Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 45.234
Filtrar
1.
J Cardiovasc Magn Reson ; 22(1): 68, 2020 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-32938483

RESUMO

BACKGROUND: Cardiovascular magnetic resonance (CMR) phase contrast (PC) flow measurements suffer from phase offset errors. Background subtraction based on stationary phantom measurements can most reliably be used to overcome this inaccuracy. Stationary tissue correction is an alternative and does not require additional phantom scanning. The aim of this study was 1) to compare measurements with and without stationary tissue correction to phantom corrected measurements on different GE Healthcare CMR scanners using different software packages and 2) to evaluate the clinical implications of these methods. METHODS: CMR PC imaging of both the aortic and pulmonary artery flow was performed in patients on three different 1.5 T CMR scanners (GE Healthcare) using identical scan parameters. Uncorrected, first, second and third order stationary tissue corrected flow measurement were compared to phantom corrected flow measurements, our reference method, using Medis QFlow, Circle cvi42 and MASS software. The optimal (optimized) stationary tissue order was determined per scanner and software program. Velocity offsets, net flow, clinically significant difference (deviation > 10% net flow), and regurgitation severity were assessed. RESULTS: Data from 175 patients (28 (17-38) years) were included, of which 84% had congenital heart disease. First, second and third order and optimized stationary tissue correction did not improve the velocity offsets and net flow measurements. Uncorrected measurements resulted in the least clinically significant differences in net flow compared to phantom corrected data. Optimized stationary tissue correction per scanner and software program resulted in net flow differences (> 10%) in 19% (MASS) and 30% (Circle cvi42) of all measurements compared to 18% (MASS) and 23% (Circle cvi42) with no correction. Compared to phantom correction, regurgitation reclassification was the least common using uncorrected data. One CMR scanner performed worse and significant net flow differences of > 10% were present both with and without stationary tissue correction in more than 30% of all measurements. CONCLUSION: Phase offset errors had a significant impact on net flow quantification, regurgitation assessment and varied greatly between CMR scanners. Background phase correction using stationary tissue correction worsened accuracy compared to no correction on three GE Healthcare CMR scanners. Therefore, careful assessment of phase offset errors at each individual scanner is essential to determine whether routine use of phantom correction is necessary. TRIAL REGISTRATION: Observational Study.


Assuntos
Aorta/diagnóstico por imagem , Insuficiência da Valva Aórtica/diagnóstico por imagem , Cardiopatias Congênitas/diagnóstico por imagem , Hemodinâmica , Interpretação de Imagem Assistida por Computador , Imagem Cinética por Ressonância Magnética/instrumentação , Artéria Pulmonar/diagnóstico por imagem , Insuficiência da Valva Pulmonar/diagnóstico por imagem , Adolescente , Adulto , Aorta/fisiopatologia , Insuficiência da Valva Aórtica/fisiopatologia , Velocidade do Fluxo Sanguíneo , Criança , Feminino , Cardiopatias Congênitas/fisiopatologia , Humanos , Masculino , Imagens de Fantasmas , Valor Preditivo dos Testes , Artéria Pulmonar/fisiopatologia , Insuficiência da Valva Pulmonar/fisiopatologia , Fluxo Sanguíneo Regional , Reprodutibilidade dos Testes , Estudos Retrospectivos , Índice de Gravidade de Doença , Adulto Jovem
2.
Br J Radiol ; 93(1114): 20200543, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32877210

RESUMO

OBJECTIVES: To evaluate interobserver agreement for T2 weighted (T2W) and diffusion-weighted MRI (DW-MRI) contours of locally advanced rectal cancer (LARC); and to evaluate manual and semi-automated delineations of restricted diffusion tumour subvolumes. METHODS: 20 cases of LARC were reviewed by 2 radiation oncologists and 2 radiologists. Contours of gross tumour volume (GTV) on T2W, DW-MRI and co-registered T2W/DW-MRI were independently delineated and compared using Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics of interobserver agreement. Restricted diffusion subvolumes within GTVs were manually delineated and compared to semi-automatically generated contours corresponding to intratumoral apparent diffusion coefficient (ADC) centile values. RESULTS: Observers were able to delineate subvolumes of restricted diffusion with moderate agreement (DSC 0.666, MDA 1.92 mm). Semi-automated segmentation based on the 40th centile intratumoral ADC value demonstrated moderate average agreement with consensus delineations (DSC 0.581, MDA 2.44 mm), with errors noted in image registration and luminal variation between acquisitions. A small validation set of four cases with optimised planning MRI demonstrated improvement (DSC 0.669, MDA 1.91 mm). CONCLUSION: Contours based on co-registered T2W and DW-MRI could be used for delineation of biologically relevant tumour subvolumes. Semi-automated delineation based on patient-specific intratumoral ADC thresholds may standardise subvolume delineation if registration between acquisitions is sufficiently accurate. ADVANCES IN KNOWLEDGE: This is the first study to evaluate the feasibility of semi-automated diffusion-based subvolume delineation in LARC. This approach could be applied to dose escalation or 'dose painting' protocols to improve delineation reproducibility.


