imaging diagnosis
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2022 ◽  
Vol 17 (2) ◽  
pp. 404-411
Author(s):  
Shabnam Bhandari Grover ◽  
Sonali Malhotra ◽  
Saurabh Pandey ◽  
Hemal Grover ◽  
Ravi Kale ◽  
...  

2022 ◽  
Vol 4 (1) ◽  
pp. 01-05
Author(s):  
Alejandro Alvarez López

Background: gonarthrosis is a common entity characterized by involvement of one or more compartments, of which the lateral one is the one with the lowest incidence in isolation. Aim: the aims of this research are too updated on the most important features on lateral knee osteoarthritis and look for updated bibliography on the subject. Methods: PubMed, Hinari, SciELO and Medline databases were searched for citations from August 1st 2021 to September 30th 2021 using the EndNote search manager and reference manager. Out of 312 articles, 44 selected citations were used in this review, being 42 of the last five years. Results: the main causes of lateral knee osteoarthritis are mentioned, especially the secondary ones. Reference is made to the main clinical and imaging elements for diagnosis based on plain radiography and magnetic resonance imaging. Both conservative and surgical treatment modalities are exposed, in the latter the main indications and complications are described, among which osteotomies and arthroplasties stand out. Conclusions: lateral gonarthrosis is the least common of the unicompartmental gonarthrosis that affect the knee joint. Clinical and imaging diagnosis provides the essential elements for both conservative and surgical therapeutic behaviour, the latter modality includes techniques that preserve the joint such as osteotomies and others that do not, such as arthroplasties.


2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tao Wang ◽  
Yizhu Chen ◽  
Hangxiang Du ◽  
Yongan Liu ◽  
Lidi Zhang ◽  
...  

This study was aimed at exploring the application value of transcranial Doppler (TCD) based on artificial intelligence algorithm in monitoring the neuroendocrine changes in patients with severe head injury in the acute phase; 80 patients with severe brain injury were included in this study as the study subjects, and they were randomly divided into the control group (conventional TCD) and the experimental group (algorithm-optimized TCD), 40 patients in each group. An artificial intelligence neighborhood segmentation algorithm for TCD images was designed to comprehensively evaluate the application value of this algorithm by measuring the TCD image area segmentation error and running time of this algorithm. In addition, the Glasgow coma scale (GCS) and each neuroendocrine hormone level were used to assess the neuroendocrine status of the patients. The results showed that the running time of the artificial intelligence neighborhood segmentation algorithm for TCD was 3.14 ± 1.02   s , which was significantly shorter than 32.23 ± 9.56   s of traditional convolutional neural network (CNN) algorithms ( P < 0.05 ). The false rejection rate (FRR) of TCD image area segmentation of this algorithm was significantly reduced, and the false acceptance rate (FAR) and true acceptance rate (TAR) were significantly increased ( P < 0.05 ). The consistent rate of the GCS score and Doppler ultrasound imaging diagnosis results in the experimental group was 93.8%, which was significantly higher than the 80.3% in the control group ( P < 0.05 ). The consistency rate of Doppler ultrasound imaging diagnosis results of patients in the experimental group with abnormal levels of follicle stimulating hormone (FSH), prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) was significantly higher than that of the control group ( P < 0.05 ). In summary, the artificial intelligence neighborhood segmentation algorithm can significantly shorten the processing time of the TCD image and reduce the segmentation error of the image area, which significantly improves the monitoring level of TCD for patients with severe craniocerebral injury and has good clinical application value.


Author(s):  
Caroline De Medeiros ◽  
Jaime Miranda Júnior ◽  
Denise Elvira Pires de Pires ◽  
José Leomar Todesco  ◽  
João Artur de Souza
Keyword(s):  

Este estudo apresenta os resultados de uma pesquisa aplicada que teve a finalidade de identificar e analisar o que a comunidade científica mundial está publicando sobre os temas radiologia e diagnóstico por imagem no período da pandemia causada pelo vírus da COVID-19, a pandemia causada pelo novo coronavírus colocou em evidência o papel fundamental da radiologia e diagnóstico por imagem, e a importância que os profissionais da saúde desempenham na sociedade. No estudo foram analisados 568 estudos extraídos na base de dados Scopus com os descritores "radiology and imaging diagnosis" e "COVID-19". A análise foi realizada por meio da ferramenta Orange Canvas mediante a técnica de clusterização. Como resultado, foi descoberto que os estudos estão direcionados para protocolos de radiologia para diagnóstico, gestão e processos básicos de trabalho da radiologia em suas atividades diárias. Um mapa mental foi proposto, a fim de sintetizar o agrupamento das descobertas dos estudos e também nesta síntese as evidências da falta de preocupação com a saúde do trabalhador de radiologia no momento da pandemia.


2021 ◽  
Vol 11 (12) ◽  
pp. 1317
Author(s):  
Andrea Sambri ◽  
Paolo Spinnato ◽  
Sara Tedeschi ◽  
Eleonora Zamparini ◽  
Michele Fiore ◽  
...  

Imaging is needed for the diagnosis of bone and joint infections, determining the severity and extent of disease, planning biopsy, and monitoring the response to treatment. Some radiological features are pathognomonic of bone and joint infections for each modality used. However, imaging diagnosis of these infections is challenging because of several overlaps with non-infectious etiologies. Interventional radiology is generally needed to verify the diagnosis and to identify the microorganism involved in the infectious process through imaging-guided biopsy. This narrative review aims to summarize the radiological features of the commonest orthopedic infections, the indications and the limits of different modalities in the diagnostic strategy as well as to outline recent findings that may facilitate diagnosis.


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