	CNNs proved significantly more accurate than traditional global features. Demonstrated readiness for real-time clinical deployment	[156]
GI tract videos	CNN with automated reporting module	End-to-end disease detection and report generation	Automatically generated reports based on real-time CNN classification, reducing documentation time and enhancing standardization	[157]

CNN: Convolutional neural network; GI: Gastrointestinal.