Artificial intelligence-assisted video colonoscopy for disease monitoring of ulcerative colitis: A prospective study.

BACKGROUNDS AND AIMS: The Mayo endoscopic subscore (MES) is the most popular endoscopic disease activity measure of ulcerative colitis (UC). Artificial intelligence (AI)-assisted colonoscopy is expected to reduce diagnostic variability among endoscopists. However, no study has been conducted to ascertain whether AI-based MES assignments can help predict clinical relapse, nor has AI been verified to improve the diagnostic performance of non-specialists. METHODS: This open-label, prospective cohort study enrolled 110 patients with UC in clinical remission. The AI algorithm was developed using 74713 images from 898 patients who underwent colonoscopy at three centers. Patients were followed up after colonoscopy for 12 months, and clinical relapse was defined as a partial Mayo score >2. A multi-video, multi-reader analysis involving 124 videos was conducted to determine whether the AI system reduced the diagnostic variability among six non-specialists. RESULTS: The clinical relapse rate for patients with AI-based MES = 1 (24.5% [12/49]) was significantly higher (log-rank test, P = 0.01) than that for patients with AI-based MES = 0 (3.2% [1/31]). Relapse occurred during the 12-month follow-up period in 16.2% (13/80) of patients with AI-based MES = 0 or 1 and 50.0% (10/20) of those with AI-based MES = 2 or 3 (log-rank test, P = 0.03). Using AI resulted in better inter- and intra-observer reproducibility than endoscopists alone. CONCLUSIONS: Colonoscopy using the AI-based MES system can stratify the risk of clinical relapse in patients with UC and improve the diagnostic performance of non-specialists.

A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study.

BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted image-enhanced endoscopy may help nonexperts provide objective accurate predictions with the use of optical imaging. We aimed to develop a novel AI-based system using 8853 images from 167 patients with UC to diagnose "vascular-healing" and establish the role of AI-based vascular-healing for predicting the outcomes of patients with UC. METHODS: This open-label prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI-based vascular-active or vascular-healing. Mayo endoscopic subscore (MES), AI outputs, and histologic assessment were recorded for 6 colorectal segments from each patient. Patients were followed up for 12 months. Clinical relapse was defined as a partial Mayo score >2 RESULTS: The clinical relapse rate was significantly higher in the AI-based vascular-active group (23.9% [16/67]) compared with the AI-based vascular-healing group (3.0% [1/33)]; P = .01). In a subanalysis predicting clinical relapse in patients with MES ≤1, the area under the receiver operating characteristic curve for the combination of complete endoscopic remission and vascular healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). CONCLUSIONS: AI-based vascular-healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.

Effectiveness of endoscopic resection for colorectal neoplasms in ulcerative colitis: a multicenter registration study.

BACKGROUND AND AIMS: Patients with ulcerative colitis (UC) are at risk of developing colorectal cancer. The feasibility of endoscopic resection (ER) for UC-associated neoplasia has been suggested, but its efficacy and safety remain unclear. We aimed to assess the efficacy and safety of ER for colorectal neoplasms in patients with UC. METHODS: This was a retrospective, multicenter cohort study of patients with UC who initially underwent ER or surgery for colorectal neoplasms between April 2015 and March 2021. Patients who had prior colorectal neoplastic lesions were excluded. RESULTS: Among 213 men and 123 women analyzed, the mean age at UC onset was 41.6 years, and the mean age at neoplasia diagnosis was 56.1 years for 240 cases of total colitis, 59 cases of left-sided colitis, 31 cases of proctitis, and 6 cases of segmental colitis. EMR was performed for 142 lesions, and endoscopic submucosal dissection (ESD) was performed for 96 lesions. The perforation rate was 2.5% for all 238 lesions removed by ER and 6.3% for the 96 lesions removed by ESD. Among 146 ER lesions followed up with endoscopy, the local recurrence rate was 2.7%. The incidence of metachronous neoplasia after ER was 6.1%. All patients were followed a median of 34.7 months after initial treatment, and 5 died (all surgical cases). Overall survival was significantly higher in the ER group than in the surgery group (P = .0085). CONCLUSIONS: ER for colorectal neoplasms in UC may be acceptable in selected cases, although follow-up for metachronous lesions is necessary.

