As previously underlined, endoscopy plays a fundamental role in the diagnosis and assessment of IBD activity [5]. According to this concept, endoscopy should guarantee an exact staging of the disease and a high level of concordance between different operators. Indeed, the definition of recurrence or the assessment of remission are cornerstones in the disease management, thus guiding the next clinical or surgical decisions [33,34].

In the study of Ozawa, the authors designed a CAD system using a CNN and evaluated its performance in the identification of normal or inflamed mucosa, using a large dataset of endoscopic images from patients with UC [35]. The performance of this new tool was valuable, with areas under the receiver operating characteristic curves (AUROCs) of 0.86 and 0.98 in the identification of MES 0 (completely normal mucosa) and MES 0–1 (mucosal healing state), respectively [35]. In a similar experience from Stidham et al. [36] a CNN showed an AUROC of 0.96 in distinguishing endoscopic remission (MES = 0 or 1) from moderate to severe disease (MES = 2 or 3), with a good weighted κ agreement between the CNN and the adjudicated reference score for identifying exact MES (κ = 0.84; 95% CI, 0.83–0.86). The application of this CNN to the entirety of the colonoscopy videos had high accuracy in identifying moderate to severe disease with an AUROC of 0.97 [36].

Moreover, Gottlieb and colleagues [37] developed another recurrent neural network able to predict MES and UCEIS from entire endoscopy videos and not only from images. The system automatically selected the frame to be analyzed and scores were calculated on the colon section, showing high agreement with the human central reader score [37]. Similarly, a fully automated video analysis system was developed to assess the grade of UC activity and predicted MES in 78% of videos (κ = 0.84). In external clinical trial videos, reviewers agreed on MES in 82.8% of videos (κ = 0.78) [38].

Not only were automated systems able to assess endoscopic activity from still images [39], but they were also able to predict a binary version of the MES directly analyzing a raw colonoscopy video, resulting in a high level of accuracy (AUC of 0.94 for MES ≥ 1 and 0.85 for MES ≥ 2 and MES ≥ 3) [40]. Looking forward, it seems that AI can also guide real-time therapy decisions in patients with UC in clinical remission by helping to stratify the relapse risk one year after AI-assisted colonoscopy [41].

Other experiences pushed forward the application of AI in the prediction of histology. Indeed, Takenaka and colleagues [42] designed a deep neural network algorithm, defined as DNUC, based on more than 40,000 images from colonoscopies and 6000 biopsies of 875 patients prospectively collected. AI system evaluations were matched with the UCEIS score expressed for each image by three expert endoscopists and with the Geboes score determined by pathologists [43]. The DNUC revealed an accuracy of 90.9% and 92.9% in the detection of endoscopic and histological remission, respectively. In addition, Maeda et al. [44] developed a CADx system to predict persistent histological inflammation using endocytoscopy in 187 retrospectively collected patients. Endocytoscopy is one of the most valuable technologies, although it is not widely available in endoscopic departments. Providing ultra-high-resolution white light images (520x), endocytoscopy allows the so-called virtual histology or optical biopsy [45].

Finally, a multicenter study in inactive patients with UC (PRognOstiC valuE of rEd Density in Ulcerative Colitis: PROCEED-UC; NCT04408703) is planned to assess the predictive value of the RD score for sustained clinical remission. It is plausible that the RD score might be used in the future as the first objective operator-independent endoscopic target in a treat-to-target strategy in UC. The main characteristics of the studies on endoscopic AI application in IBD are summarized in Table 2.