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1.
J Transl Med ; 19(1): 29, 2021 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-33413480

RESUMO

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.


Assuntos
/métodos , /diagnóstico , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , /estatística & dados numéricos , China/epidemiologia , Feminino , Ensaios de Triagem em Larga Escala/métodos , Ensaios de Triagem em Larga Escala/estatística & dados numéricos , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Nomogramas , Pandemias , Pneumonia Viral/epidemiologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos , Estudos Retrospectivos , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Pesquisa Médica Translacional
2.
Medicine (Baltimore) ; 99(50): e23692, 2020 Dec 11.
Artigo em Inglês | MEDLINE | ID: mdl-33327359

RESUMO

The purpose of this study was to compare the effectiveness of a metal artifact reduction algorithm (MAR), model-based iterative reconstruction (MBIR), and virtual monochromatic imaging (VMI) for reducing metal artifacts in CT imaging.A phantom study was performed for quantitatively evaluating the dark bands and fine streak artifacts generated by unilateral hip prostheses. Images were obtained by conventional scanning at 120 kilovolt peak, and reconstructed using filtered back projection, MAR, and MBIR. Furthermore, virtual monochromatic images (VMIs) at 70 kilo-electron volts (keV) and 140 keV with/without use of MAR were obtained by dual-energy CT. The extents and mean CT values of the dark bands and the differences in the standard deviations and location parameters of the fine streak artifacts evaluated by the Gumbel method in the images obtained by each of the methods were statistically compared by analyses of variance.Significant reduction of the extent of the dark bands was observed in the images reconstructed using MAR than in those not reconstructed using MAR (all, P < .01). Images obtained by VMI at 70 keV and 140 keV with use of MAR showed significantly increased mean CT values of the dark bands as compared to those obtained by reconstructions without use of MAR (all, <.01). Significant reduction of the difference in the standard deviations used to evaluate fine streak artifacts was observed in each of the image sets obtained with VMI at 140 keV with/without MAR and conventional CT with MBIR as compared to the images obtained using other methods (all, P < .05), except between VMI at 140 keV without MAR and conventional CT with MAR. The location parameter to evaluate fine streak artifacts was significantly reduced in CT images obtained using MBIR and in images obtained by VMI at 140 keV with/without MAR as compared to those obtained using other reconstruction methods (all, P < .01).In our present study, MAR appeared to be the most effective reconstruction method for reducing dark bands in CT images, and MBIR and VMI at 140 keV appeared to the most effective for reducing streak artifacts.


Assuntos
Metais , Próteses e Implantes , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Humanos , Imagens de Fantasmas , Tomografia Computadorizada por Raios X/normas
3.
Rev. cuba. inform. méd ; 12(2): e386, tab, graf
Artigo em Espanhol | LILACS, CUMED | ID: biblio-1144463

RESUMO

Una de las campañas más reconocidas en el mundo es la lucha contra el cáncer, siendo el sistema renal uno de los más afectados por esta patología. El carcinoma de células renales (CCR), el más común de cáncer renal en los adultos, representa la sexta causa de muerte por cáncer. Debido al aumento en el uso de las técnicas de diagnóstico por imagen, las lesiones renales pueden ser diagnosticadas en forma incidental aproximadamente en 50% de los casos. Cuba apuesta por el uso de la tecnología en la salud y en la Universidad de las Ciencias Informáticas (UCI) se ha desarrollado un sistema para el almacenamiento, transmisión y visualización de imágenes médicas (XAVIA PACS), el cual se encuentra implantado en varios hospitales del país, pero no cuenta con alternativas para realizar la detección del CCR en imágenes tomográficas, haciendo más lento el diagnóstico, lo que se traduce en menos posibilidades para el paciente. La presente investigación tiene como objetivo realizar un análisis sobre las principales técnicas de segmentación y procesamiento para la detección de carcinomas renales en imágenes de tomografías abdominal, que propicie a los equipos de desarrollo contar con la base teórica necesaria para enfrentar el problema en cuestión. Para ello se realizó un análisis documental sobre trabajos relacionados con la temática y que propician soluciones al problema. Se estudiaron algoritmos y técnicas computacionales efectivas para la segmentación y procesamiento de imágenes abdominales. Como resultado de la investigación se obtuvieron los algoritmos más acordes para el sistema XAVIA PACS y el contexto médico cubano(AU)


