The new device enabled safe, simple, and time-saving, single-stage surgical management of left colon cancer obstruction. The ability to perform on-table colonoscopy enabled treatment and recognition of synchronous bowel pathology.
PurposeBowel preparation with sodium phosphate was recently prohibited by the U.S. Food and Drug Administration. Polyethylene glycol (PEG) is safe and effective; however, it is difficult to drink. To identify an easy bowel preparation method for colonoscopy, we evaluated three different bowel preparation regimens regarding their efficacy and patient satisfaction.MethodsIn this randomized, comparative study, 892 patients who visited a secondary referral hospital for a colonoscopy between November 2012 and February 2013 were enrolled. Three regimens were evaluated: three packets of sodium picosulfate/magnesium citrate (PICO, group A), two packets of PICO with 1 L of PEG (PICO + PEG 1 L, group B), and two packets of PICO with 2 L of PEG (PICO + PEG 2 L, group C). A questionnaire survey regarding the patients' preference for the bowel preparation regimen and satisfaction was conducted before the colonoscopies. The quality of bowel cleansing was scored by the colonoscopists who used the Aronchick scoring scale and the Ottawa scale.ResultsThe patients' satisfaction rate regarding the regimens were 72% in group A, 64% in group B, and 45.9% in group C. Nausea and abdominal bloating caused by the regimens were more frequent in group C than in group A or group B (P < 0.01). Group C showed the lowest preference rate compared to the other groups (P < 0.01). Group C showed better right colon cleansing efficacy than group A or group B.ConclusionGroup A exhibited a better result than group B or group C in patient satisfaction and preference. In the cleansing quality, no difference was noted between groups A and C.
Hyperspectral cameras acquire reflectance values at many different wavelength bands. Dimensions tend to increase because spectral information is stored in each pixel. Several attempts have been made to reduce dimensional problems such as the feature selection using Adaboost and dimension reduction using the Simulated Annealing technique. We propose a novel material detection method that consists of four steps: feature band selection, feature extraction, SVM (Support Vector Machine) learning, and target and specific region detection. It is a combination of the band ratio method and Simulated Annealing algorithm based on detection rate. The experimental results validate the effectiveness of the proposed feature selection and band ratio method.
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