Symptom and cost evaluation of ureteric stent extraction using strings versus flexible cystoscopy at a single high-volume centre

Background: This study evaluated ureteric stent symptoms using a modified ureteric stent symptom questionnaire (USSQ) for patients undergoing stent removal by either flexible cystoscopy, or manual, healthcare worker lead string extraction. Methods: Patients completed a stent symptom questionnaire derived from the USSQ. Mean data were compared between groups using paired Student’s t-tests. Data are presented as mean ± SD with associated p-values. A departmental cost analysis was undertaken. Results: Ninety patients completed questionnaires, 30 undergoing flexible cystoscopy and 60 string extraction. Six stents with strings were removed early (days 1–5, 6.67%, four female and two male). There were no adverse events recorded for this group. Men undergoing string extraction had significantly less pain (2.47 ± 1.88) compared to men undergoing flexible cystoscopic removal (5.67 ± 2.27, p < 0.001). There was no significant difference in pain scores for women undergoing string and flexible cystoscopic stent removal (3.56 ± 2.56 versus 3.66 ± 3.14, p > 0.90). The mean duration of stents left in situ was significantly less in the string group (10.1 ± 5.3 versus 26.5 ± 14.1 days, p < 0.001). Urinary tract infection rates did not differ between the string and the cystoscopy groups (3.7% ± 0.19 versus 2.78% ± 0.46, p > 0.90). The haematuria rates were 32.14% ± 0.47 and 66.67% ± 0.5, respectively ( p < 0.001). In our study, 60 flexible cystoscopies were avoided, saving over £67,000 at the institution within our study period. Conclusions: In our small study, stents removed with strings reduced pain scores in men, the duration of an indwelling ureteric stent, and cost to the health service. Level of evidence: 2b

Mathematical Morphology Based Image Segmentation and Character String Extraction Using Fuzzy Inference

Signs are ubiquitous indoors and outdoors, and they are often used for finding public places and other locations. However, information on signs is inaccessible to many visually impaired people, unless represented non-visually such as with Braille, tactile graphics, or speech. Automatically reading text from signs in natural scene images is a vital application for assisting visually impaired people. However, finding text in scene images is a great challenge because it cannot be assumed that the acquired image contains only characters. Natural scene images usually contain diverse text in different sizes, styles, fonts, and colors, and complex backgrounds. Therefore, we turn to the development of a portable camera-based assistive system to aid visually impaired people reading text from natural scenery. In this paper, a new method for character string extraction from scene images is discussed. The algorithm is implemented and evaluated using a set of natural scene images. Accuracy, precision, and recall rates of the proposed method are calculated and analyzed to determine success and limitations. Recommendations for improvements are given based on the results.