Assuntos
Adenocarcinoma/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética/métodos , Interpretação de Imagem Assistida por Computador/métodos , Neoplasias Retais/diagnóstico por imagem , Adenocarcinoma/patologia , Adenocarcinoma/terapia , Adulto , Idoso , Idoso de 80 Anos ou mais , Competência Clínica , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Variações Dependentes do Observador , Neoplasias Retais/patologia , Neoplasias Retais/terapia , Reprodutibilidade dos Testes , Estudos Retrospectivos , Carga Tumoral
3.
Curr Opin Ophthalmol ; 31(5): 303-311, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32740061

RESUMO

PURPOSE OF REVIEW: As artificial intelligence continues to develop new applications in ophthalmic image recognition, we provide here an introduction for ophthalmologists and a primer on the mechanisms of deep learning systems. RECENT FINDINGS: Deep learning has lent itself to the automated interpretation of various retinal imaging modalities, including fundus photography and optical coherence tomography. Convolutional neural networks (CNN) represent the primary class of deep neural networks applied to these image analyses. These have been configured to aid in the detection of diabetes retinopathy, AMD, retinal detachment, glaucoma, and ROP, among other ocular disorders. Predictive models for retinal disease prognosis and treatment are also being validated. SUMMARY: Deep learning systems have begun to demonstrate a reliable level of diagnostic accuracy equal or better to human graders for narrow image recognition tasks. However, challenges regarding the use of deep learning systems in ophthalmology remain. These include trust of unsupervised learning systems and the limited ability to recognize broad ranges of disorders.


Assuntos
Aprendizado Profundo , Diagnóstico por Imagem/métodos , Oftalmopatias/diagnóstico , Interpretação de Imagem Assistida por Computador/métodos , Oftalmologistas , Humanos , Redes Neurais de Computação
4.
Curr Opin Ophthalmol ; 31(5): 324-328, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32769696

RESUMO

PURPOSE OF REVIEW: To review four recent controversial topics arising from deep learning applications in ophthalmology. RECENT FINDINGS: The controversies of four recent topics surrounding deep learning applications in ophthalmology are discussed, including the following: lack of explainability, limited generalizability, potential biases and protection of patient confidentiality in large-scale data transfer. SUMMARY: These controversial issues spanning the domains of clinical medicine, public health, computer science, ethics and legal issues, are complex and likely will benefit from an interdisciplinary approach if artificial intelligence in ophthalmology is to succeed over the next decade.


Assuntos
Inteligência Artificial , Oftalmopatias/diagnóstico , Interpretação de Imagem Assistida por Computador/métodos , Oftalmologia , Big Data , Humanos
5.
Radiol Clin North Am ; 58(5): 875-884, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32792120

RESUMO

Indeterminate renal masses remain a diagnostic challenge for lesions not initially characterized as angiomyolipoma or Bosniak I/II cysts. Differential for indeterminate renal masses include oncocytoma, fat-poor angiomyolipoma, and clear cell, papillary, and chromophobe renal cell carcinoma. Qualitative and quantitative techniques using data derived from multiphase contrast-enhanced imaging have provided methods for specific differentiation and subtyping of indeterminate renal masses, with emerging applications such as radiocytogenetics. Early and accurate characterization of indeterminate renal masses by multiphase contrast-enhanced imaging will optimize triage of these lesions into surgical, ablative, and active surveillance treatment plans.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Neoplasias Renais/diagnóstico por imagem , Neoplasias Renais/patologia , Imagem por Ressonância Magnética/métodos , Tomografia Computadorizada por Raios X/métodos , Meios de Contraste , Diagnóstico Diferencial , Humanos , Aumento da Imagem/métodos , Rim/diagnóstico por imagem , Rim/patologia , Triagem
6.
Radiol Clin North Am ; 58(5): 995-1008, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32792129

RESUMO

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.


