Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 22.166
Filtrar
2.
Radiologe ; 62(5): 451-462, 2022 May.
Artigo em Alemão | MEDLINE | ID: mdl-35501563
3.
Radiologia (Engl Ed) ; 64(2): 101-102, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35504674
4.
Radiologia (Engl Ed) ; 64(2): 169-178, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35504683

RESUMO

Mónico Sánchez Moreno (1880-1961) was an important figure in the early years of electromedicine, rubbing elbows with world-class physicists like Nikola Tesla. Her main contribution to the field was the invention and commercialization of a portable X-ray generator, replacing the heavy transformer had been necessary to generate power with a lightweight portable device that could work with direct or alternating current at 220 or 125V. This device was easily adaptable to other applications in electromedicine, such as cauterization or disinfection. This indefatigable entrepreneur could have triumphed in America, but preferred to work toward furthering technological development in the land that she loved. Her efforts made it possible to have an affordable device made in Spain that would allow radiological examinations to be done in places where it would have been otherwise unthinkable. In conclusion, Mónico Sánchez Moreno was a self-made woman who deserves to be remembered for her pioneering role in portable radiology.


Assuntos
Radiologia , Tecnologia Radiológica , Feminino , Humanos , Espanha
6.
Rofo ; 194(5): 564, 2022 05.
Artigo em Alemão | MEDLINE | ID: mdl-35508159
8.
Artif Intell Med ; 128: 102281, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35534140

RESUMO

Proximal femur fractures represent a major health concern, and substantially contribute to the morbidity of elderly. Correct classification and diagnosis of hip fractures has a significant impact on mortality, costs and hospital stay. In this paper, we present a method and empirical validation for automatic subclassification of proximal femur fractures and Dutch radiological report generation that does not rely on manually curated data. The fracture classification model was trained on 11,000 X-ray images obtained from 5000 electronic health records in a general hospital. To generate the Dutch reports, we first trained an embedding model on 20,000 radiological reports of pelvic region fractures, and used its embeddings in the report generation model. We trained the report generation model on the 5000 radiological reports associated with the fracture cases. Our report generation model is on par with state-of-the-art in terms of BLEU and ROUGE scores. This is promising, because in contrast to those earlier works, our approach does not require manual preprocessing of either images or the reports. This boosts the applicability of automatic clinical report generation in practice. A quantitative and qualitative user study among medical students found no significant difference in provenance of real and generated reports. A qualitative, in-depth clinical relevance study with medical domain experts showed that from a human perspective the quality of the generated reports approximates the quality of the original reports and highlights challenges in creating sufficiently detailed and versatile training data for automatic radiology report generation.


Assuntos
Fraturas do Quadril , Radiologia , Idoso , Fêmur , Fraturas do Quadril/diagnóstico por imagem , Humanos , Idioma , Radiografia
9.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao ; 44(2): 324-331, 2022 Apr.
Artigo em Chinês | MEDLINE | ID: mdl-35538770

RESUMO

As the detection rate of pancreatic cystic neoplasms (PCN) increases,recommendations or guidelines for the diagnosis and treatment of PCN have been released from professional organizations.From the perspective of radiology,we compared seven guidelines in terms of general introduction,preoperative monitoring methods and strategies,stratification of risk factors,surgical indications,and postoperative follow-ups,aiming to provide references for the evaluation of images and the formulation of individualized approach for the treatment of PCN.


Assuntos
Cisto Pancreático , Neoplasias Pancreáticas , Radiologia , Humanos , Cisto Pancreático/diagnóstico por imagem , Cisto Pancreático/terapia , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/terapia , Carbonitrila de Pregnenolona , Radiografia
11.
Semin Roentgenol ; 57(2): 168-171, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35523531
12.
Comput Intell Neurosci ; 2022: 5667264, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35602611

RESUMO

Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process's 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.


Assuntos
Neoplasias da Mama , Radiologia , Algoritmos , Inteligência Artificial , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Aprendizado de Máquina , Mamografia/métodos
14.
Radiologia (Engl Ed) ; 64 Suppl 1: 4-10, 2022 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-35428466

