Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome.

IBS is not considered to be an organic disease and usually shows no abnormality on lower gastrointestinal endoscopy, although biofilm formation, dysbiosis, and histological microinflammation have recently been reported in patients with IBS. In this study, we investigated whether an artificial intelligence (AI) colorectal image model can identify minute endoscopic changes, which cannot typically be detected by human investigators, that are associated with IBS. Study subjects were identified based on electronic medical records and categorized as IBS (Group I; n = 11), IBS with predominant constipation (IBS-C; Group C; n = 12), and IBS with predominant diarrhea (IBS-D; Group D; n = 12). The study subjects had no other diseases. Colonoscopy images from IBS patients and from asymptomatic healthy subjects (Group N; n = 88) were obtained. Google Cloud Platform AutoML Vision (single-label classification) was used to construct AI image models to calculate sensitivity, specificity, predictive value, and AUC. A total of 2479, 382, 538, and 484 images were randomly selected for Groups N, I, C and D, respectively. The AUC of the model discriminating between Group N and I was 0.95. Sensitivity, specificity, positive predictive value, and negative predictive value of Group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The overall AUC of the model discriminating between Groups N, C, and D was 0.83; sensitivity, specificity, and positive predictive value of Group N were 87.5%, 46.2%, and 79.9%, respectively. Using the image AI model, colonoscopy images of IBS could be discriminated from healthy subjects at AUC 0.95. Prospective studies are needed to further validate whether this externally validated model has similar diagnostic capabilities at other facilities and whether it can be used to determine treatment efficacy

Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome

IBS is not considered to be an organic disease and usually shows no abnormality on lower gastrointestinal endoscopy, although biofilm formation, dysbiosis, and histological microinflammation have recently been reported in patients with IBS. In this study, we investigated whether an artificial intelligence (AI) colorectal image model can identify minute endoscopic changes, which cannot typically be detected by human investigators, that are associated with IBS. Study subjects were identified based on electronic medical records and categorized as IBS (Group I; n = 11), IBS with predominant constipation (IBS-C; Group C; n = 12), and IBS with predominant diarrhea (IBS-D; Group D; n = 12). The study subjects had no other diseases. Colonoscopy images from IBS patients and from asymptomatic healthy subjects (Group N; n = 88) were obtained. Google Cloud Platform AutoML Vision (single-label classification) was used to construct AI image models to calculate sensitivity, specificity, predictive value, and AUC. A total of 2479, 382, 538, and 484 images were randomly selected for Groups N, I, C and D, respectively. The AUC of the model discriminating between Group N and I was 0.95. Sensitivity, specificity, positive predictive value, and negative predictive value of Group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The overall AUC of the model discriminating between Groups N, C, and D was 0.83; sensitivity, specificity, and positive predictive value of Group N were 87.5%, 46.2%, and 79.9%, respectively. Using the image AI model, colonoscopy images of IBS could be discriminated from healthy subjects at AUC 0.95. Prospective studies are needed to further validate whether this externally validated model has similar diagnostic capabilities at other facilities and whether it can be used to determine treatment efficacy. Author summary This study reports on an endoscopic image artificial intelligence (AI) model for detecting irritable bowel syndrome (IBS). Endoscopic images of IBS patients usually do not have any teacher data because their changes cannot be detected by a human observer. However, we investigated the possibility of using the presence or absence of symptoms as teacher data, and found that endoscopic images of IBS patients could be discriminated with high accuracy from those of healthy subjects, and that endoscopic images of diarrhea-type IBS could also be discriminated from those of constipation-type IBS. It is expected that this will enable endoscopic AI diagnosis in other functional gastrointestinal diseases such as NERD and functional dyspepsia by building an image AI model based on the presence or absence of symptoms. In addition, this study uses a code-free deep learning approach, which has the potential to improve clinicians’ access to deep learning. Further research is needed to determine whether real-time IBS image determination as well as prediction of treatment efficacy is possible

Colonic TRPV4 overexpression is related to constipation severity

Abstract Background Chronic constipation is prevalent and involves both colon sensitivity and various changes in intestinal bacteria, particularly mucosa-associated microflora. Here we examined regulatory mechanisms of TRPV4 expression by co-culturing colon epithelial cell lines with intestinal bacteria and their derivatives. We also investigated TRPV4 expression in colon epithelium from patients with constipation. Methods Colon epithelial cell lines were co-cultured with various enterobacteria (bacterial components and supernatant), folate, LPS, or short chain fatty acids. TRPV4 expression levels and promoter DNA methylation were assessed using pyrosequencing, and microarray network analysis. For human samples, correlation coefficients were calculated and multiple regression analyses were used to examine the association between clinical background, rectal TRPV4 expression level and mucosa-associated microbiota. Results Co-culture of CCD841 cells with P. acnes, C. perfringens, or S. aureus transiently decreased TRPV4 expression but did not induce methylation. Co-culture with clinical isolates and standard strains of K. oxytoca, E. faecalis, or E. coli increased TRPV4 expression in CCD841 cells, and TRPV4 and TNF-alpha expression were increased by E. coli culture supernatants but not bacterial components. Although folate, LPS, IL-6, TNF-alpha, or SCFAs alone did not alter TRPV4 expression, TRPV4 expression following exposure to E. coli culture supernatants was inhibited by butyrate or TNF-alphaR1 inhibitor and increased by p38 inhibitor. Microarray network analysis showed activation of TNF-alpha, cytokines, and NOD signaling. TRPV4 expression was higher in constipated patients from the terminal ileum to the colorectum, and multiple regression analyses showed that low stool frequency, frequency of defecation aids, and duration were associated with TRPV4 expression. Meanwhile, incomplete defecation, time required to defecate, and number of defecation failures per 24 h were associated with increased E. faecalis frequency. Conclusions Colon epithelium cells had increased TRPV4 expression upon co-culture with K. oxytoca, E. faecalis, or E. coli supernatants, as well as TNFα-stimulated TNFαR1 expression via a pathway other than p38. Butyrate treatment suppressed this increase. Epithelial TRPV4 expression was increased in constipated patients, suggesting that TRPV4 together with increased frequency of E. faecalis may be involved in the pathogenesis of various constipation symptoms