Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the n展开更多
Objective: The aim of this prospective study is <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">to </span>...Objective: The aim of this prospective study is <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">evaluate how much damage the patellar cartilage presents during a total knee replacement. Methods: The damage of the articular patellar surface was analysed by visual inspection and photographs in 354 primary total knee replacement</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">. The authors graded the degree of cartilage lesion in five groups. The cartilage status was analyzed and correlated with age, gender, side, body mass index (BMI), Kellgren-Lawrence radiographic scale and axial deviation. Results: After statistical analysis, we concluded: there was no evidence of an association between patellar arthrosis and age gender, side, weight and deformity. Conclusions: Articular cartilage was damaged in all 354 knees. Important subchondral bone exposure occurred in 274 knees (77</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">4%). Obese patients had more severe patellar osteoarthritis.</span></span></span>展开更多
<strong></strong><strong>Objective(s):</strong> The aim of this study is to explore if there is a correlation between the typical voice classification and the oropharyngeal and laryngeal morpho...<strong></strong><strong>Objective(s):</strong> The aim of this study is to explore if there is a correlation between the typical voice classification and the oropharyngeal and laryngeal morphology, using video laryngeal stroboscopy and cervical posterior-anterior radiography on professional singers in Greece. <strong>Methods:</strong> 55 professional singers (28 females: 7 sopranos, 12 mezzo-sopranos, and 9 contraltos;27 males: 8 tenors, 12 baritones and 7 basses) were recruited for this study. All participants underwent stroboscopic and cervical posterior-anterior radiographic imaging of their oral pharyngeal and laryngeal area. Additionally, the voice classification and features (e.g., height, weight) of individuals were correlated statistically. <strong>Results:</strong> Statistically significant correlations were observed between the VC of the participants with the Phonetic Area (PA) (r = −0.451, p = 0.001) and the VC with the Oral-pharyngeal Cavity (OPC) area (r = −0.402, p = 0.001) in the total sample. Specifically, in male singers, the PA and VC correlation was r = −0.319, p = 0.047, and the VC and OPC area was r = −0.328, p = 0.044. Likewise, in female singers, the PA area and VC and PA were r = −0.336, p = 0.041 and the OPC area and VC were r = −0.344, p = 0.039. The analysis confirmed no correlations between VC and height and body weight. <strong>Conclusions:</strong> The cervical posteroanterior radiography in conjunction with laryngeal stroboscopy provided new morphometric correlations of the VC of professional singers with their Oropharyngeal and Laryngeal Anatomy.展开更多
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the n
文摘Objective: The aim of this prospective study is <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">evaluate how much damage the patellar cartilage presents during a total knee replacement. Methods: The damage of the articular patellar surface was analysed by visual inspection and photographs in 354 primary total knee replacement</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">. The authors graded the degree of cartilage lesion in five groups. The cartilage status was analyzed and correlated with age, gender, side, body mass index (BMI), Kellgren-Lawrence radiographic scale and axial deviation. Results: After statistical analysis, we concluded: there was no evidence of an association between patellar arthrosis and age gender, side, weight and deformity. Conclusions: Articular cartilage was damaged in all 354 knees. Important subchondral bone exposure occurred in 274 knees (77</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">4%). Obese patients had more severe patellar osteoarthritis.</span></span></span>
文摘<strong></strong><strong>Objective(s):</strong> The aim of this study is to explore if there is a correlation between the typical voice classification and the oropharyngeal and laryngeal morphology, using video laryngeal stroboscopy and cervical posterior-anterior radiography on professional singers in Greece. <strong>Methods:</strong> 55 professional singers (28 females: 7 sopranos, 12 mezzo-sopranos, and 9 contraltos;27 males: 8 tenors, 12 baritones and 7 basses) were recruited for this study. All participants underwent stroboscopic and cervical posterior-anterior radiographic imaging of their oral pharyngeal and laryngeal area. Additionally, the voice classification and features (e.g., height, weight) of individuals were correlated statistically. <strong>Results:</strong> Statistically significant correlations were observed between the VC of the participants with the Phonetic Area (PA) (r = −0.451, p = 0.001) and the VC with the Oral-pharyngeal Cavity (OPC) area (r = −0.402, p = 0.001) in the total sample. Specifically, in male singers, the PA and VC correlation was r = −0.319, p = 0.047, and the VC and OPC area was r = −0.328, p = 0.044. Likewise, in female singers, the PA area and VC and PA were r = −0.336, p = 0.041 and the OPC area and VC were r = −0.344, p = 0.039. The analysis confirmed no correlations between VC and height and body weight. <strong>Conclusions:</strong> The cervical posteroanterior radiography in conjunction with laryngeal stroboscopy provided new morphometric correlations of the VC of professional singers with their Oropharyngeal and Laryngeal Anatomy.