Use of advanced endoscopic technology for optical characterization of neoplasia in patients with ulcerative colitis: Systematic review.

OBJECTIVES: Advances in endoscopic technology, including magnifying and image-enhanced techniques, have been attracting increasing attention for the optical characterization of colorectal lesions. These techniques are being implemented into clinical practice as cost-effective and real-time approaches. Additionally, with the recent progress in endoscopic interventions, endoscopic resection is gaining acceptance as a treatment option in patients with ulcerative colitis (UC). Therefore, accurate preoperative characterization of lesions is now required. However, lesion characterization in patients with UC may be difficult because UC is often affected by inflammation, and it may be characterized by a distinct "bottom-up" growth pattern, and even expert endoscopists have relatively little experience with such cases. In this systematic review, we assessed the current status and limitations of the use of optical characterization of lesions in patients with UC. METHODS: A literature search of online databases (MEDLINE via PubMed and CENTRAL via the Cochrane Library) was performed from 1 January 2000 to 30 November 2021. RESULTS: The database search initially identified 748 unique articles. Finally, 25 studies were included in the systematic review: 23 focused on differentiation of neoplasia from non-neoplasia, one focused on differentiation of UC-associated neoplasia from sporadic neoplasia, and one focused on differentiation of low-grade dysplasia from high-grade dysplasia and cancer. CONCLUSIONS: Optical characterization of neoplasia in patients with UC, even using advanced endoscopic technology, is still challenging and several issues remain to be addressed. We believe that the information revealed in this review will encourage researchers to commit to the improvement of optical diagnostics for UC-associated lesions.

Combined endocytoscopy with pit pattern diagnosis in ulcerative colitis-associated neoplasia: Pilot study.

OBJECTIVES: Ulcerative colitis-associated neoplasias (UCAN) are often flat with an indistinct boundary from surrounding tissues, which makes differentiating UCAN from non-neoplasias difficult. Pit pattern (PIT) has been reported as one of the most effective indicators to identify UCAN. However, regenerated mucosa is also often diagnosed as a neoplastic PIT. Endocytoscopy (EC) allows visualization of cell nuclei. The aim of this retrospective study was to demonstrate the diagnostic ability of combined EC irregularly-formed nuclei with PIT (EC-IN-PIT) diagnosis to identify UCAN. METHODS: This study involved patients with ulcerative colitis whose lesions were observed by EC. Each lesion was diagnosed by two independent expert endoscopists, using two types of diagnostic strategies: PIT alone and EC-IN-PIT. We evaluated and compared the diagnostic abilities of PIT alone and EC-IN-PIT. We also examined the difference in the diagnostic abilities of an EC-IN-PIT diagnosis according to endoscopic inflammation severity. RESULTS: We analyzed 103 lesions from 62 patients; 23 lesions were UCAN and 80 were non-neoplastic. EC-IN-PIT diagnosis had a significantly higher specificity and accuracy compared with PIT alone: 84% versus 58% (P < 0.001), and 88% versus 67% (P < 0.01), respectively. The specificity and accuracy were significantly higher for Mayo endoscopic score (MES) 0-1 than MES 2-3: 93% versus 68% (P < 0.001) and 95% versus 74% (P < 0.001), respectively. CONCLUSIONS: Our novel EC-IN-PIT strategy had a better diagnostic ability than PIT alone to predict UCAN from suspected and initially detected lesions using conventional colonoscopy. UMIN clinical trial (UMIN000040698).