One of the most recognized campaigns in the world is the fight against cancer, the kidney system being one of the most affected by this pathology. Renal cell carcinoma (RCC), the most common form of kidney cancer in adults, represents the sixth leading cause of cancer death. Due to the increased use of diagnostic imaging techniques, kidney injuries can be diagnosed incidentally in approximately 50% of cases. Cuba is committed to the use of technology in health and a system for the storage, transmission and display of medical images (XAVIA PACS) has been developed at the University of Computer Sciences (UCI), which is implanted in several hospitals of the country, but it does not have alternatives to detect RCC in tomographic images, slowing down the diagnosis, which translates into fewer possibilities for the patient. The objective of this research is to carry out an analysis on the main segmentation and processing techniques for the detection of renal carcinomas in abdominal tomography images, which provides development teams with the theoretical basis necessary to face the problem in question. For this, a documentary analysis was carried out on works related to the subject and that provide solutions to the problem. Algorithms and effective computational techniques for the segmentation and processing of abdominal images were studied. As a result of the research, the most suitable algorithms for the XAVIA PACS system and the Cuban medical context were obtained(AU)


Assuntos
Algoritmos , Linguagens de Programação , Software , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Neoplasias Renais/epidemiologia , Neoplasias Renais/diagnóstico por imagem
4.
PLoS One ; 15(11): e0242535, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33201919

RESUMO

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Aprendizado Profundo , Pneumonia Viral/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X , Betacoronavirus , Humanos , Pandemias , Tórax/patologia , Tórax/ultraestrutura
5.
Medicine (Baltimore) ; 99(46): e23112, 2020 Nov 13.
Artigo em Inglês | MEDLINE | ID: mdl-33181680

RESUMO

To determine the association between mammographic density (MD) and the risk of breast cancer (BC) in Chinese women and to investigate the role of fertility risk factors in regulating the relationship between MD and BC.We used Quantra software and the BI-RADS classification to assess MD in 466 patients and 932 controls. Conditional matched logistic multiple regression analysis was used to determine the relationship between MD and BC, and risk was evaluated with the odds ratio (OR) and 95% confidence interval (CI).The ORs for category 4 versus category 2 were 1.95 (95% confidence interval [95% CI] (1.42∼2.66)) and 1.76 (95% CI (1.28∼2.42)) for the BI-RADS and Quantra classifications, respectively. The ORs for category 5 volumetric breast density (VBD) versus category 2 VBD and 5 fibroglandular tissue volume (FGV) versus category 2 FGV were 1.63 (95% CI (1.20∼2.23)) and 1.92 (95% CI (1.40∼2.63)), respectively. Females with category 5 VBD whose age at menarche was ≤13 years had the highest risk of BC (OR = 2.16, 95% CI (1.24∼3.79)), and females with category 5 FGV whose age at menarche was = 15 years had the lowest risk of BC (OR = 1.65, 95% CI (1.05∼2.62)). Females with categories 3-5 VBD and categories 3-5 FGV had reduced risks of BC with increasing number of births. Females with category 5 VBD had an increased risk of BC with increasing age at first childbirth (the OR increased from 1.49 to 1.95). Those with category 5 VBD had a reduced risk of BC with increasing breastfeeding duration (the OR decreased from 2.08 to 1.55). Females with category 5 FGV had a reduced risk of BC with increasing breastfeeding duration (the OR decreased from 4.12 to 1.62).Both the BI-RADS density classification and Quantra measures indicated that MD is positively associated with the risk of BC in Chinese women and that associations between MD and BC risk differ by age at menarche, parity, age at first childbirth and breastfeeding duration.


Assuntos
Densidade da Mama , Neoplasias da Mama/diagnóstico , Mama , Mamografia/métodos , Medição de Risco/métodos , Fatores Etários , Mama/diagnóstico por imagem , Mama/patologia , Aleitamento Materno/estatística & dados numéricos , Neoplasias da Mama/epidemiologia , China/epidemiologia , Detecção Precoce de Câncer , Feminino , Humanos , Menarca , Pessoa de Meia-Idade , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , História Reprodutiva , Software
6.
Comput Math Methods Med ; 2020: 9756518, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33014121

