Artificial intelligence‐assisted endoscopy changes the definition of mucosal healing in ulcerative colitis

Nakase, Hiroshi; Hirano, Takehiro; Wagatsuma, Kohei; Ichimiya, Tadashi; Yamakawa, Tsukasa; Yokoyama, Yoshihiro; Hayashi, Yoshihiko; Hirayama, Daisuke; Kazama, Tomoe; Yoshii, Shinji; Yamano, Hiro‐o

doi:10.1111/den.13825

Cited by 24 publications

(26 citation statements)

References 49 publications

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“…Therefore, calculating the UCCIS score requires time and effort. Recently, endoscopic evaluation using artificial intelligence (AI) has been reported in gastrointestinal endoscopy, and AI has been used for diagnosis and disease assessment in UC [28][29][30]. Although evaluation of the entire colon using an endoscopic score, such as the UCCIS, tends to be avoided due to the complexity of the calculation method in clinical practice, if the state of the entire colonic mucosa could be automatically evaluated by AI, an endoscopic score evaluating the entire colon could be used more commonly in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Further research on the clinical relevance of the ulcerative colitis colonoscopic index of severity for predicting 5-year relapse

Ishida

Onoue

Miyazu

et al. 2021

Int J Colorectal Dis

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Purpose The ulcerative colitis colonoscopic index of severity (UCCIS) evaluates the state of the entire colonic mucosa in ulcerative colitis. However, no cut-off values of scores for predicting clinical relapse in patients with ulcerative colitis have been established. This study aimed to determine the cut-off values for predicting clinical relapse in patients with ulcerative colitis. Methods The endoscopic scores (sum of Mayo endoscopic subscores (S-MES) and UCCIS) of 157 patients with ulcerative colitis experiencing clinical remission and their subsequent clinical course were retrospectively reviewed. The optimal cut-off values for predicting relapse and relapse-free rates were analyzed by receiver operating characteristic analysis. Results Forty patients with ulcerative colitis experienced relapse within 24 months. The median UCCIS for these patients at the time of study enrollment was significantly higher than that for patients with clinical remission (P < 0.001). The cut-off value of the UCCIS for predicting relapse was 9.8. The relapse-free rate was significantly lower in patients with UCCIS ≥ 9.8 than in those with UCCIS < 9.8 (log-rank test P < 0.001). For patients who experienced relapse within 5 years, the optimal cut-off values for the UCCIS and S-MES were 10.2 and 1, respectively (P = 0.004). Conclusions The data from this study indicate that the USSIC is a more relevant score than the S-MES for predicting the time to relapse in patients with ulcerative colitis in remission.

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Section: Discussionmentioning

confidence: 99%

Further research on the clinical relevance of the ulcerative colitis colonoscopic index of severity for predicting 5-year relapse

Ishida

Onoue

Miyazu

et al. 2021

Int J Colorectal Dis

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“…Traditionally, the therapeutic effects are mainly evaluated through clinical symptom score. Nowadays, the disease activity can also be assessed by objective indicators such as endoscopic findings and biomarkers (7). The goals are not only to induce and maintain remission in symptom, to prevent and treat complications but also to achieve mucosal healing.…”

Section: Introductionmentioning

confidence: 99%

Treatment of Inflammatory Bowel Disease: A Comprehensive Review

2021

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Inflammatory bowel disease (IBD), as a global disease, has attracted much research interest. Constant research has led to a better understanding of the disease condition and further promoted its management. We here reviewed the conventional and the novel drugs and therapies, as well as the potential ones, which have shown promise in preclinical studies and are likely to be effective future therapies. The conventional treatments aim at controlling symptoms through pharmacotherapy, including aminosalicylates, corticosteroids, immunomodulators, and biologics, with other general measures and/or surgical resection if necessary. However, a considerable fraction of patients do not respond to available treatments or lose response, which calls for new therapeutic strategies. Diverse therapeutic options are emerging, involving small molecules, apheresis therapy, improved intestinal microecology, cell therapy, and exosome therapy. In addition, patient education partly upgrades the efficacy of IBD treatment. Recent advances in the management of IBD have led to a paradigm shift in the treatment goals, from targeting symptom-free daily life to shooting for mucosal healing. In this review, the latest progress in IBD treatment is summarized to understand the advantages, pitfalls, and research prospects of different drugs and therapies and to provide a basis for the clinical decision and further research of IBD.

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“…Recently, AI solutions have been explored to better define mucosal healing in IBD[ 70 ]. With the incidence and global burden of IBD still on the rise, involving a large number of young patients with normal life expectancy, there is a constant need for more accurate ways to stratify risks and predict prognosis in these patients.…”

Section: Applications Of Ai In Endoscopic Techniquesmentioning

confidence: 99%

“…With the incidence and global burden of IBD still on the rise, involving a large number of young patients with normal life expectancy, there is a constant need for more accurate ways to stratify risks and predict prognosis in these patients. Because the concept of mucosal healing is a rather new concept, the authors expect that AI tools for healing evaluation through endoscopic monitoring, which have recently been developed, could play a key role in better standardizing it[ 70 ].…”

Section: Applications Of Ai In Endoscopic Techniquesmentioning

confidence: 99%

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Berbís¹,

Aneiros‐Fernández²,

Olivares³

et al. 2021

WJG

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The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization ( i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

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Artificial intelligence‐assisted endoscopy changes the definition of mucosal healing in ulcerative colitis

Cited by 24 publications

References 49 publications

Further research on the clinical relevance of the ulcerative colitis colonoscopic index of severity for predicting 5-year relapse

Further research on the clinical relevance of the ulcerative colitis colonoscopic index of severity for predicting 5-year relapse

Treatment of Inflammatory Bowel Disease: A Comprehensive Review

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

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