RESUMEN

The use of radiological images is widespread in the emergency department (ED) as physicians commonly rely on them during initial evaluations to confirm diagnoses, contributing to prolonged waiting times. This study aimed to determine the relationship between commonly gathered triage data and the need for radiological imaging. Data were collected from electronic charts that contained routinely collected hospital data at the time of triage in the King Abdulaziz Medical City (KAMC) in Riyadh ED. The binary logistic regression results demonstrated a statistically significant relationship between age and radiological imaging ordered in the ED. Each one-unit increase in age corresponded to a 0.983-fold increase in the likelihood of ordering radiological imaging (odds ratio: 0.983, 95% confidence interval: 0.972-0.995, p = 0.004). In contrast, hypertension, diabetes, and heart failure were independent predictors of the need for radiological imaging in the ED (p >0.05). Patient data that are immediately available during ED triage can be used to predict the need for radiological imaging during ED visits. Such models can identify patients who may require radiological imaging during ED visits and expedite patient disposition.

RESUMEN

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.

RESUMEN

COVID-19 causes an ongoing worldwide pandemic situation. The non-discovery of specialized drugs and/or any other kind of medicines makes the situation worse. Early diagnosis of this disease will be certainly helpful to start the treatment early and also to bring down the dire spread of this highly infectious virus. This article describes the proposed novel unsupervised segmentation method to segment the radiological image samples of the chest area that are accumulated from the COVID-19 infected patients. The proposed approach is helpful for physicians, medical technologists, and other related experts in the quick and early diagnosis of COVID-19 infection. The proposed approach will be the SUFEMO (SUperpixel based Fuzzy Electromagnetism-like Optimization). This approach is developed depending on some well-known theories like the Electromagnetism-like optimization algorithm, the type-2 fuzzy logic, and the superpixels. The proposed approach brings down the processing burden that is required to deal with a considerably large amount of spatial information by assimilating the notion of the superpixel. In this work, the EMO approach is modified by utilizing the type 2 fuzzy framework. The EMO approach updates the cluster centers without using the cluster center updation equation. This approach is independent of the choice of the initial cluster centers. To decrease the related computational overhead of handling a lot of spatial data, a novel superpixel-based approach is proposed in which the noise-sensitiveness of the watershed-based superpixel formation approach is dealt with by computing the nearby minima from the gradient image. Also, to take advantage of the superpixels, the fuzzy objective function is modified. The proposed approach was evaluated using both qualitatively and quantitatively using 310 chest CT scan images that are gathered from various sources. Four standard cluster validity indices are taken into consideration to quantify the results. It is observed that the proposed approach gives better performance compared to some of the state-of-the-art approaches in terms of both qualitative and quantitative outcomes. On average, the proposed approach attains Davies-Bouldin index value of 1.812008792, Xie-Beni index value of 1.683281, Dunn index value 2.588595748, and ß index value 3.142069236 for 5 clusters. Apart from this, the proposed approach is also found to be superior with regard to the rate of convergence. Rigorous experiments prove the effectiveness of the proposed approach and establish the real-life applicability of the proposed method for the initial filtering of the COVID-19 patients.

RESUMEN

Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach is based on artificial cell swarm optimization and will be known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented in the Matlab environment. The proposed approach uses a novel superpixel computation method which is helpful to effectively represent the pixel intensity information which is beneficial for the optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm optimization approach. So, a twofold contribution can be observed in this work which is helpful to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical impact of this work. Both qualitative and quantitative experimental results show the effectiveness of the proposed approach and also establish it as an effective computer-aided tool to fight against the COVID-19 virus. Four well-known cluster validity measures Davies-Bouldin, Dunn, Xie-Beni, and ß index are used to quantify the segmented results and it is observed that the proposed approach not only performs well but also outperforms some of the standard approaches. On average, the proposed approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie-Beni index for 3, 5,7, and 9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a contribution to the community.

RESUMEN

Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients' chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models' generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.

