Introduction Noise pollution in the operating rooms is one of the remaining challenges. Both patients and physicians are exposed to different sound levels during the operative cases, many of which can last for hours. This study aims to evaluate the noise pollution in the operating rooms during different surgical procedures. Materials and methods In this cross-sectional study, sound level in the operating rooms of Hamadan University-affiliated hospitals (totally 10) in Iran during different surgical procedures was measured using B&K sound meter. The gathered data were compared with national and international standards. Statistical analysis was performed using descriptive statistics and one-way ANOVA, t-test, and Pearson’s correlation test. Results Noise pollution level at majority of surgical procedures is higher than national and international documented standards. The highest level of noise pollution is related to orthopedic procedures, and the lowest one related to laparoscopic and heart surgery procedures. The highest and lowest registered sound level during the operation was 93 and 55 dB, respectively. Sound level generated by equipments (69 ± 4.1 dB), trolley movement (66 ± 2.3 dB), and personnel conversations (64 ± 3.9 dB) are the main sources of noise. Conclusion The noise pollution of operating rooms are higher than available standards. The procedure needs to be corrected for achieving the proper conditions.

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Background: Some parametric models are used to diagnose problems of lung segmentation more easily and effectively.Objective: The present study aims to detect lung diseases (nodules and tuberculosis) better using an active shape model (ASM) from chest radiographs. Material and Methods:In this analytical study, six grouping methods, including three primary methods such as physicians, Dice similarity, and correlation coefficients) and also three secondary methods using SVM (Support Vector Machine) were used to classify the chest radiographs regarding diaphragm congestion and heart reshaping. The most effective method, based on the evaluation of the results by a radiologist, was found and used as input data for segmenting the images by active shape model (ASM). Several segmentation parameters were evaluated to calculate the accuracy of segmentation. This work was conducted on JSRT (Japanese Society of Radiological Technology) database images and tuberculosis database images were used for validation. Results:The results indicated that the ASM can detect 94.12 ± 2.34 % and 94.38 ± 3.74 % (mean± standard deviation) of pulmonary nodules in left and right lungs, respectively, from the JRST radiology datasets. Furthermore, the ASM model detected 88.33 ± 6.72 % and 90.37 ± 5.48 % of tuberculosis in left and right lungs, respectively. Conclusion:The ASM segmentation method combined with pre-segmentation grouping can be used as a preliminary step to identify areas with tuberculosis or pulmonary nodules. In addition, this presented approach can be used to measure the size and dimensions of the heart in future studies.