Assuntos
Inteligência Artificial , Carcinoma de Células Renais/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Neoplasias Renais/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Tomografia Computadorizada por Raios X/métodos , Diagnóstico Diferencial , Humanos , Rim/diagnóstico por imagem
7.
PLoS One ; 15(8): e0234169, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32810131

RESUMO

Toxoplasma gondii is an obligate intracellular parasite infecting up to one third of the human population. The central event in the pathogenesis of toxoplasmosis is the conversion of tachyzoites into encysted bradyzoites. A novel approach to analyze the structure of in vivo-derived tissue cysts may be the increasingly used computational image analysis. The objective of this study was to quantify the geometrical complexity of T. gondii cysts by morphological, particle, and fractal analysis, as well as to determine if it is impacted by parasite strain, cyst age, and host type. A total of 31 images of T. gondii brain cysts of four type-2 strains (Me49, and local isolates BGD1, BGD14, and BGD26) was analyzed using ImageJ software. The parameters of interest included diameter, circularity, packing density (PD), fractal dimension (FD), and lacunarity. Although cyst diameter varied widely, its negative correlation with PD was observed. Circularity was remarkably close to 1, indicating a perfectly round shape of the cysts. PD and FD did not vary among cysts of different strains, age, and derived from mice of different genetic background. Conversely, lacunarity, which is a measure of heterogeneity, was significantly lower for BGD1 strain vs. all other strains, and higher for Me49 vs. BGD14 and BGD26, but did not differ among Me49 cysts of different age, or those derived from genetically different mice. The results indicate a highly uniform structure and occupancy of the different T. gondii tissue cysts. This study furthers the use of image analysis in describing the structural complexity of T. gondii cyst morphology, and presents the first application of fractal analysis for this purpose. The presented results show that use of a freely available software is a cost-effective approach to advance automated image scoring for T. gondii cysts.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Toxoplasma/citologia , Toxoplasmose Animal/patologia , Toxoplasmose Animal/parasitologia , Animais , Encéfalo/parasitologia , Encéfalo/patologia , Cistos/parasitologia , Cistos/patologia , Feminino , Fractais , Interações Hospedeiro-Parasita , Humanos , Camundongos , Camundongos Endogâmicos BALB C , Toxoplasma/patogenicidade , Toxoplasma/ultraestrutura
8.
J Cardiovasc Magn Reson ; 22(1): 56, 2020 08 03.
Artigo em Inglês | MEDLINE | ID: mdl-32753047

RESUMO

BACKGROUND: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed-ups using a deep-learning single volume super-resolution reconstruction, to recover high-resolution features from rapidly acquired low-resolution WH-bSSFP images. METHODS: A 3D residual U-Net was trained using synthetic data, created from a library of 500 high-resolution WH-bSSFP images by simulating 50% slice resolution and 50% phase resolution. The trained network was validated with 25 synthetic test data sets. Additionally, prospective low-resolution data and high-resolution data were acquired in 40 patients. In the prospective data, vessel diameters, quantitative and qualitative image quality, and diagnostic scoring was compared between the low-resolution, super-resolution and reference high-resolution WH-bSSFP data. RESULTS: The synthetic test data showed a significant increase in image quality of the low-resolution images after super-resolution reconstruction. Prospectively acquired low-resolution data was acquired ~× 3 faster than the prospective high-resolution data (173 s vs 488 s). Super-resolution reconstruction of the low-resolution data took < 1 s per volume. Qualitative image scores showed super-resolved images had better edge sharpness, fewer residual artefacts and less image distortion than low-resolution images, with similar scores to high-resolution data. Quantitative image scores showed super-resolved images had significantly better edge sharpness than low-resolution or high-resolution images, with significantly better signal-to-noise ratio than high-resolution data. Vessel diameters measurements showed over-estimation in the low-resolution measurements, compared to the high-resolution data. No significant differences and no bias was found in the super-resolution measurements in any of the great vessels. However, a small but significant for the underestimation was found in the proximal left coronary artery diameter measurement from super-resolution data. Diagnostic scoring showed that although super-resolution did not improve accuracy of diagnosis, it did improve diagnostic confidence compared to low-resolution imaging. CONCLUSION: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting. We were able to train the network using synthetic training data from retrospective high-resolution whole heart data. The resulting network can be applied very quickly, making these techniques particularly appealing within busy clinical workflow. Thus, we believe that this technique may help speed up whole heart CMR in clinical practice.