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission. METHODS: This open-label, prospective, cohort study was conducted in a referral center. The cohort comprised 145 consecutive patients with UC in clinical remission who underwent AI-assisted colonoscopy with a contact-microscopy function. We classified patients into either the Healing group or Active group based on the AI outputs during colonoscopy. The primary outcome measure was clinical relapse of UC (defined as a partial Mayo score >2) during 12 months of follow-up after colonoscopy. RESULTS: Overall, 135 patients completed the 12-month follow-up after AI-assisted colonoscopy. AI-assisted colonoscopy classified 61 patients as the Healing group and 74 as the Active group. The relapse rate was significantly higher in the AI-Active group (28.4% [21/74]; 95% confidence interval, 18.5%-40.1%) than in the AI-Healing group (4.9% [3/61]; 95% confidence interval, 1.0%-13.7%; P < .001). CONCLUSIONS: Real-time use of AI predicts the risk of clinical relapse in patients with UC in clinical remission, which helps clinicians make real-time decisions regarding treatment interventions. (Clinical trial registration number: UMIN000036650.).

Beyond complete endoscopic healing: Goblet appearance using an endocytoscope to predict future sustained clinical remission in ulcerative colitis.

OBJECTIVES: Complete endoscopic healing, defined as Mayo endoscopic score (MES) = 0, is an optimal target in the treatment of ulcerative colitis (UC). However, some patients with MES = 0 show clinical relapse within 12 months. Histologic goblet mucin depletion has emerged as a predictor of clinical relapse in patients with MES = 0. We observed goblet depletion in vivo using an endocytoscope, and analyzed the association between goblet appearance and future prognosis in UC patients. METHODS: In this retrospective cohort study, all enrolled UC patients had MES = 0 and confirmed clinical remission between October 2016 and March 2020. We classified the patients into two groups according to the goblet appearance status: preserved-goblet and depleted-goblet groups. We followed the patients until March 2021 and evaluated the difference in cumulative clinical relapse rates between the two groups. RESULTS: We identified 125 patients with MES = 0 as the study subjects. Five patients were subsequently excluded. Thus, we analyzed the data for 120 patients, of whom 39 were classified as the preserved-goblet group and 81 as the depleted-goblet group. The patients were followed-up for a median of 549 days. During follow-up, the depleted-goblet group had a significantly higher cumulative clinical relapse rate than the preserved-goblet group (19% [15/81] vs. 5% [2/39], respectively; P = 0.02). CONCLUSIONS: Observing goblet appearance in vivo allowed us to better predict the future prognosis of UC patients with MES = 0. This approach may assist clinicians with onsite decision-making regarding treatment interventions without a biopsy.

Real-Time Artificial Intelligence-Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy.

BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists distinguish between neoplastic polyps requiring removal and nonneoplastic polyps not requiring removal. In this study, we tested whether CADx analyzed images helped in this decision-making process. METHODS: We performed a multicenter clinical study comparing a novel CADx-system that uses real-time ultra-magnifying polyp visualization during colonoscopy with standard visual inspection of small (≤5 mm in diameter) polyps in the sigmoid colon and the rectum for optical diagnosis of neoplastic histology. After committing to a diagnosis (i.e., neoplastic, uncertain, or nonneoplastic), all imaged polyps were removed. The primary end point was sensitivity for neoplastic polyps by CADx and visual inspection, compared with histopathology. Secondary end points were specificity and colonoscopist confidence level in unaided optical diagnosis. RESULTS: We assessed 1289 individuals for eligibility at colonoscopy centers in Norway, the United Kingdom, and Japan. We detected 892 eligible polyps in 518 patients and included them in analyses: 359 were neoplastic and 533 were nonneoplastic. Sensitivity for the diagnosis of neoplastic polyps with standard visual inspection was 88.4% (95% confidence interval [CI], 84.3 to 91.5) compared with 90.4% (95% CI, 86.8 to 93.1) with CADx (P=0.33). Specificity was 83.1% (95% CI, 79.2 to 86.4) with standard visual inspection and 85.9% (95% CI, 82.3 to 88.8) with CADx. The proportion of polyp assessment with high confidence was 74.2% (95% CI, 70.9 to 77.3) with standard visual inspection versus 92.6% (95% CI, 90.6 to 94.3) with CADx. CONCLUSIONS: Real-time polyp assessment with CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during a colonoscopy compared with optical evaluation without CADx. (Funded by the Research Council of Norway [Norges Forskningsråd], the Norwegian Cancer Society [Kreftforeningen], and the Japan Society for the Promotion of Science; UMIN number, UMIN000035213.)