RESUMO

OBJECTIVE: To evaluate radiology residents' opinions about breast imaging and the possibility of choosing this subspecialty after completing their residency. MATERIAL AND METHODS: We elaborated a 15-question survey aimed at radiology residents in Spain. The survey was approved by the Spanish Society of Breast Imaging (SEDIM) and the Spanish Society of Medical Radiology (SERAM), and it was disseminated by the SERAM through links to Google Forms via social networks and emails. Responses sent between February 21, 2020 and July 31, 2020 were accepted. RESULTS: A total of 72 residents responded to the survey (7.83% response rate); 69.44% of these were third- or fourth-year residents. Of the respondents, 73.61% knew about the SEDIM, and 18.06% knew about the European Society of Breast Imaging. The duration of training programs was three months for 70.83% of respondents. In 7.84% of the responses, residents stated that their supervision was less than 50%, and 70.59% of the residents stated that the rotation exceeded their expectations. One-third of the respondents would consider a fellowship in breast imaging. In all hospitals, residents did diagnostic mammography and breast ultrasound; not all did interventional procedures. Aspects of breast imaging that were rated negatively included the lack of CT studies and the possible legal repercussions of errors. Aspects that were rated positively were dynamics, interventionism, and the role of the radiologist in the process of care for patients with breast cancer. CONCLUSIONS: Most residents considered that their rotations in breast imaging exceeded their expectations; however, only a small percentage of residents would consider specializing in the field.


Assuntos
Internato e Residência , Radiologia , Bolsas de Estudo , Humanos , Mamografia , Radiologia/educação , Inquéritos e Questionários
15.
Arch Iran Med ; 25(3): 196-200, 2022 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-35429964

RESUMO

Abdulkarim Vessal, a distinguished professor of the Shiraz School of Medicine, was the founder of the "Archives of Iranian Medicine Journal" and a permanent member of the "Iranian Academy of Medical Sciences", who finally, after five decades of efforts to promote radiology and medical journalism in Iran, passed away on February 18, 2022 in Shiraz. His demise is a great loss for the Iranian medical community, especially in Shiraz. In the present paper, his life and career are briefly reviewed.


Assuntos
Radiologia , Academias e Institutos , História do Século XX , Humanos , Irã (Geográfico)
17.
BMJ Open ; 12(4): e059216, 2022 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-35393330

RESUMO

OBJECTIVES: We assessed the effect of gender, rank and research productivity on compensation for faculty at academic medical centres. DESIGN: A web-based retrospective review of salary for professors in 2016. SETTING: Faculty from six state-run, publicly funded academic medical centres in the Western USA. PARTICIPANTS: 799 faculty members, 225 assistant (51% women), 200 associate (40% women) and 374 full professors (32% women) from general surgery (26% women), obstetrics and gynaecology (70% women) and radiology (34% women). METHODS: Archived online faculty profiles were reviewed for gender, rank and compensation (total, baseline and supplemental). Total compensation was defined as baseline compensation plus supplemental income. Baseline compensation was defined as base salary minus reductions due to participation in the voluntary Employee Reduction in Time and phased retirement programmes. Supplemental income was defined as additional salary for clinical care and research (eg, grants). Elsevier's Scopus was used to collect data on h-index, a measure of research productivity. Linear regression models were estimated to determine the relationship between these factors and salary. RESULTS: Total compensation was significantly higher for men across all professorial ranks in both general surgery [Formula: see text] and obstetrics and gynaecology [Formula: see text]. Women faculty members within these departments earned almost US$75 000 less than their men colleagues. The disparity in salary originates from gaps in supplemental income, as baseline compensation was not significantly different between men and women. No significant gender difference in total compensation for radiology was found [Formula: see text]. Higher h-index was associated with higher baseline compensation across all departments as well as with supplemental income for general surgery. Higher h-index was related to lower supplemental income for radiology and was not related to supplemental income for obstetrics and gynaecology. CONCLUSIONS: Further investigations should focus on discrepancies in supplemental income, which may preferentially benefit men.


Assuntos
Radiologia , Salários e Benefícios , Centros Médicos Acadêmicos , Docentes de Medicina , Feminino , Humanos , Masculino , Estudos Retrospectivos , Fatores Sexuais , Estados Unidos
18.
PLoS One ; 17(4): e0265873, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35377901