Assuntos
Aprendizado Profundo , Coração/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador , Imagem por Ressonância Magnética , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Feminino , Coração/fisiopatologia , Cardiopatias Congênitas/diagnóstico por imagem , Cardiopatias Congênitas/fisiopatologia , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Prospectivos , Reprodutibilidade dos Testes , Fatores de Tempo , Fluxo de Trabalho , Adulto Jovem
9.
J Cardiovasc Magn Reson ; 22(1): 60, 2020 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-32814579

RESUMO

BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T1 mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies. METHODS: Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T1 mapping at 1.5 T using a Probabilistic Hierarchical Segmentation (PHiSeg) network (PHCUMIS 119-127, 2019). In addition, we use the uncertainty information provided by the PHiSeg network in a novel automated quality control (QC) step to identify uncertain T1 values. The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients (N=100 for the PHiSeg network and N=700 for the QC). We used the proposed method to obtain reference T1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions. RESULTS: T1 values computed from automatic and manual segmentations were highly correlated (r=0.97). Bland-Altman analysis showed good agreement between the automated and manual measurements. The average Dice metric was 0.84 for the LV myocardium. The sensitivity of detection of erroneous outputs was 91%. Finally, T1 values were automatically derived from 11,882 CMR exams from the UK Biobank. For the healthy cohort, the mean (SD) corrected T1 values were 926.61 (45.26), 934.39 (43.25) and 927.56 (50.36) for global, interventricular septum and free-wall respectively. CONCLUSIONS: The proposed pipeline allows for automatic analysis of myocardial native T1 mapping and includes a QC process to detect potentially erroneous results. T1 reference values were presented for healthy subjects and common clinical cardiac conditions from the largest cohort to date using T1-mapping images.


Assuntos
Cardiomiopatias/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador , Imagem por Ressonância Magnética , Miocárdio/patologia , Redes Neurais de Computação , Automação , Teorema de Bayes , Cardiomiopatias/patologia , Cardiomiopatias/fisiopatologia , Estudos de Casos e Controles , Humanos , Valor Preditivo dos Testes , Controle de Qualidade , Reprodutibilidade dos Testes , Volume Sistólico , Incerteza , Função Ventricular Esquerda
10.
J Comput Assist Tomogr ; 44(5): 759-765, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32842061

RESUMO

OBJECTIVE: To compare the intravoxel incoherent motion (IVIM) parameters of rectal tumors before and after lumen distension obtained with sonography transmission gel. METHODS: Twenty-five patients were enrolled. The multiple b values of IVIM including 0, 20, 50, 100, 150, 200, 400, 600, 800, 1000, 1500, and 2000 s/mm. Two blinded readers have drawn the region of interests and calculated the D, D*, and f values. Interobserver variability between the 2 readers was measured by intraclass correlation coefficients and Altman-Bland plots. The intergroup differences of the average values were compared with the paired sample t test. RESULTS: After distention, the interrater agreement of the D* value increased obviously (from 0.547 to 0.692) and that of the D and f values increased slightly (from 0.731 and 0.618 to 0.807 and 0.666). The difference in the D value had statistical significance (P = 0.0043). CONCLUSIONS: Intraluminal distension can increase the repeatability of IVIM parameters and the value of IVIM.


Assuntos
Imagem de Difusão por Ressonância Magnética/métodos , Géis/uso terapêutico , Neoplasias Retais/diagnóstico por imagem , Reto/diagnóstico por imagem , Adulto , Meios de Contraste , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Movimento/fisiologia , Variações Dependentes do Observador , Neoplasias Retais/fisiopatologia , Reto/fisiopatologia , Ultrassonografia
11.
PLoS One ; 15(8): e0237358, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790705

