Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up

Table 2 Artificial intelligence implications in the interpretation of endoscopic and capsule images of inflammatory bowel disease patients

Ref.	Purpose	AI/DL model	Design	Result
Peng et al[15], 2015	To predict the seasonal variation effect on the onset, relapse and severity of IBD patients	ANN	Retrospective	Great accuracy in predicting the frequency of relapse (Mean square error = 0.009, Mean absolute percentage error = 17.1%)
Maeda et al[16], 2019	To predict the persistence of histologic inflammation in ulcerative colitis patients using endoscopy images	SVM	Retrospective	Sensitivity, specificity, and accuracy of 74%, 97%, and 91%, respectively
Gottlieb et al[20], 2020	Determine the severity of UC from full-length endoscopy videos	CNN	Prospective	Inter-rater agreement factor (QWK) of 0.844 for eMS and 0.855 for UCEIS
Takenaka et al[21], 2020	To identify histological remission using colonoscopy images	Deep Neural Network	Prospective	Histologic remission identified with 92.9% accuracy
Stidham et al[22], 2019	To identify remission from disease group using colonoscopy images	CNN	Retrospective	Successfully identified the remission from the moderate-to-severe disease group with an AUROC of 0.966, a sensitivity of 83.0%, a specificity of 96.0%, PPV of 0.87, and a NPV of 0.94

AI: Artificial intelligence; DL: Deep learning; ANN: Artificial neural networks; SVM: Support vector machines; AUROC: Area under the receiver operating characteristic curves; NPV: Negative predictive value; PPV: Positive predictive value; CNN: Convolutional neural network.