Background: Detection of retained foreign bodies remains a significant problem in the emergency department. Foreign bodies can go undetected causing infectious complications ultrasonography is too inaccessible and expensive. The purpose of this study is comparison of ultrasonography with radiography for the detection of cervical esophageal foreign bodies
Methods: This cross-sectional study evaluated 58 patients referred with suspected upper esophageal foreign body in the Emergency Department, Amir Alam. Patients were evaluated with ultrasonography and x-ray. After surgical exploration, different type of foreign bodies were recorded. The SPSS statistical software was used for analysis. For applicable efficacy outcome measures, a Spearman correlation was used. Differences were significant when P<0.05. All values were expressed as the frequency and present.
Results: Fifty eight patients were studied. 25 patients (43.4%) were male and 31 patients (56.9%) were female, in 28 (48.2%) patients foreign bodies were detected in radiography. 30 patients (51.8%) were not recorded in techniqe. It was found in patients 22 (78.6%) organic body, and six cases (21.4%) non-organic body. radiographic outcomes in patients with foreign bodies were positive in 26 patients (92.9%) and in two patients (7.1%) were negative. Ultrasound results were positive in 27 patients (96.4%) and in one patient (3.6%) were negative. Association of ultrasound and radiography results were significant in patients with foreign body (Spearman correlation=0.896, P=0.001 Kappa=0.890).
Conclusion: These reports suggest that result of ultrasound with radiography for the detection foreign bodies in cervical esophagus have good agreement. The use of ultrasonography in the emergency department to detect and eventually remove foreign bodies by emergency physicians is an important issue because there is not always an ultrasound technologist or radiologist available.
Discovery of x-ray and using of it for medical imaging have produced tremendous outcomes for diagnosis and treatment of diseases. More than 10 million diagnostic radiological procedures and 100,000 nuclear medicine exams are being performed daily around the world. According to the national commission on radiological protection and measurements (NCRP)-report 160, medical x-ray is contribute to approximately 95% of all radiological examinations that is responsible for 74% of the collective dose to the US population. Despite of unique benefits of ionizing radiations, in the field of radiation protection, they are associated with potential risks such as cancer and genetically abnormalities. The cancer risk attributable to diagnostic radiology is estimated about 0.6% to 3%. It is estimated that the radiation dose from diagnostic x-ray procedures are annually responsible for 7,587 and 5,695 cases of radiation induced cancer in the population of Japan and US, respectively. Although the radiation dose associated with most radiological procedures are very low, but rapid increasing use of radiography procedures during two past decades have been concerned due to the cancer risk associated with ionizing radiations. On the base of linear no-threshold (LNT) model of dose-response curve, any level of exposure is dangerous. Deoxyribonucleic acid (DNA) is the main target of ionizing radiation. For radiological exposure with low dose, the stochastic effects such as genetic damages and leukemia are concerned. According to the recommendations of the radiation protection regulatory organizations, radiological procedure must be done with respect to social and economic factors in which exposure of patient and population kept as low as reasonable and achievable. Hence, prescription of a radiological test is acceptable only when its advantages are higher than its damages. Optimizing the different parameters such as: collimating the primary beam field to the area of diagnostic interest, exposure conditions (high kVp and low mAs), projections, exposure time and shielding can reduce the patients' exposure besides the saving of image quality. Following the radiation protection guidelines can considerably decrease the exposure risks.
Background: Anode heel effect refers to reduction of radiation intensity in the anode side of X-ray tube. This variation in radiation intensity across the anode-cathode of X-ray tube can be benefited for decrease radiation exposure in some radiological examinations. The aim of this study was to evaluate the effect of anode heel orientation on the radiation dose received by the testes in male patients undergoing pelvic radiography.
Methods: This is a cross-sectional study, conducted at one of the teaching hospitals of Ahvaz, Jundishapur University of Medical Science Ahvaz, Iran, from September 2015 to March 2016. In order to measure the profile of radiation intensity variation, 13 paired sets of high radiosensitive cylindrical lithium fluoride thermo-luminescent dosimeters (TLD) aligned on the cathode-anode central axis upon the table and then irradiated using routine exposure parameters. The anode of X-ray tube was positioned toward the feet for 40 patients and toward the head for 39 patients undergoing pelvic radiography. For measure the entrance skin dose (ESD), 8 TLD chips were located on the central point of the radiation field and 5 TLDs were located on the testes position to measure the dose received.
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Results: Radiation intensity profile showed that radiation intensity decrease from the cathode to the anode side. Discrepancy of radiation intensity on central axis of cathode-anode was calculated about 35%. The radiation dose received by the testes was 26.74% lower for patients the anode directed toward the feet, compared to the patients in which the anode directed toward the head (FTC: 1.260±0.296 mGy, FTA: 0.923±0.167 mGy, P<0.05). There was no meaningful difference for the measured ESD of pelvis between two groups of patients (FTC: 1.256±0.315 mGy, FTA: 1.195±0.205 mGy, P=0.788). Conclusion: In pelvic radiography, positioning of testes directed to the anode of X-ray tube can decrease the receive dose. |
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With the rapid expansion of artificial intelligence across clinical disciplines, a variety of artificial neural networks (ANNs) have become indispensable tools for endowing computer systems with advanced analytical power. Dentistry, as an information‑rich branch of medicine, routinely generates and must interpret large, complex datasets from imaging and diagnostic records. Consequently, researchers have increasingly directed their attention toward intelligent, automated techniques for analyzing dental data. This study therefore surveys and synthesizes the methods that have been applied to the intelligent and automated analysis of such data, highlighting the prevailing trends in current literature.The majority of the examined investigations relied on panoramic radiographic images of the teeth orthopantomograms (OPG) as their primary source material. Three overarching technical objectives repeatedly emerged: first, tooth diagnosis, meaning the reliable separation and identification of each individual tooth from its neighbors; second, sample segmentation, that is, the piece‑by‑piece analysis of visual information within the image; and third, semantic segmentation, namely, the contextual interpretation of information extracted from the radiograph. Depending upon which of these objectives was pursued, researchers selected different neural‑network architectures and configurations. Across the reviewed corpus, input images were typically subjected to preprocessing steps such as normalization, noise reduction, and contrast enhancement before being supplied to a neural network for training, thereby preparing the data for subsequent machine interpretation. In several instances, the raw output produced by the neural network underwent additional post‑processing, a stage designed to refine the preliminary results and enhance overall accuracy. The comparative analysis presented here concentrates on how effectively the various neural‑network models fulfilled the three technical objectives described above. The surveyed articles reveal two dominant analytical approaches. In the intelligent problem‑solving paradigm, convolutional neural networks (CNNs) overwhelmingly predominate. Conversely, in the automated paradigm, investigators favor classical, non‑learning algorithmic techniques. Work employing ANNs consistently emphasizes image comprehension, segmentation, feature extraction, feature classification, network modeling, and careful variable tuning to promote effective learning that aligns with each study’s stated objectives.
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