RESUMO

OBJECTIVE: To clarify the relationship between amide proton transfer-weighted (APTW) signal, which reflects intracellular pH, and clinico-radiological findings in patients with hyperacute to subacute cerebral infarction. MATERIALS AND METHODS: Twenty-nine patients (median age, 70 years [IQR, 54 to 74]; 15 men) were retrospectively examined. The 10th, 25th, 50th, 75th, and 90th percentiles of APTW signal (APT10, APT25, APT50, APT75 and APT90, respectively) were measured within the infarction region-of-interest (ROI), and compared between poor prognosis and good prognosis groups (modified Rankin Scale [mRS] score ≥2 and mRS score <2, respectively). Correlations between APTW signal and time after onset, lesion size, National Institutes of Health Stroke Scale (NIHSS) score, mRS score, and mean apparent diffusion coefficient (ADC) were evaluated. RESULTS: The poor prognosis group had lower APT50, APT75, and APT90 than the good prognosis group (-0.66 [-1.19 to -0.27] vs. -0.09 [-0.62 to -0.21]; -0.27 [-0.63 to -0.01] vs. 0.31 [-0.15 to 1.06]; 0.06 [-0.21 to 0.34] vs. 0.93 [0.36 to 1.50] %; p <0.05, respectively). APT50 was positively correlated with time after onset (r = 0.37, p = 0.0471) and negatively with lesion size (r = -0.39, p = 0.0388). APT75 and APT90 were negatively correlated with NIHSS (r = -0.41 and -0.43; p <0.05, respectively). APT50, APT75 and APT90 were negatively correlated with mRS (r = -0.37, -0.52 and -0.57; p <0.05, respectively). APT10 and APT25 were positively correlated with mean ADC (r = 0.37 and 0.38; p <0.05, respectively). CONCLUSION: We demonstrated correlations between APTW signals of infarctions and clinico-radiological findings in patients with hyperacute to subacute infarctions. The poor prognosis group had a lower APTW signal than the good prognosis group. APTW signal was reduced in large infarctions, infarctions with low ADC, and in patients with high NIHSS and mRS scores.


Assuntos
Infarto Cerebral/diagnóstico , Imagem por Ressonância Magnética/métodos , Adulto , Idoso , Amidas/química , Encéfalo/diagnóstico por imagem , Infarto Cerebral/diagnóstico por imagem , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Prognóstico , Prótons , Estudos Retrospectivos
12.
Eur J Vasc Endovasc Surg ; 60(4): 539-547, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32741677

RESUMO

OBJECTIVE: The rupture of abdominal aortic aneurysms (AAAs) is associated with high mortality despite surgical developments. The determination of aneurysm diameter allows for follow up of aneurysm growth but fails in precisely predicting aneurysm rupture. In this study, time resolved three dimensional ultrasound (4D ultrasound) based wall motion indices (WMIs) are investigated to see if they are capable of distinguishing between uneven affected regions of the aneurysm wall. METHODS: In a prospective study, 56 patients with an AAA were examined using 4D ultrasound. Local longitudinal, circumferential, and shear strains were computed using custom methods. The deformation of the neck and sac of each aneurysm was characterised by statistical indices of the obtained distributions of local wall strains (WMIs): mean and peak strain, heterogeneity index, and local strain ratio. The locations of regions with highest local peak strain were determined. RESULTS: Compared with the aneurysm neck, the sac is characterised by low mean strain, but highly heterogeneous deformation, described by high local strain ratio and heterogeneity index. Differences were highly significant (p < .001) for all strain components. The regions with the highest circumferential peak strain were found more often in the posterior part of the aneurysm neck (p < .050) and sac (p < .001) regions, compared with other wall regions. No statistically significant correlation was found between the WMIs and maximum AAA diameter, except for longitudinal mean strain, which decreased with the increasing diameter (rho = -.42, p < .010). CONCLUSION: Characterisation of wall kinematics by 4D ultrasound based WMIs provides a new and independent criterion for the distinction of diseased tissue in the AAA sac and the less affected neck region. This is a promising step towards the establishment of new biomarkers to differentiate between the mechanical instability of the AAA and rupture risk.


