Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Table 2 Artificial Intelligence in assessment of disease severity in inflammatory bowel disease

Ref.	AI classifier vs comparator	IBD type	Study design and sample size	Modality	Outcomes	Study results/validation cohort
Kumar et al[40], 2012	Support vector machines (SVM) vs human observers	CD	Cross-sectional, 50000 images (number of patients not given)	Small bowel capsule endoscopy	Endoscopic Inflammation	Database of 47 studies including 50000 capsule endoscopy images evaluating severity of small bowel lesions. Method had good precision (> 90% for lesion detection) and recall (> 90%) for lesions of varying severity. Validation cohort included
Biasci et al[41], 2019	Logistic regression with an adaptive Elastic-Net penalty. No comparator	CD/UC	Prospective cohort, 118 IBD patients	Transcriptomics from purified CD8 T cells and/or whole blood	Disease severity, medication escalation	A 17-gene qPCR-based classifier stratified patients into two distinct subgroups. IBDhi patients experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation [HR 2.65 (CD), 3.12 (UC)] and more escalations over time [for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value = 90.9% (CD), 100% (UC)]. Validation cohort included
Waljee et al[42], 2019	RF. No comparator	CD	Post-hoc analysis of prospective clinical trials, 401 CD patients	Clinical and laboratory data from publicly available clinical trials (UNITI-1, UNITI-2, and IM-UNITI)	Crohn's disease remission, C-reactive protein < 5 mg/L	A prediction model using the week-6 albumin to C-reactive protein ratio had an AUC of 0.76 [95% confidence interval (CI): 0.71-0.82]. Validation cohort included
Mahapatra et al[43], 2016	RF. No comparator	CD	Cross-sectional, 35 CD patients	Abdominal magnetic resonance imaging	Segmentation of diseased colon (intestinal inflammation)	Model segmentation accuracy ranged from 82.7% to 92.2%. Validation cohort included
Reddy et al[44], 2019	Gradient boosting machines vs logistic regression	CD	Retrospective, 3335 CD patients	Electronic medical record	Severity of intestinal inflammation (by C-reactive protein)	Machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (AUC) = 92.82%. Validation cohort included
Douglas et al[45], 2018	RF. No comparator	Peds CD	Cross-sectional, 20 CD patients, 20 healthy controls	Shotgun metagenomics (MGS), 16S rRNA gene sequencing	Disease State (Relapse/Remission)	MGS modules significantly classified samples by disease state (accuracy = 68.4%, P = 0.043 and accuracy = 65.8%, P = 0.03, respectively), 16S datasets had a maximum accuracy of 68.4% and P = 0.016 based on strain level for disease state. Validation cohort included
Maeda et al[46], 2019	SVM vs human reader	UC	Retrospective cohort, 187 UC patients	Endocytoscopy	Histologic inflammation	Computer aided diagnosis (CAD) of histologic inflammation provided diagnostic sensitivity, specificity, and accuracy as follows: 74% (95%CI: 65-81), 97% (95%CI: 95-99), and 91% (95%CI: 83-95), respectively. Its reproducibility was perfect (k = 1). Validation cohort included
Charisis et al[47], 2016	SVM vs human reader	CD	Retrospective cohort, 13 CD patients	Wireless capsule endoscopy (WCE) images	Endoscopic Inflammation	Experimental results, along with comparison with other related efforts, have shown that the hybrid adaptive filtering [HAF-Differential Lacunarity (DLac) analysis (HAF-DLac)] via SVM approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). Validation cohort included
Klang et al[48], 2020	Convolutional neural network (CNN) vs human reader	CD	Retrospective cohort, 49 CD patients	WCE images	Endoscopic Inflammation	Dataset included 17640 CE images from 49 patients: 7391 images with mucosal ulcers and 10249 images of normal mucosa. For randomly split images results, AUC was 0.99 with accuracies ranging from 95.4% to 96.7%. For individual patient-level experiments, the AUCs were 0.94-0.99. Validation cohort included
Ungaro et al[49], 2021	Random survival forest. No comparator	Peds CD	Retrospective case-control, 265 peds CD patients	Protein biomarkers using a proximity extension assay (Olink Proteomics)	Penetrating and stricturing complications	A model with 5 protein markers predicted penetrating complications with an AUC of 0.79 (95%CI: 0.76-0.82) compared to 0.69 (95%CI: 0.66-0.72) for serologies and 0.74 (95%CI: 0.71-0.77) for clinical variables. A model with 4 protein markers predicted structuring complications with an AUC of 0.68 (95%CI: 0.65-0.71) compared to 0.62 (95%CI: 0.59-0.65) for serologies and 0.52 (95%CI: 0.50-0.55) for clinical variables. Validation cohort included
