# Application of OCT in the Gastrointestinal Tract
## 4 New Technologies and Future Applications  
### Artificial Intelligence (AI)  
From music suggestion algorithms to air traffic management, artificial intelligence (AI) is omnipresent in today's cutting edge technology. However, unlike programs for checkers and chess, there may be too many predictive parameters for the detection of GI tract neoplasias for a knowledge base to be realistically programmed. Thus, machines must be able to learn. In machine learning, computational methods use experience (i.e. training) to make predictions and improve performance [101]. Deep learning addresses the major pitfall of machine learning, i.e., failure to recognize factors of variance in representation learning, by introducing and solving in terms of other simpler representations [102]. While deep learning can be viewed from a neurally-inspired perspective (often called a \"neural network\"), having multiple levels of composition need not be neurally inspired [102]. AI has been applied to endoscopic images to help highlight or identify areas of GI cancers [103]. The miss rates for early neoplastic lesions in the GI tract are significant; one retrospective study found a miss rate of 75.2% for gastric superficial neoplasia (GSNs) [104]. [PERSON] et al. demonstrated the feasibility of deep learning in order to detect early cancers and even map the stomach to address the blind spots responsible for many of these misses during gastroscopies [105]. In the colon, AI may be applied to both cancerous and non-cancerous conditions. In ulcerative colitis (UC), a computer-aided diagnosis (CAD) system was able to identify histologic inflammation associated with UC with 74% sensitivity, 97% specificity and 91% accuracy; this is useful, as WLE struggles to differentiate between healing and inflammation (active disease) [106]. For detection of pre-cancerous lesions, polyps may be missed under WLE at a rate of up to 22% [107]. Thus, research has been aimed at training computers to recognize polyp shape [108], texture, and color [109]. The recently described Window Median Depth of valleys Accumulation (WM-DOVA) energy maps system defines polyps as mucosal protrusions and their boundaries in terms of intensity valleys [110,111]. On the basis of this model, which fits better for zenithal views of the polyp, a pixel inside a polyp would be surrounded by high-intensity valleys in the majority of directions, with brighter regions corresponding to pixels with a higher likelihood of a polyp being present [110]. This algorithm worked better for smaller polyps of type 0-II, and the main causes of failure were deviation from the polyp appearance model, low image quality and poor prep [110]. In the esophagus, combining OCT and AI has been aimed to improve detection of neoplasias beyond the present sensitivity of random biopsies per the Seattle Protocol [112]. OCT address this problem with its volumetric rapid imaging, but its interpretation may be time/labor intensive due to the volume of images [113] and may be highly reader-dependent. However, the voluminous library of OCT images could be used for AI training. In BE, the parameters of atypical gland counts, lack of mucosal layering, and surface-to-surface intensity can be used as metrics [66]. Although performed ex-vivo, a study by [PERSON] et al. used a novel VLE diagnostic algorithm (VLE-DA) for dysplastic detection, producing a sensitivity of 86%, a specificity of 88%, and a diagnostic accuracy of 87% [66], which approaches the PIVI standards. Although the authors used an earlier form of OCT technology's diagnostic scoring index, they see an in vivo possibility for using an image viewer region of interest (ROI) over a 1 cm longitudinal distance of BE mucosa [66]. Given the importance of subsurface gland-like structures in the esophagus [62], computer-aided detection was devised to highlight these structures to potentially guide biopsies or ablative treatment in the future [114].