RESUMO

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico por imagem , Pandemias , Pneumonia Viral/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Inteligência Artificial , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Aprendizado Profundo , Humanos , Redes Neurais de Computação , Pneumonia/classificação , Pneumonia/diagnóstico por imagem , Pneumonia Viral/epidemiologia , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos , Radiografia Torácica/estatística & dados numéricos , Sensibilidade e Especificidade
7.
J Cancer Res Ther ; 16(4): 780-787, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32930118

RESUMO

Background: There are "blind spots" on chest computed tomography (CT) where pulmonary nodules can easily be overlooked. The number of missed pulmonary nodules can be minimized by instituting a training program with particular focus on the depiction of nodules at blind spots. Purpose: The purpose of this study was to assess the variation in lung nodule detection in chest CT based on location, attenuation characteristics, and reader experience. Materials and Methods: We selected 18 noncalcified lung nodules (6-8 mm) suspicious of primary and metastatic lung cancer with solid (n = 7), pure ground-glass (6), and part-solid ground-glass (5) attenuation from 12 chest CT scans. These nodules were randomly inserted in chest CT of 34 patients in lung hila, 1st costochondral junction, branching vessels, paramediastinal lungs, lung apices, juxta-diaphragm, and middle and outer thirds of the lungs. Two residents and two chest imaging clinical fellows evaluated the CT images twice, over a 4-month interval. Before the second reading session, the readers were trained and made aware of the potential blind spots. Chi-square test was used to assess statistical significance. Results: Pretraining session: Fellows detected significantly more part-solid ground-glass nodules compared to residents (P = 0.008). A substantial number of nodules adjacent to branching vessels and posterior mediastinum were missed. Posttraining session: There was a significant increase in detectability independent of attenuation and location of nodules for all readers (P < 0.0008). Conclusion: Dedicated chest CT training improves detection of lung nodules, especially the part-solid ground-glass nodules. Detection of nodules adjacent to branching vessels and the posterior mediastinal lungs is difficult even for fellowship-trained radiologists.


Assuntos
Neoplasias Pulmonares/diagnóstico , Nódulos Pulmonares Múltiplos/diagnóstico , Radiologia/educação , Treinamento por Simulação/métodos , Nódulo Pulmonar Solitário/diagnóstico , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Detecção Precoce de Câncer/métodos , Feminino , Humanos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Masculino , Mediastino/diagnóstico por imagem , Mediastino/patologia , Pessoa de Meia-Idade , Nódulos Pulmonares Múltiplos/diagnóstico por imagem , Nódulos Pulmonares Múltiplos/patologia , Lesões Pré-Cancerosas/diagnóstico por imagem , Lesões Pré-Cancerosas/patologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiologia/métodos , Software , Nódulo Pulmonar Solitário/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos
8.
PLoS One ; 15(9): e0239459, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32946530

RESUMO

This study aimed to evaluate the visualization of peripheral bronchioles in normal lungs via quarter-detector computed tomography (QDCT). Visualization of bronchioles within 10 mm from the pleura is considered a sign of bronchiectasis. However, it is not known peripheral bronchioles how close to the pleura in normal lungs can be tracked using QDCT. This study included 228 parts in 76 lungs from 38 consecutive patients who underwent QDCT. Reconstruction was performed with different thicknesses, increments, and matrix sizes: 0.5-mm thickness and increment with 512 and 1024 matrixes (Group5 and Group10, respectively) and 0.25-mm thickness and increment with 1024 matrix (Group10Thin). The distance between the most peripheral bronchiole visible and the pleura was determined in the three groups. The distance between the peripheral bronchial duct ends and the nearest pleural surface were significantly shorter in the order of Group10Thin, Group10, and Group5, and the mean distances from the pleura in Group10Thin and Group10 were shorter than 10 mm. These findings suggest the visualization of peripheral bronchioles in QDCT was better with a 1024 axial matrix than with a 512 matrix, and with a 0.25-mm slice thickness/increment than with a 0.5-mm slice thickness/increment. Our study also indicates bronchioles within 10 mm of the pleura do not necessarily indicate pathology.


