Background/AimsHypnotherapy is considered as a promising intervention for irritable bowel syndrome (IBS), but the evidence is still limited. The aims of this study were to conduct a systematic review and meta-analysis to estimate the efficacy of hypnotherapy for the treatment of IBS.
MethodsA literature search was performed using MEDLINE (PubMed), Embase, PsycINFO and the Cochrane Central Register of Controlled Trials (CENTRAL database). Only randomized controlled trials that compared hypnotherapy with any other conventional treatment or no treatment in patients with IBS were included. Studies had to report outcomes as IBS symptom score or quality of life. The mean change in outcome score was used to pool these outcomes for the meta-analysis. Data were synthesized using the standardized mean difference for continuous data.
ResultsSeven randomized controlled trials (6 papers) involving 374 patients with IBS were identified. Performance bias was high in all trials because it was impossible to blind participants and therapists in this type of intervention. The outcomes in this meta-analysis were evaluated at 3 months for short-term effects and at 1 year for long-term effects. The change in abdominal pain score at 3 months was significant in the hypnotherapy group (standardized mean difference, -0.83; 95% CI, -1.65 to -0.01). Three of the 4 trials showed greater improvement in overall gastrointestinal symptoms in the hypnotherapy group.
ConclusionsThis study provides clearer evidence that hypnotherapy has beneficial short-term effects in improving gastrointestinal symptoms of patients with IBS.
Background: Although great advances in artificial intelligence for interpreting small bowel capsule endoscopy (SBCE) images have been made in recent years, its practical use is still limited. The aim of this study was to develop a more practical convolutional neural network (CNN) algorithm for the automatic detection of various small bowel lesions.Methods: A total of 7556 images were collected for the training dataset from 526 SBCE videos. Abnormal images were classified into two categories: hemorrhagic lesions (red spot/ angioectasia/active bleeding) and ulcerative lesions (erosion/ ulcer/stricture). A CNN algorithm based on VGGNet was trained in two different ways: the combined model (hemorrhagic and ulcerative lesions trained separately) and the binary model (all abnormal images trained without discrimination). The detected lesions were visualized using a gradient class activation map (Grad-CAM). The two models were validated using 5,760 independent images taken at two other academic hospitals.Results: Both the combined and binary models acquired high accuracy for lesion detection, and the difference between the two models was not significant (96.83% vs 96.62%, P = 0.122). However, the combined model showed higher sensitivity (97.61% vs 95.07%, P < 0.001) and higher accuracy for individual lesions from the hemorrhagic and ulcerative categories than the binary model. The combined model also revealed more accurate localization of the culprit area on images evaluated by the Grad-CAM.Conclusions: Diagnostic sensitivity and classification of small bowel lesions using a convolutional neural network are improved by the independent training for hemorrhagic and ulcerative lesions. Grad-CAM is highly effective in localizing the lesions.
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