RESUMEN

Many reports suggest the SARS-CoV-2 infection may result in neurological complications. A wide spectrum of clinical syndromes have been reported, including both central and peripheral nervous system. Such symptoms may be a consequence of a direct viral injury, secondary to systemic inflammatory response, autoimmune processes, ischemic lesions or combination of these. Anosmia and dysgeusia are highly prevalent in the early stage of infection. Cerebrovascular events in patients with COVID-19 have also been documented with increasing frequency. Some cases of parainfectious autoimmune neurologic manifestations concurrent with active SARS-CoV-2 infection have been described, including hemorrhagic necrotizing encephalopathy, Guillain-Barré and Miller-Fisher syndromes. There are also a few reports documenting encephalitis and acute demyelinating encephalomyelitis (ADEM) in the course of COVID-19. There is also a growing number of cases of patients after recovery from COVID-19 with psychosomatic disorders, manifesting with memory disfunction, cognitive functions disorders, depression or other affective disorders, which may lead to a decrease of intellectual functions. Many of these neurological manifestations of the infection are possible to distinguish using radiological imaging techniques. It plays a very important role in evaluating the course of COVID-19 as well as diagnosing respiratory complications and choosing a proper management of infected patients. Similarly, radiological techniques play crucial role in identifying the cause of neurological symptoms connected to SARS-CoV-2 infection, being one of the most important elements of diagnostics. Especially in case of the presence of nervous system implication, using radiological imaging techniques to monitor the emerging onset of various symptoms is crucial to assess the severity and scope of involvement. Quick diagnostic process and identifying complications as fast as possible in order to implement specific treatment can be crucial to avoid long-term secondary conditions and accelerate the recovery period. In this review, we present the most important neurological complications that may occur in the course of SARS-CoV-2 infection and summarize their radiological manifestations.

RESUMEN

The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.

RESUMEN

BACKGROUND: The prevalence of primary hepatic mucosa-associated lymphoid tissue (MALT) lymphomas is extremely low. Here, we describe a case of this disease misdiagnosed as hepatocellular carcinoma (HCC) and review relevant literature to prevent future misdiagnoses. CASE PRESENTATION: a 58-year-old woman complained about abdominal pain for more than four months. About two months prior, she came to our hospital with elevated levels of HBV DNA and positive HBsAg and HBcAb. After two months of entecavir treatment, HBV DNA decreased to a normal level. She returned to the hospital with worsened abdominal pain for over a month. Magnetic resonance imaging and systemic positron emission tomography-computed tomography identified two nodes in the liver, and she was diagnosed with HCC. The patient then underwent a laparoscopic hepatectomy. Microscopic examination showed a diffuse infiltrate of small-to-medium-sized lymphocytes and lymphoepithelial lesions. Immunohistochemical staining showed that most of the lymphoid cells were strongly positive for CD20, CD79a, BCL2, IgM and weakly positive for IgD, while negative for CD3, CD10, BCL6, MUM1, CD43, CD5, cyclin D1, CD23, CD30, and PD1. The Ki-67 index of lymphoid cells was 5%. Further pathologic analysis confirmed the diagnosis of primary hepatic MALT lymphoma. The patient received antiviral treatment and recovered well with no sign of relapse for 17 months. CONCLUSIONS: Primary hepatic MALT lymphoma is an uncommon disease that is difficult to diagnose and has no widely accepted treatment. Surgical resection is a good choice for both diagnosis and local therapy, and strict follow-up of the patient is essential.

RESUMEN

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortunately, collecting large-scale training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions. Our CoSinGAN is able to capture the conditional distribution of the single radiological image, and further synthesize high-resolution (512 × 512) and diverse radiological images that match the input conditions precisely. We evaluate the efficacy of CoSinGAN in learning lung and infection segmentation from very few radiological images by performing 5-fold cross validation on COVID-19-CT-Seg dataset (20 CT cases) and an independent testing on the MosMed dataset (50 CT cases). Both 2D U-Net and 3D U-Net, learned from four CT slices by using our CoSinGAN, have achieved notable infection segmentation performance, surpassing the COVID-19-CT-Seg-Benchmark, i.e., the counterparts trained on an average of 704 CT slices, by a large margin. Such results strongly confirm that our method has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of COVID-19 pandemic.