Assuntos
Bronquíolos/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Bronquiectasia/diagnóstico por imagem , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pleura/diagnóstico por imagem , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/estatística & dados numéricos
9.
PLoS One ; 15(9): e0238926, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32925940

RESUMO

Fractures of the wrist are common in Emergency Departments, where some patients are treated with a procedure called Manipulation under Anaesthesia. In some cases, this procedure is unsuccessful and patients need to revisit the hospital where they undergo surgery to treat the fracture. This work describes a geometric semi-automatic image analysis algorithm to analyse and compare the x-rays of healthy controls and patients with dorsally displaced wrist fractures (Colles' fractures) who were treated with Manipulation under Anaesthesia. A series of 161 posterior-anterior radiographs from healthy controls and patients with Colles' fractures were acquired and analysed. The patients' group was further subdivided according to the outcome of the procedure (successful/unsuccessful) and pre- or post-intervention creating five groups in total (healthy, pre-successful, pre-unsuccessful, post-successful, post-unsuccessful). The semi-automatic analysis consisted of manual location of three landmarks (finger, lunate and radial styloid) and automatic processing to generate 32 geometric and texture measurements, which may be related to conditions such as osteoporosis and swelling of the wrist. Statistical differences were found between patients and controls, as well as between pre- and post-intervention, but not between the procedures. The most distinct measurements were those of texture. Although the study includes a relatively low number of cases and measurements, the statistical differences are encouraging.


Assuntos
Fratura de Colles/diagnóstico por imagem , Fratura de Colles/terapia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Casos e Controles , Feminino , Humanos , Masculino , Manipulação Ortopédica , Pessoa de Meia-Idade , Radiografia , Resultado do Tratamento
10.
PLoS One ; 15(9): e0238582, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32966278

RESUMO

OBJECTIVE: To compare "virtual" unenhanced (VUE) computed tomography (CT) images, reconstructed from rapid kVp-switching dual-energy computed tomography (DECT), to "true" unenhanced CT images (TUE), in clinical abdominal imaging. The ability to replace TUE with VUE images would have many clinical and operational advantages. METHODS: VUE and TUE images of 60 DECT datasets acquired for standard-of-care CT of pancreatic cancer were retrospectively reviewed and compared, both quantitatively and qualitatively. Comparisons included quantitative evaluation of CT numbers (Hounsfield Units, HU) measured in 8 different tissues, and 6 qualitative image characteristics relevant to abdominal imaging, rated by 3 experienced radiologists. The observed quantitative and qualitative VUE and TUE differences were compared against boundaries of clinically relevant equivalent thresholds to assess their equivalency, using modified paired t-tests and Bayesian hierarchical modeling. RESULTS: Quantitatively, in tissues containing high concentrations of calcium or iodine, CT numbers measured in VUE images were significantly different from those in TUE images. CT numbers in VUE images were significantly lower than TUE images when calcium was present (e.g. in the spine, 73.1 HU lower, p < 0.0001); and significantly higher when iodine was present (e.g. in renal cortex, 12.9 HU higher, p < 0.0001). Qualitatively, VUE image ratings showed significantly inferior depiction of liver parenchyma compared to TUE images, and significantly more cortico-medullary differentiation in the kidney. CONCLUSIONS: Significant differences in VUE images compared to TUE images may limit their application and ability to replace TUE images in diagnostic abdominal CT imaging.


Assuntos
Abdome/diagnóstico por imagem , Neoplasias Pancreáticas/diagnóstico por imagem , Imagem Radiográfica a Partir de Emissão de Duplo Fóton/métodos , Tomografia Computadorizada por Raios X/métodos , Feminino , Humanos , Masculino , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Estudos Retrospectivos
11.
PLoS One ; 15(9): e0239562, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32966330

RESUMO

Reproducible and unbiased methods to quantify alveolar structure are important for research on many lung diseases. However, manually estimating alveolar structure through stereology is time consuming and inter-observer variability is high. The objective of this work was to develop and validate a fast, reproducible and accurate (semi-)automatic alternative. A FIJI-macro was designed that automatically segments lung images to binary masks, and counts the number of test points falling on tissue and the number of intersections of the air-tissue interface with a set of test lines. Manual selection remains necessary for the recognition of non-parenchymal tissue and alveolar exudates. Volume density of alveolar septa ([Formula: see text]) and mean linear intercept of the airspaces (Lm) as measured by the macro were compared to theoretical values for 11 artificial test images and to manually counted values for 17 lungs slides using linear regression and Bland-Altman plots. Inter-observer agreement between 3 observers, measuring 8 lungs both manually and automatically, was assessed using intraclass correlation coefficients (ICC). [Formula: see text] and Lm measured by the macro closely approached theoretical values for artificial test images (R2 of 0.9750 and 0.9573 and bias of 0.34% and 8.7%). The macro data in lungs were slightly higher for [Formula: see text] and slightly lower for Lm in comparison to manually counted values (R2 of 0.8262 and 0.8288 and bias of -6.0% and 12.1%). Visually, semi-automatic segmentation was accurate. Most importantly, manually counted [Formula: see text] and Lm had only moderate to good inter-observer agreement (ICC 0.859 and 0.643), but agreements were excellent for semi-automatically counted values (ICC 0.956 and 0.900). This semi-automatic method provides accurate and highly reproducible alveolar morphometry results. Future efforts should focus on refining methods for automatic detection of non-parenchymal tissue or exudates, and for assessment of lung structure on 3D reconstructions of lungs scanned with microCT.


