Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks

BACKGROUND Small intestinal bleeding (SIB) remains a significant challenge in the diagnosis of obscure gastrointestinal bleeding. While capsule endoscopy (CE) is the gold standard for visualization, manual interpretation of the extensive video footage is labor-intensive and subject to inter-observer variability. Although convolutional neural networks (CNNs) have improved lesion detection, standard models often fail to account for temporal continuity, limiting their ability to accurately predict the specific location of bleeding points within the small bowel. AIM To develop and validate a deep learning framework integrating CNNs with long short-term memory (LSTM) networks to enhance the automated detection and precise localization of SIB. METHODS This study employed two datasets for automated bleeding detection: One from National Cheng Kung University, consisting of white light imaging images from 100 patients obtained via PillCam™ SB 3 CE, and the Kvasir-Capsule Image dataset, which includes 47238 labeled images across 14 pathological categories. Nineteen continuous picture sequences were recovered, comprising 3806 bleeding photos and 3275 non-bleeding images. RESULTS Data augmentation was implemented, utilizing CNNs for feature extraction, succeeded by long short-term memory networks for prediction. The CNN model attained an accuracy of 98.6% for 10 categories and 96.7% for 2 categories. Findings demonstrate that CNN-LSTM models exhibit superior performance with expanded category sets. CONCLUSION These findings underscore the capability of deep learning models to enhance the accuracy and efficiency of CE-based gastrointestinal bleeding diagnosis, hence facilitating improved clinical decision-making.