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

Amrubicin Monotherapy for Patients with Platinum-Refractory Gastroenteropancreatic Neuroendocrine Carcinoma

Objective. Patients with gastroenteropancreatic neuroendocrine carcinoma (NEC) have a poor prognosis. Platinum-based combination chemotherapy is commonly used as first-line treatment; however, the role of salvage chemotherapy remains unknown. This study aimed to analyze the efficacy and safety of amrubicin monotherapy in patients with platinum-refractory gastroenteropancreatic NEC. Methods. Among 22 patients with advanced gastroenteropancreatic NEC, 10 received amrubicin monotherapy between September 2007 and May 2014 after failure of platinum-based chemotherapy. The efficacy and toxicity of the treatment were analyzed retrospectively. Results. Eight males and two females (median age, 67 years (range, 52–78)) received platinum-based chemotherapy, including cisplatin plus irinotecan (n=7, 70%), cisplatin plus etoposide (n=2, 20%), and carboplatin plus etoposide (n=1, 10%) before amrubicin therapy. Median progression-free survival and overall survival after amrubicin therapy were 2.6 and 5.0 months, respectively. Two patients had partial response (20% response rate), and their PFS were 6.2 months and 6.3 months, respectively. Furthermore, NEC with response for amrubicin had characteristics with a high Ki-67 index and receipt of prior chemotherapy with cisplatin and irinotecan. Grade 3-4 neutropenia and anemia were observed in four and five patients, respectively. Conclusion. Amrubicin monotherapy appears to be potentially active and well-tolerated for platinum-refractory gastroenteropancreatic NEC