Assuntos
Displasia Broncopulmonar/patologia , Interpretação de Imagem Assistida por Computador/métodos , Alvéolos Pulmonares/patologia , Animais , Displasia Broncopulmonar/diagnóstico por imagem , Modelos Animais de Doenças , Feminino , Técnicas Histológicas/estatística & dados numéricos , Variações Dependentes do Observador , Gravidez , Alvéolos Pulmonares/diagnóstico por imagem , Coelhos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Microtomografia por Raio-X/estatística & dados numéricos
12.
J Comput Assist Tomogr ; 44(5): 790-795, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32936580

RESUMO

BACKGROUND: The state-of-art motion correction algorithm is inadequate for correcting motion artifacts in coronary arteries in cardiovascular computed tomography angiography (CCTA) for children with high heart rates, and even less effective for heart structures beyond coronary arteries. PURPOSE: This study aimed to evaluate the effectiveness of a second-generation, whole-heart motion correction algorithm in improving the heart image quality of CCTA for children with high heart rates. MATERIALS AND METHODS: Forty-two consecutive symptomatic cardiac patients with high heart rates (122.6 ± 18.8 beats/min) were enrolled. All patients underwent CCTA on a 256-row CT using a prospective electrocardiogram-triggered single-beat protocol. Images were reconstructed using a standard algorithm (STD), state-of-the-art first-generation coronary artery motion correction algorithm (MC1), and second-generation, whole-heart motion correction algorithm (MC2). The image quality of the origin of left coronary, right coronary, aortic valve, pulmonary valve, mitral valve, tricuspid valve, aorta root, pulmonary artery root, ventricular septum (VS), and atrial septum (AS) was assessed by 2 experienced radiologists using a 4-point scale (1, nondiagnostic; 2, detectable; 3, measurable; and 4, excellent); nonparametric test was used to analyze and compare the differences among 3 groups; and post hoc multiple comparisons were used between different methods. RESULTS: There were group differences for cardiac structures except VS and AS, with MC2 having the best image quality and STD having the worst image quality. Post hoc multiple comparisons showed that MC2 was better than MC1 and STD in all structures except VS and AS where all 3 algorithms performed equally, whereas MC1 was better than STD only in the origin of left coronary, right coronary, and mitral valve. CONCLUSIONS: A second-generation, whole-heart motion correction algorithm further significantly improves cardiac image quality beyond the coronaries in CCTA for pediatric patients with high heart rates.


Assuntos
Angiografia por Tomografia Computadorizada/métodos , Angiografia Coronária/métodos , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/fisiopatologia , Frequência Cardíaca/fisiologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Algoritmos , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Masculino , Estudos Retrospectivos
13.
PLoS One ; 15(8): e0237674, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790772

RESUMO

Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/Malignancy) of two- view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.


Assuntos
Neoplasias da Mama/diagnóstico , Aprendizado Profundo , Detecção Precoce de Câncer/métodos , Mamografia/métodos , Programas de Rastreamento/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Mama/diagnóstico por imagem , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Feminino , Humanos
14.
IEEE J Biomed Health Inform ; 24(10): 2776-2786, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32750973

RESUMO

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.


