Copyright: ©Author(s) 2026.
World J Gastroenterol. Apr 21, 2026; 32(15): 116105
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116105
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116105
Figure 3 Schematic representation of the temporal frame sequences used for long short-term memory input.
A: Balanced sequence: Continuous video frames capturing the 3-second interval immediately before and the 3-second interval after the bleeding point; B: Post-event sequence: Discontinuous frames preceding the bleeding point, followed by a continuous 6-second sequence of the bleeding event; C: Pre-event sequence: A continuous 6-second sequence leading up to the bleeding point, followed by discontinuous frames.
- Citation: Kuo HY, Lee KH, Chou CK, Mukundan A, Karmakar R, Chen TH, Wang TL, Liu PH, Wang HC. Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks. World J Gastroenterol 2026; 32(15): 116105
- URL: https://www.wjgnet.com/1007-9327/full/v32/i15/116105.htm
- DOI: https://dx.doi.org/10.3748/wjg.v32.i15.116105