RESUMEN

Introduction: A commonly utilized method of measuring femoral stem migration in total hip arthroplasty (THA) on plain anteroposterior (AP) pelvis radiograph with referenced image magnification has not been rigorously evaluated. This study aims to validate the reproducibility of the methods used in this technique. Methods: A retrospective study of the standardized AP pelvis radiographs of patients who had undergone THA utilizing a Corail® femoral stem was performed from June 2012 through December 2017. Radiological evaluation (head diameter, stem length, and stem seating length) were undertaken at three clinical follow-up times. Each radiographic measurement of each radiograph was repeated five times. Outcomes investigated included inter- and intra-radiograph reproducibility evaluation and radiographic image magnification. The stem length error and stem subsidence were also evaluated. Results: Two hundred THA patients met the inclusion/ exclusion criteria. The intra-radiograph reproducibility of the stem length and head diameter measurements have at least "good" reproducibility with repeated measurements falling within 0.5 mm for both measurements. The reliability for femoral stem seating length measurements has "questionable/poor" reproducibility. The inter-radiograph reproducibility was, however, substantially lower. High level of unreliable measurements with values less than 0.0 mm for both femoral stem length errors (55%) and femoral stem subsidence (32%) measurements. Less than 45% accuracy (femoral stem length error: 33%; femoral stem subsidence: 44%) to within 3 mm error. Conclusions: This study demonstrates that the assessment of radiographic implant migration after THA made on a sequence of plain AP pelvis radiograph have poor reproducibility.Level of Evidence: III.

Asunto(s)

RESUMEN

We report a case of idiopathic hypereosinophilic syndrome (IHES) characterized by multiple liver mass lesions in an 82-year-old man. Numerous hypoechoic lesions were observed on ultrasonography and were mainly distributed in the S4, S6, and S7 segments. Plain computed tomography (CT) scans revealed low-density lesions. Dynamic CT images revealed arterial and portal vein branches passing through these lesions, with marginal areas enhanced during the arterial phase. The enhanced areas were extended during the portal venous phase. Contrast-enhanced ultrasonography (CEUS) images revealed enhanced vasculature in the early vascular phase. CEUS images obtained in the late vascular phase revealed enhanced areas containing microbubbles extended into the parenchyma; a prolonged enhancement pattern was observed. Kupffer-phase images revealed large portions of the lesion filled with microbubbles and a star-like defect at the center of the nodule. F18-2-fluoro-2-deoxyglucose (FDG) positron emission tomography/CT scans revealed intense FDG uptake by these lesions, which was similar to that by the segments S4, S6, and S7. Liver biopsy revealed diffused eosinophils infiltrated. The patient was closely followed up and was completely cured 11 weeks later without any treatment. This is a rare case of IHES with multiple liver mass lesions, which was well researched using multi-imaging equipment and cured without any treatment.

Asunto(s)

RESUMEN

Objective:To analyze characteristics of CT scan and MRI images of middle ear adenomas,and provide pre-operation diagnosis and differential diagnosis combined with clinical manifestation.Method:Retrospective analysis of 8 cases of middle ear adenomas which were diagnosed and treated with surgery in Beijing Tongren Hospital between 2004 and 2014, patients' complain, clinical manifestation, physical examination, pure tone analysis, CT scan and MRI images were collected.Result:A total of 8 cases were included in this study with 5 females and 3 males. Age of onset ranged from 21 to 51 years old, with an average age of 37.5 years old, and middle age of 37 years old. All patients suffered from single side middle ear adenomas, 5 of left side and 3 of right side. All 8 patients suffered from hearing loss with tinnitus or stuffy feelings, 2 cases with otalgia, 1 with facial nerve paralysis. Physical examination showed 5 cases of bulging of tympanic membrane, 2 cases of out growth of mass into the external ear canal, and 1 with normal tympanic membrane. CT scan of 7 patients showed mass in tympanum and mastoid, with packaged ossicular chain and with no significant bone damage. 2 cases showed out growth of mass into the external ear canal, and 1 case showed limited mass in middle tympanum. MRI images of 5 cases showed equal T1 and T2 signals with intensifying in tympanum. Long T1 or equal T1 and long T2 signal in mastoid showed obstructive inflammation.Conclusion:Characters of middle ear adenomas includes mass in tympanum without bone damage in CT scan, and equal T1 and T2 signal with significant intensifying in tympanum in MRI images. Combined with clinical manifestation, this radiological information may provide benefit for pre-operation diagnosis.