Barash et al[50], 2021	Ordinal CNN. No comparator	CD	Retrospective cohort, 49 CD patients	WCE images	Ulcer Severity Grading	The classification accuracy of the algorithm was 0.91 (95%CI: 0.867-0.954) for grade 1 vs grade 3 ulcers, 0.78 (95%CI: 0.716-0.844) for grade 2 vs grade 3, and 0.624 (95%CI: 0.547-0.701) for grade 1 vs grade 2. Validation cohort included
Lamash et al[51], 2019	CNN vs semi-supervised and active learning models	CD	Retrospective cohort, 23 CD patients	Magnetic resonance imaging	Active Crohn’s Disease	CNN exhibited Dice similarity coefficient of 75% ± 18%, 81% ± 8%, and 97% ± 2% for the lumen, wall, and background, respectively. The extracted markers of wall thickness at the location of min radius (P = 0.0013) and the median value of relative contrast enhancement (P = 0.0033) could differentiate active and nonactive disease segments. Other extracted markers could differentiate between segments with strictures and segments without strictures (P < 0.05). Validation cohort included
Takenaka et al[52], 2020	Deep neural networks vs human reader (endoscopist)	UC	Prospective cohort, 2012 UC patients	Colonoscopy images	Endoscopic inflammation	Deep neural network identified patients with endoscopic remission with 90.1% accuracy (95%CI: 89.2-90.9) and a kappa coefficient of 0.798 (95%CI: 0.780-0.814), using findings reported by endoscopists as the reference standard. Validation cohort included
Bossuyt et al[53], 2020	Computer algorithm based on red density (RD) vs blinded central readers	UC	Prospective cohort, 29 UC patients, 6 healthy controls	Colonoscopy Images	Endoscopic and histologic inflammation	In the construction cohort, RD correlated with rhi (r = 0.74, P < 0.0001), Mayo endoscopic subscores (r = 0.76, P < 0.0001) and Endoscopic index of severity scores (r = 0.74, P < 0.0001). The RD sensitivity to change had a standardized effect size of 1.16. in the validation set, RD correlated with rhi (r = 0.65, P = 0.00002). Validation cohort included
Bhambhvani et al[54], 2021	CNN vs human reader (endoscopist)	UC	Retrospective cohort, 777 UC patients	Colonoscopy images	Mayo Endoscopic Scores (MES)	The final model classified MES 3 disease with an AUC of 0.96, MES 2 disease with an AUC of 0.86, and MES 1 disease with an AUC 0.89. Overall accuracy was 77.2%. Across MES 1, 2, and 3, average specificity was 85.7%, average sensitivity was 72.4%, average PPV was 77.7%, and the average NPV was 87.0%. Validation cohort included
Ozawa et al[55], 2019	CNN vs human reader (endoscopist)	UC	Retrospective cohort, 841 UC patients	Colonoscopy images	MES	The CNN-based CAD system showed a high level of performance with AUC of 0.86 and 0.98 to identify Mayo 0 and 0-1, respectively. The performance of the CNN was better for the rectum than for the right side and left side of the colon when identifying Mayo 0 (AUC = 0.92, 0.83, and 0.83, respectively). Validation cohort included
Bossuyt et al[56], 2021	Automated CAD Algorithm vs human reader	UC	Prospective cohort, 48 UC patients	Colonoscopy images with confocal laser endomicroscopy	Histologic Remission	The current automated CAD algorithm detects histologic remission with a high performance (sensitivity of 0.79 and specificity of 0.90) compared with the UCEIS (sensitivity of 0.95 and specificity of 0.69) and MES (sensitivity of 0.98 and specificity of 0.61). No validation cohort included
Stidham et al[57], 2019	CNN vs human reader	UC	Retrospective cohort, 3082 UC patients	Colonoscopy images	Endoscopy severity	The CNN was excellent for distinguishing endoscopic remission from moderate-to-severe disease with an AUC of 0.966 (95%CI: 0.967-0.972); a PPV of 0.87 (95%CI: 0.85-0.88) with a sensitivity of 83.0% (95%CI: 80.8-85.4) and specificity of96.0% (95%CI: 95.1-97.1); and NPV of 0.94 (95%CI: 0.93-0.95). No validation cohort included
Gottlieb et al[58], 2021	Neural network vs human central reader	UC	Prospective cohort, 249 UC patients	Colonoscopy images	Endoscopy severity	The model's agreement metric was excellent, with a quadratic weighted kappa of 0.844 (95%CI: 0.787-0.901) for endoscopic Mayo Score and 0.855 (95%CI: 0.80-0.91) for UCEIS. No validation cohort included

AI: Artificial intelligence; IBD: Inflammatory bowel disease; CD: Crohn’s disease; UC: Ulcerative colitis; AUC: Area under the curve; NPV: Negative predictive value; PPV: Positive predictive value; qPCR: Quantitative real-time polymerase chain reaction; HR: Hazard ratio.