Assuntos
Algoritmos , Betacoronavirus , Técnicas de Laboratório Clínico/métodos , Infecções por Coronavirus/diagnóstico , Pandemias , Pneumonia Viral/diagnóstico , Teorema de Bayes , Técnicas de Laboratório Clínico/estatística & dados numéricos , Biologia Computacional , Infecções por Coronavirus/diagnóstico por imagem , Infecções por Coronavirus/epidemiologia , Bases de Dados Factuais/estatística & dados numéricos , Aprendizado Profundo , Humanos , Redes Neurais de Computação , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/epidemiologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/estatística & dados numéricos
15.
PLoS One ; 15(8): e0235672, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32845877

RESUMO

A new computer-aided detection scheme is proposed, the 3D U-Net convolutional neural network, based on multiscale features of transfer learning to automatically detect pulmonary nodules from the thoracic region containing background and noise. The test results can be used as reference information for doctors to assist in the detection of early lung cancer. The proposed scheme is composed of three major steps: First, the pulmonary parenchyma area is segmented by various methods. Then, the 3D U-Net convolutional neural network model with a multiscale feature structure is built. The network model structure is subsequently fine-tuned by the transfer learning method based on weight, and the optimal parameters are selected in the network model. Finally, datasets are extracted to train the fine-tuned 3D U-Net network model to detect pulmonary nodules. The five-fold cross-validation method is used to obtain the experimental results for the LUNA16 and TIANCHI17 datasets. The experimental results show that the scheme not only has obvious advantages in the detection of medium and large-sized nodules but also has an accuracy rate of more than 70% for the detection of small-sized nodules. The scheme provides automatic and accurate detection of pulmonary nodules that reduces the overfitting rate and training time and improves the efficiency of the algorithm. It can assist doctors in the diagnosis of lung cancer and can be extended to other medical image detection and recognition fields.


Assuntos
Imageamento Tridimensional/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Redes Neurais de Computação , Nódulo Pulmonar Solitário/diagnóstico por imagem , Algoritmos , Detecção Precoce de Câncer/métodos , Humanos , Aprendizado de Máquina , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos
16.
PLoS One ; 15(8): e0237434, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32797096

RESUMO

OBJECTIVES: To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom. MATERIALS AND METHODS: Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kVp and 80kVp, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kVp/120 mAs serving as the standard of reference. RESULTS: Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05). CONCLUSIONS: Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required.


Assuntos
Pulmão/fisiologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X , Algoritmos , Animais , Antropometria , Densitometria , Pulmão/diagnóstico por imagem , Pulmão/efeitos da radiação , Exposição à Radiação , Razão Sinal-Ruído , Suínos , Tórax/diagnóstico por imagem
17.
Medicine (Baltimore) ; 99(34): e21886, 2020 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-32846848

RESUMO

BACKGROUND: As the gold standard for imaging sinus disease, the main disadvantage of computed tomography (CT) of the pediatric paranasal sinus is radiation exposure. Because of this, 1 protocol for CT should reduce radiation dose while maintaining image quality. The aim of this study is to evaluate the image quality of dose-reduced paranasal sinus computed tomography (CT) using an ultralow tube voltage (70 kVp) combined with iterative reconstruction (IR) in children. METHODS: CT scans of the paranasal sinus were performed using different protocols [70 kVp protocols with IR, Group A, n = 80; 80 kVp protocols with a filtered back projection algorithm, Group B, n = 80] in 160 pediatric patients. Then, the volume-weighted CT dose index, dose-length product, and effective dose were estimated. Image noise, the signal-to-noise ratio and the diagnostic image quality were also evaluated. RESULTS: For the radiation dose, the volume-weighted CT dose index, dose-length product and effective dose values were significantly lower for the 70 kVp protocols than for the 80 kVp protocols (P < .001). Compared with the 80 kVp protocols, the 70 kVp protocols had significantly higher levels of image noise (P = .001) and a lower signal-to-noise ratio (P = .002). No significant difference in the overall subjective image quality grades was observed between these 2 groups (P = .098). CONCLUSION: The ultralow tube voltage (70 kVp) technique combined with IR enabled a significant dose reduction in CT examinations performed in the pediatric paranasal sinus while maintaining diagnostic image quality with clinically acceptable image noise.