Assuntos
Aorta Abdominal/diagnóstico por imagem , Aneurisma da Aorta Abdominal/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador , Modelos Cardiovasculares , Modelagem Computacional Específica para o Paciente , Ultrassonografia , Idoso , Idoso de 80 Anos ou mais , Aorta Abdominal/fisiopatologia , Aneurisma da Aorta Abdominal/complicações , Aneurisma da Aorta Abdominal/fisiopatologia , Ruptura Aórtica/diagnóstico por imagem , Ruptura Aórtica/etiologia , Ruptura Aórtica/fisiopatologia , Fenômenos Biomecânicos , Feminino , Hemodinâmica , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Prognóstico , Estudos Prospectivos , Fatores de Risco , Estresse Mecânico
14.
PLoS One ; 15(8): e0237213, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32797099

RESUMO

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machine learning with emphasis on modern efficient convolutional neural networks (CNNs) algorithms, for the diagnosis of prostate cancer metastasis from bone scintigraphy images. The advantageous and outstanding capabilities of deep learning, machine learning's groundbreaking technological advancement, have not yet been fully investigated regarding their application in computer-aided diagnosis systems in the field of medical image analysis, such as the problem of bone metastasis classification in whole-body scans. In particular, CNNs are gaining great attention due to their ability to recognize complex visual patterns, in the same way as human perception operates. Considering all these new enhancements in the field of deep learning, a set of simpler, faster and more accurate CNN architectures, designed for classification of metastatic prostate cancer in bones, is explored. This research study has a two-fold goal: to create and also demonstrate a set of simple but robust CNN models for automatic classification of whole-body scans in two categories, malignant (bone metastasis) or healthy, using solely the scans at the input level. Through a meticulous exploration of CNN hyper-parameter selection and fine-tuning, the best architecture is selected with respect to classification accuracy. Thus a CNN model with improved classification capabilities for bone metastasis diagnosis is produced, using bone scans from prostate cancer patients. The achieved classification testing accuracy is 97.38%, whereas the average sensitivity is approximately 95.8%. Finally, the best-performing CNN method is compared to other popular and well-known CNN architectures used for medical imaging, like VGG16, ResNet50, GoogleNet and MobileNet. The classification results show that the proposed CNN-based approach outperforms the popular CNN methods in nuclear medicine for metastatic prostate cancer diagnosis in bones.


Assuntos
Neoplasias Ósseas/secundário , Redes Neurais de Computação , Neoplasias da Próstata/patologia , Imagem Corporal Total/métodos , Neoplasias Ósseas/classificação , Neoplasias Ósseas/diagnóstico por imagem , Diagnóstico por Computador/métodos , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Masculino , Cintilografia/métodos , Software
15.
PLoS One ; 15(8): e0237587, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32804986

RESUMO

In radiomics studies, researchers usually need to develop a supervised machine learning model to map image features onto the clinical conclusion. A classical machine learning pipeline consists of several steps, including normalization, feature selection, and classification. It is often tedious to find an optimal pipeline with appropriate combinations. We designed an open-source software package named FeAture Explorer (FAE). It was programmed with Python and used NumPy, pandas, and scikit-learning modules. FAE can be used to extract image features, preprocess the feature matrix, develop different models automatically, and evaluate them with common clinical statistics. FAE features a user-friendly graphical user interface that can be used by radiologists and researchers to build many different pipelines, and to compare their results visually. To prove the effectiveness of FAE, we developed a candidate model to classify the clinical-significant prostate cancer (CS PCa) and non-CS PCa using the PROSTATEx dataset. We used FAE to try out different combinations of feature selectors and classifiers, compare the area under the receiver operating characteristic curve of different models on the validation dataset, and evaluate the model using independent test data. The final model with the analysis of variance as the feature selector and linear discriminate analysis as the classifier was selected and evaluated conveniently by FAE. The area under the receiver operating characteristic curve on the training, validation, and test dataset achieved results of 0.838, 0.814, and 0.824, respectively. FAE allows researchers to build radiomics models and evaluate them using an independent testing dataset. It also provides easy model comparison and result visualization. We believe FAE can be a convenient tool for radiomics studies and other medical studies involving supervised machine learning.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Neoplasias da Próstata/diagnóstico por imagem , Humanos , Masculino , Imageamento por Ressonância Magnética Multiparamétrica , Curva ROC , Software , Aprendizado de Máquina Supervisionado
16.
Ann R Coll Surg Engl ; 102(8): 577-580, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32777930