RESUMO

The 2019 novel coronavirus pandemic has not only created massive public health issues, it has also produced excessive psychological disorders in healthcare professionals, including radiology staff. The aim of this study is to assess the risk perception and mental health of radiology staff in Saudi Arabia during the COVID-19 pandemic. The researcher asked radiology staff to complete an online Google Forms questionnaire, between June 10, 2020 and June 17, 2020, which contained demographic data and self-designed questions related to anxiety, insomnia, depressive symptoms, and mental health services during the pandemic. A total of 168 radiology staff participated in the study. The results indicated that 53.05% and 57.14% of the participants were experiencing mild to severe symptoms of anxiety and depression, respectively. Moreover, 47.02% of the participants were experiencing insomnia symptoms. Among all the participants, only 16.61% had received psychological materials from their hospital during the pandemic, while 22.02% had accessed online psychological assistance techniques. The health of roughly one-third (30.95%) of the participants was worse than it had been before the pandemic. COVID-19 is a source of mental health disorders for healthcare professionals, particularly radiology staff. The findings of this study indicate that more than 70% of radiology staff in Saudi Arabia are concerned about insufficient protective measures and the risk of infection. In addition, a large percentage of them have experienced mental health disorders, such as anxiety, insomnia, and depression. Regular mental healthcare services are required to decrease the negative impact of the pandemic and enhance the overall mental health of the radiology staff.


Assuntos
COVID-19 , Radiologia , Distúrbios do Início e da Manutenção do Sono , Ansiedade/epidemiologia , Ansiedade/psicologia , COVID-19/epidemiologia , Estudos Transversais , Depressão/epidemiologia , Depressão/psicologia , Humanos , Internet , Saúde Mental , Pandemias , SARS-CoV-2 , Arábia Saudita/epidemiologia , Distúrbios do Início e da Manutenção do Sono/epidemiologia
19.
Acad Radiol ; 29(5): 763-770, 2022 May.
Artigo em Inglês | MEDLINE | ID: mdl-35379477

RESUMO

RATIONALE AND OBJECTIVES: Our goal was to create an artificial intelligence (AI) training curriculum for residents that taught them to create, train, evaluate and refine deep learning (DL) models. Hands-on training of models was emphasized and didactic presentations of the mathematical and programmatic underpinnings of DL were minimized. MATERIALS AND METHODS: We created a three-session, 6-hour curriculum based on a "no-code" machine learning system called Lobe.ai. This class met weekly in June 2021. Pre-class homework included reading assignments, software installation, dataset downloads, and image-collection and labeling. The class sessions included several short, didactic presentations, but were largely devoted to hands-on training of DL models. After the course, our residents completed a short, anonymous, online survey about the course. RESULTS: Our residents learned to acquire and label a wide variety of image datasets. They quickly learned to train DL models to classify these datasets, as well as how to evaluate and refine these models. Our survey showed that most residents felt AI to be important and worth learning, but most were not very interested in learning to program. Most felt that the course taught them useful things about DL, and they were now more interested in the topic. Most would recommend the course to other residents, as well as to medical students and to radiology faculty. CONCLUSION: The course met our objectives of teaching our residents to create, train, evaluate, and refine DL models. We hope that the hands-on experience they gained in this course will enable them to recognize problems in diagnostic AI systems, and to help solve such problems in their own radiology practices.


Assuntos
Aprendizado Profundo , Internato e Residência , Radiologia , Inteligência Artificial , Automóveis , Currículo , Humanos , Radiologia/educação
20.
Clin Imaging ; 86: 83-88, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35367867

RESUMO

PURPOSE: To assess radiology representation, multimedia content, and multilingual content of United States lung cancer screening (LCS) program websites. MATERIALS AND METHODS: We identified the websites of US LCS programs with the Google internet search engine using the search terms lung cancer screening, low-dose CT screening, and lung screening. We used a standardized checklist to assess and collect specific content, including information regarding LCS staff composition and references to radiologists and radiology. We also tabulated types and frequencies of included multimedia and multilingual content and patient narratives. RESULTS: We analyzed 257 unique websites. Of these, only 48% (124 of 257) referred to radiologists or radiology in text, images, or videos. Radiologists were featured in images or videos on only 14% (36 of 257) of websites. Radiologists were most frequently acknowledged for their roles in reading or interpreting imaging studies (35% [90 of 574]). Regarding multimedia content, only 36% (92 of 257) of websites had 1 image, 27% (70 of 257) included 2 or more images, and 26% (68 of 257) of websites included one or more videos. Only 3% (7 of 257) of websites included information in a language other than English. Patient narratives were found on only 15% (39 of 257) of websites. CONCLUSIONS: The field of Radiology is mentioned in text, images, or videos by less than half of LCS program websites. Most websites make only minimal use of multimedia content such as images, videos, and patient narratives. Few websites provide LCS information in languages other than English, potentially limiting accessibility to diverse populations.


Assuntos
Neoplasias Pulmonares , Radiologia , Detecção Precoce de Câncer , Humanos , Internet , Neoplasias Pulmonares/diagnóstico por imagem , Multimídia , Ferramenta de Busca , Estados Unidos
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...