diagnostic radiology
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2022 ◽  
Vol 38 (3) ◽  
Author(s):  
Ayman S Alhasan ◽  
Shahad M Alahmadi ◽  
Yara A Altayeb ◽  
Tareef S Daqqaq

Objectives: The primary purpose of this study was to assess and report the perceived negative impact of long duty hours on education and personal well-being among medical trainees in the diagnostic radiology residency training program in Saudi Arabia. Methods: This cross-sectional study used a questionnaire (sent by email) with eight indicators related to the education and well-being of radiology residents in Saudi Arabia during the academic year 2019–2020. Participants were given a five-point Likert response format for each indicator. The relative importance index (RII) was calculated to rank the different indicators. Results: Our of 337 residents, 116 diagnostic radiology trainees completed the survey, with a response rate of 34.4%. A total of 102 (87.9%) indicated their preference for 16-hour shifts instead of the currently implemented 24-hour duty system. Using the RII, three items related to the post-duty day ranked at the top of the list. The negative impact on sleep rhythm during the post-call day ranked first (mean 4.23 ± 1.02, RII 0.84), followed by the impact on social life, family activities, and exercise during the post-call day (mean 4.09 ± 1.06, RII 0.81). The third highest ranking factor was missing academic activities on the post-call day (mean 3.91 ± 1.15, RII 0.78). There was no relationship between negative perception and gender (P > 0.05). Conclusion: The 24-hour duty system had a negative impact on radiology residents’ education and personal well-being, especially for items related to the post-call day. Reforming duty hours should be considered to promote residents’ well-being. doi: https://doi.org/10.12669/pjms.38.3.4440 How to cite this:Alhasan AS, Alahmadi SM, Altayeb YA, Daqqaq TS. Impact of long duty hours on education and well-being of diagnostic radiology residents: A national survey in Saudi Arabia. Pak J Med Sci. 2022;38(3):---------. doi: https://doi.org/10.12669/pjms.38.3.4440 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Author(s):  
Jarrel Seah ◽  
Tom Boeken ◽  
Marc Sapoval ◽  
Gerard S. Goh

AbstractMachine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.


2022 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Ahmed W. Moawad ◽  
David T. Fuentes ◽  
Mohamed G. ElBanan ◽  
Ahmed S. Shalaby ◽  
Jeffrey Guccione ◽  
...  

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Lingling Li ◽  
Yangyang Long ◽  
Bangtong Huang ◽  
Zihong Chen ◽  
Zheng Liu ◽  
...  

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (−3.08%) test AUC with parameters 1.2M (−73.73%) and FLOPs 3.1B (−80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (−6.79%) test AUC with parameters 0.5M (−88.46%) and FLOPs 0.66B (−95.88%).


2022 ◽  
pp. 517-533
Author(s):  
Haripriya Ramotar ◽  
Constantinos Tingerides
Keyword(s):  

2021 ◽  
Vol 32 (01) ◽  
pp. 19-21
Author(s):  
Mahboob Ahmed ◽  
Neelam Raheel ◽  
Saira Bilal ◽  
Nighat Haroon

Aim: The aim of the study is to evaluate the role of computed tomography in identifying the various pattern of pneumatization in the sphenoid sinuses.Knowledge  of pattern of pneumatization is essential for various trans-sphenoidal surgical procedures. Methodology: This is a retrospective study conducted at tertiary care hospital Lahore General Hospital Department of Diagnostic Radiology from the period of June 2020 to December 2020.The study consisted of a total of  80 patients from age group of 20 years to 70 years who were referred to the Department of Diagnostic Radiology for CT scan(PNS) .Patients of age less than 20 years , previous facial surgeries , trauma of skull base and having tumor of sphenoid sinuses were excluded. Sphenoid sinuses images were evaluated for pneumatization by posterior and anterior  extensions. RESULTS: The patients included were in the age range of 20-70 year with an average age of 43.5 year  in which 44 (55%) were male and 36(45) were female The pneumatization pattern observed in the sphenoid sinuses in descending order was as follows , post sellar prevalence was 75%, prevalence of  sellar was 10% and 2.5% was presellar. Conchal prevelance was observed to be 0%. Conclusion: Sphenoid  sinus anatomy review before trans-sphenoidal surgery is vital for safer endoscopic instrumentation of the patients . Keyword: Sphenoid sinus, pneumatization, cerebral fluid leak, endoscopy


2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Branislav Anđelković ◽  
Jonathan P. Elias

An anthropoid wooden coffin with human mummy was purchased in Luxor in February 1888 by the Serbian mécène and world traveler Pavle Riđički (1805‒1893). Due to historical, political and cultural circumstances the first studies of the mummy did not start until May 1993. The ancient ‘patient’ ‒ Nesmin, stolist-priest of Akhmim, son of Djedhor (son of Wennefer, son of Djedhor) born to Chay-Hathor-Imw/Tjay-Hathor-imw ‒ who became known as the Belgrade mummy ‒ underwent a CT scan at the University of Belgrade, Faculty of Dental Medicine, Diagnostic Radiology Center. The present paper provides the first complete analysis of the CT scan. At the time of death (350‒325 B.C.) Belgrade Nesmin was between 35 and 40 years old. A proper bioanthropological study is presented. The mummification features are discussed. The distribution of funerary amulets on the mummy has been established. The mummy’s cultural biography is specified. A museum superstition phenomenon is noted.


2021 ◽  
Vol 2090 (1) ◽  
pp. 012107
Author(s):  
A Konstantinidis ◽  
N Martini ◽  
V Koukou ◽  
G Fountos ◽  
N Kalyvas ◽  
...  

Abstract Characterization of digital X-ray imaging devices is very important because it can be used to measure and compare the performance of detectors used in Diagnostic Radiology. This characterization is usually made through the calculation of Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and Detective Quantum Efficiency (DQE). These parameters, especially the DQE, are very important because they quantify the effect of spatial resolution, contrast and noise on Radiographic image quality (IQ). The IEC 62220-1-1:2015 International Standard provides comprehensive guidelines how to capture and analyze X-ray images to characterize digital X-ray detectors. A novel, fast and free MATLAB-based software was developed, named RAD_IQ, to calculate the Signal Transfer Property (STP), perform Noise Component Analysis (NCA), and calculate the parameters MTF, NPS & DQE of X-ray detectors based on the novel IEC 62220-1-1:2015 International Standard for General Radiography and IEC 62220-1-1:2007 for Digital Mammography. Our results were validated against well-established software products used for quantitative image analysis of digital X-ray detectors. The calculated parameters were within 5% difference compared to available software products. The conclusion of our study was that RAD_IQ can be easily used from Medical Physicists, Biomedical Engineers and researchers without any programming experience to characterize the performance of digital X-ray detectors used in Diagnostic Radiology.


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