Assuntos
Aumento da Imagem/instrumentação , Doenças dos Seios Paranasais/diagnóstico por imagem , Exposição à Radiação/efeitos adversos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/instrumentação , Adolescente , Algoritmos , Criança , Pré-Escolar , China/epidemiologia , Estudos de Viabilidade , Feminino , Humanos , Masculino , Estudos Prospectivos , Doses de Radiação , Exposição à Radiação/estatística & dados numéricos , Razão Sinal-Ruído
18.
PLoS One ; 15(8): e0236021, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32745082

RESUMO

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases. PURPOSE: To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans. MATERIALS AND METHODS: Three fully automated machine learning algorithms were used to assess CCS, level of liver steatosis and emphysema percentage in the lung. The algorithms were used on low-dose computed tomography scans acquired from 12,332 participants in NLST. RESULTS: In a multivariate analysis, association between the three algorithm scores and CVD mortality have shown an OR of 1.72 (p = 0.003), 2.62 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively, and an OR of 1.12 (p = 0.044) for level of liver steatosis. Similar results were shown for the incidence of CVD, OR of 1.96 (p < 0.0001), 4.94 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively. Also, emphysema percentage demonstrated an OR of 0.89 (p < 0.0001). Similar results are shown for univariate analyses of the algorithms. CONCLUSION: The three automated machine learning algorithms could help physicians to assess the incidence and risk of CVD mortality in this specific population. Application of these algorithms to existing LDCT scans can provide valuable health care information and assist in future research.


Assuntos
Doenças Cardiovasculares/mortalidade , Aprendizado de Máquina , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X , Doenças Cardiovasculares/diagnóstico , Doenças Cardiovasculares/etiologia , Fumar Cigarros/efeitos adversos , Fumar Cigarros/epidemiologia , Ensaios Clínicos Fase III como Assunto , Vasos Coronários/diagnóstico por imagem , Detecção Precoce de Câncer/métodos , Enfisema/diagnóstico , Enfisema/epidemiologia , Enfisema/etiologia , Fígado Gorduroso/diagnóstico , Fígado Gorduroso/epidemiologia , Feminino , Humanos , Fígado/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/etiologia , Neoplasias Pulmonares/mortalidade , Masculino , Programas de Rastreamento/métodos , Pessoa de Meia-Idade , National Cancer Institute (U.S.) , Ensaios Clínicos Controlados Aleatórios como Assunto , Estudos Retrospectivos , Medição de Risco/métodos , Fatores de Risco , Estados Unidos/epidemiologia
19.
Phys Med ; 77: 36-42, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32771702

RESUMO

PURPOSE: To assess the impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images from a Fast kV-Switching Dual Energy CT (KVSCT) platform. METHODS: Acquisitions on an image-quality phantom were performed using a KVSCT for three dose levels (CTDIvol:12.72/10.76/8.79 mGy). Raw data were reconstructed for five energy levels (40/50/60/70/80 keV) using Filtered Back Projection (FBP) and four levels of ASIR (ASIR30/ASIR50/ASIR70/ASIR100). Noise power spectrum (NPS) and task-based transfer function (TTF) were measured before computing a Detectability index (d') to model the detection task of liver metastasis (LM) and hepatocellular carcinoma (HCC) as function of keV. RESULTS: From 40 to 70 keV, noise-magnitude was reduced on average by -68% ± 1% with FBP; -61% ± 3% with ASIR50 and -52% ± 6% with ASIR100. The mean spatial frequency of the NPS decreased when the energy level decreased and the iterative level increased. TTF values at 50% decreased as the energy level increased and as the percentage of ASIR increased. The detectability of both lesions increased with increasing dose level and percentage of ASIR. For the LM, d' peaked at 70 keV for all reconstruction types, except for ASIR70 at 12.72 mGy and ASIR100, where d' peaked at 50 keV. For HCC, d' peaked at 60 keV for FBP and ASIR30 but peaked at 50 keV for ASIR50, ASIR70 and ASIR100. CONCLUSIONS: Using percentage of ASIR above 50% at low-energy monochromatic images could limit the increase of noise-magnitude, benefit from spatial resolution improvement and hence enhance detectability of subtle low contrast focal liver lesions such as HCC.


Assuntos
Carcinoma Hepatocelular/diagnóstico por imagem , Neoplasias Hepáticas/diagnóstico por imagem , Fígado/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Humanos , Imagens de Fantasmas , Doses de Radiação , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/instrumentação
20.
Nat Commun ; 11(1): 4080, 2020 08 14.
Artigo em Inglês | MEDLINE | ID: mdl-32796848

RESUMO

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.


Assuntos
Inteligência Artificial , Técnicas de Laboratório Clínico/métodos , Infecções por Coronavirus/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Betacoronavirus/isolamento & purificação , Criança , Pré-Escolar , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/virologia , Aprendizado Profundo , Feminino , Humanos , Imageamento Tridimensional/métodos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/virologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Adulto Jovem
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