Asunto(s)

RESUMEN

RATIONALE AND OBJECTIVES: Radiology practice has become increasingly based on volumetric images (VIs), but tests in medical education still mainly involve two-dimensional (2D) images. We created a novel, digital, VI test and hypothesized that scores on this test would better reflect radiological anatomy skills than scores on a traditional 2D image test. To evaluate external validity we correlated VI and 2D image test scores with anatomy cadaver-based test scores. MATERIALS AND METHODS: In 2012, 246 medical students completed one of two comparable versions (A and B) of a digital radiology test, each containing 20 2D image and 20 VI questions. Thirty-three of these participants also took a human cadaver anatomy test. Mean scores and reliabilities of the 2D image and VI subtests were compared and correlated with human cadaver anatomy test scores. Participants received a questionnaire about perceived representativeness and difficulty of the radiology test. RESULTS: Human cadaver test scores were not correlated with 2D image scores, but significantly correlated with VI scores (r = 0.44, P < .05). Cronbach's α reliability was 0.49 (A) and 0.65 (B) for the 2D image subtests and 0.65 (A) and 0.71 (B) for VI subtests. Mean VI scores (74.4%, standard deviation 2.9) were significantly lower than 2D image scores (83.8%, standard deviation 2.4) in version A (P < .001). VI questions were considered more representative of clinical practice and education than 2D image questions and less difficult (both P < .001). CONCLUSIONS: VI tests show higher reliability, a significant correlation with human cadaver test scores, and are considered more representative for clinical practice than tests with 2D images.

Asunto(s)

RESUMEN

OBJETIVO: Investigar o efeito da adição de filtros de alumínio (1 mm) e cobre (0,4 mm) na redução das doses efetivas de radiação e na qualidade das imagens em exames videofluoroscópicos. MATERIAIS E MÉTODOS: Ao tubo de raios X adicionou-se câmara de ionização conectada a um eletrômetro para medir o produto kerma-área, com técnica de 65 kVp e 0,7 mA, sem e com adição dos filtros. Foi medida resolução espacial, a de baixo contraste e tons de cinza, utilizando os objetos de teste de Leeds. Quinze voluntários tiveram o produto kerma-área/minuto do estudo faríngeo comparados, dez com filtração e base e cinco com adição dos filtros associados. RESULTADOS: A adição dos filtros separados ou associados produziu expressiva redução do produto kerma-área, com ganho na qualidade das imagens videofluoroscópicas determinado pela maior separação dos tons de cinza e aumento da relação brilho/contraste da curva de cinza. CONCLUSÃO: A interposição adicional de filtros de alumínio e cobre, em especial quando associados, melhora a qualidade das imagens, com expressiva redução das doses de radiação necessárias à sua geração.

OBJECTIVE: The purpose of thys study was to investigate the effect of the addition of aluminum (1 mm) and copper (0.4 mm) filters on effective radiation doses and image quality in videofluoroscopy. MATERIALS AND METHODS: An ionization chamber coupled with an electrometer was added to x-ray tube to measure the kerma area product with 65 kV and 0.7 mA technique, without and with additional filtration. Low contrast, gray scale and spatial resolution were measured utilizing Leeds test objects. Fifteen volunteers underwent pharynx study, ten without and five with aluminum and cooper filters associated, and had the kerma area product/minute compared. RESULTS: The specified filters addition, either separated or associated, allowed an expressive decrease in kerma area product besides an actual improvement in the videofluoroscopic images quality determined by a better gray tones differentiation and increased brightness contrast ratio in the gray curve. CONCLUSION: Additional aluminum and copper filters interposition, especially when associated, results in improved image quality with expressive reduction in the required radiation doses.

Asunto(s)