RESUMO

INTRODUCTION: An increasing quantity of data is required to guide precision medicine and advance future healthcare practices, but current analytical methods often become overwhelmed. Artificial intelligence (AI) provides a promising solution. Plastic surgery is an innovative surgical specialty expected to implement AI into current and future practices. It is important for all plastic surgeons to understand how AI may affect current and future practice, and to recognise its potential limitations. METHODS: Peer-reviewed published literature and online content were comprehensively reviewed. We report current applications of AI in plastic surgery and possible future applications based on published literature and continuing scientific studies, and detail its potential limitations and ethical considerations. FINDINGS: Current machine learning models using convolutional neural networks can evaluate breast mammography and differentiate benign and malignant tumours as accurately as specialist doctors, and motion sensor surgical instruments can collate real-time data to advise intraoperative technical adjustments. Centralised big data portals are expected to collate large datasets to accelerate understanding of disease pathogeneses and best practices. Information obtained using computer vision could guide intraoperative surgical decisions in unprecedented detail and semi-autonomous surgical systems guided by AI algorithms may enable improved surgical outcomes in low- and middle-income countries. Surgeons must collaborate with computer scientists to ensure that AI algorithms inform clinically relevant health objectives and are interpretable. Ethical concerns such as systematic biases causing non-representative conclusions for under-represented patient groups, patient confidentiality and the limitations of AI based on the quality of data input suggests that AI will accompany the plastic surgeon, rather than replace them.


Assuntos
Inteligência Artificial , Interpretação de Imagem Assistida por Computador , Procedimentos Cirúrgicos Reconstrutivos , Big Data , Mama/diagnóstico por imagem , Mama/cirurgia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/cirurgia , Feminino , Humanos , Mamografia
17.
Virchows Arch ; 477(4): 475-486, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32833038

RESUMO

Since digital microscopy (DM) has become a useful alternative to conventional light microscopy (CLM), several approaches have been used to evaluate students' performance and perception. This systematic review aimed to integrate data regarding the use of DM for education in human pathology, determining whether this technology can be an adequate learning tool, and an appropriate method to evaluate students' performance. Following a specific search strategy and eligibility criteria, three electronic databases were searched and several articles were screened. Eight studies involving medical and dental students were included. The test of performance comprised diagnostic and microscopic description, clinical features, differential, and final diagnoses of the specimens. The students' achievements were equivalent, similar or higher using DM in comparison with CLM in four studies. All publications employed question surveys to assess the students' perceptions, especially regarding the easiness of equipment use, quality of images, and preference for one method. Seven studies (87.5%) indicated the students' support of DM as an appropriate method for learning. The quality assessment categorized most studies as having a low bias risk (75%). This study presents the efficacy of DM for human pathology education, although the high heterogeneity of the included articles did not permit outlining a specific method of performance evaluation.


Assuntos
Instrução por Computador , Educação em Odontologia/métodos , Educação Médica/métodos , Interpretação de Imagem Assistida por Computador , Microscopia , Patologia/educação , Competência Clínica , Currículo , Escolaridade , Humanos , Internato e Residência , Aprendizagem , Estudantes de Odontologia , Estudantes de Medicina
18.
Eur J Radiol ; 130: 109202, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32745895

RESUMO

BACKGROUND: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. PURPOSE: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. METHODS: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. RESULTS: 189 patients were analyzed. PaO2/FIO2 ratio and oxygen saturation (SaO2) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO2/FIO2 ratio and SaO2 (p < 0.05). CONCLUSION: Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.


Assuntos
Betacoronavirus , Infecções por Coronavirus/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Idoso , Estado Terminal , Estudos de Avaliação como Assunto , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pandemias , Projetos de Pesquisa , Estudos Retrospectivos , Fatores de Risco , Sensibilidade e Especificidade
19.
Curr Opin Ophthalmol ; 31(5): 312-317, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32694266

RESUMO

PURPOSE OF REVIEW: In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside. RECENT FINDINGS: In the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to 'deep' convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows. SUMMARY: Real-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.


Assuntos
Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Retinopatia da Prematuridade/diagnóstico , Algoritmos , Humanos , Recém-Nascido , Aprendizado de Máquina , Redes Neurais de Computação
20.
IEEE Trans Med Imaging ; 39(8): 2595-2605, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32730212

RESUMO

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Pneumonia Viral/diagnóstico por imagem , Algoritmos , Betacoronavirus , Infecções Comunitárias Adquiridas/diagnóstico por imagem , Humanos , Pandemias , Curva ROC , Radiografia Torácica , Tomografia Computadorizada por Raios X
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA