scholarly journals Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: a preliminary report

2020 ◽  
Vol 8 (3) ◽  
pp. 317-326
Author(s):  
Grigory A. Lein ◽  
Natalia S. Nechaeva ◽  
Gulnar М. Mammadova ◽  
Andrey A. Smirnov ◽  
Maxim M. Statsenko
Keyword(s):  
Neural Network ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
X Rays ◽  
X Ray ◽  
Scoliotic Deformity ◽  
Window Method ◽  
Treatment Type

Background. A large number of studies have focused on automating the process of measuring the Cobb angle. Although there is no practical tool to assist doctors with estimating the severity of the curvature of the spine and determine the best suitable treatment type. Aim. We aimed to examine the algorithms used for distinguishing vertebral column, vertebrae, and for building a tangent on the X-ray photographs. The superior algorithms should be implemented into the clinical practice as an instrument of automatic analysis of the spine X-rays in scoliosis patients. Materials and methods. A total of 300 digital X-rays of the spine of children with idiopathic scoliosis were gathered. The X-rays were manually ruled by a radiologist to determine the Cobb angle. This data was included into the main dataset used for training and validating the neural network. In addition, the Sliding Window Method algorithm was implemented and compared with the machine learning algorithms, proving it to be vastly superior in the context of this research. Results. This research can serve as the foundation for the future development of an automated system for analyzing spine X-rays. This system allows processing of a large amount of data for achieving 85% in training neural network to determine the Cobb angle. Conclusions. This research is the first step toward the development of a modern innovative product that uses artificial intelligence for distinguishing the different portions of the spine on 2D X-ray images for building the lines required to determine the Cobb angle.

An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
P. Srinivasa Rao ◽  
Pradeep Bheemavarapu ◽  
P. S. Latha Kalyampudi ◽  
T. V. Madhusudhana Rao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Chronic Respiratory Disease ◽  
Automated System ◽  
X Rays ◽  
Medical Problems ◽  
X Ray ◽  
Chest X Ray ◽  
Fully Connected

Background: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of the coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. Objective: This paper proposes a deep learning model for classification of coronavirus infected patient detection using chest X-ray radiographs. Methods: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with rectified linear unit, softmax (last layer) activation functions and max pooling layers which were trained using the publicly available COVID-19 dataset. Results and Conclusion: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting COVID-19 and normal patient’s images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

scholarly journals May bending radiographs be replaced by recumbent CT scans in patients with adolescent idiopathic scoliosis?

2021 ◽  
Author(s):  
Max Prost ◽  
Joachim Windolf ◽  
Markus Rafael Konieczny
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Strong Correlation ◽  
Ct Scans ◽  
X Rays ◽  
Exclusion Criteria ◽  
X Ray ◽  
A Minor ◽  
Main Curve

Abstract Purpose There is no data that show if it is possible to determine if a curve is structural or non-structural or to assess flexibility of an adolescent idiopathic scoliosis (AIS) by recumbent images like a CT scan (CTS) instead of bending radiographs (BR). We investigated if the results of BR may be compared to those of CTS. Methods We retrospectively analyzed prospectively collected data of patients with AIS in whom a selective spinal fusion was performed and in whom a CTS, BR, and full spine x-rays were made preoperatively. We measured the Cobb angles of the main and the minor curve in full spine x-ray, BR, and CTS. Results After applying inclusion and exclusion criteria, 39 patients were included. We found a strong correlation (r = 0.806, p < 0.01) between the Cobb angle of the main curve in BR and the Cobb angle of the main curve in the CTS and between the Cobb angle of the minor curve in BR and the Cobb angle of the minor curve in the CTS (r = 0.601, p < 0.01). All patients with a minor curve of less than 25 degrees in the BR had a Cobb angle of less than 35 degrees in the CTS. Conclusion Spinal curves showed a significant correlation between bending radiographs and recumbent images (CTS). In our group of patients, a Cobb angle of the minor curve of less than 35 degrees in the CTS indicated that this minor curve was non-structural.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

scholarly journals Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 548
Author(s):  
Mateus Maia ◽  
Jonatha S. Pimentel ◽  
Ivalbert S. Pereira ◽  
João Gondim ◽  
Marcos E. Barreto ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Predictive Performance ◽  
Polynomial Kernel ◽  
Support Vector ◽  
X Rays ◽  
X Ray ◽  
Applied Study ◽  
The World ◽  
Simulated Images

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel (CSVMPol) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.

scholarly journals IN-BRACE CORRECTION (IBC) TERHADAP TIPE DAN BESARAN KURVA PADA PASIEN ADOLESCENT IDIOPATHIC SKOLIOSIS (AIS)

2020 ◽  
Vol 14 (1) ◽  
pp. 46-52
Author(s):  
Raden Candra ◽  
Fika Trifani
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
P Value ◽  
X Ray

Skoliosis adalah kelengkungan tulang belakang ke lateral yang melebihi 10 derajat. Tinjauan lapangan pada klinik dan rumah sakit di Indonesia menunjukan banyaknya kasus pasien Adolescent Idiopathic Scoliosis (AIS) yang telah ditangani dengan penggunaan skoliosis brace. In-brace correction (IBR) merupakan cara menilai kualitas skoliosis brace secara cepat setelah brace dipasangkan kepada pasien dengan metode X-Ray dengan menggunakan brace. Akan tetapi, hasil IBR tersebut sering ditemukan berbeda dari satu pasien dengan yang lainnya sehingga dibutuhkan untuk mengetahui faktor yang dapat menyebabkan perbedaan tersebut. Oleh karena itu, tujuan pada penelitian ini adalah untuk menilai apakah terdapat hubungan antara tipe kurva dan besaran kurva terhadap IBR pada pasien AIS. Analisis retrospective sebanyak 120 data sekunder telah digunakan dalam penelitian ini melalui rekam medis pasien yang menggunakan scoliosis brace dari tahun 2016 - 2018. Data yang diambil berupa Cobb angle tanpa menggunakan brace, In-Brace Cobb angle, dan tipe kurva skoliosis. Rata-rata IBR adalah 56,0% pada besaran kurva ringan (20°-29°), 37,2% pada besaran kurva sedang (30° - 40°), 36,7% pada besaran kurva parah (>40°). Sedangkan, rata-rata IBR tertinggi adalah pada tipe kurva ganda dimana lumbar > thoraks yaitu sebesar 50,3%, lalu disusul dengan kurva tunggal thoraks dan kurva ganda thoraks > lumbar sebesar 40,3% dan 39,1% secara berurutan. terdapat perbedaan yang signifikan IBR bedasarkan Besaran Kurva dan Tipe Kurva pada pasien adolescent idiopatik skoliosis dengan p value 0,000 dan 0,029 secara berurutan. Dapat disimpulkan bahwa tipe dan besaran kurva scoliosis merupakan faktor yang dapat mempengaruhi hasil IBR secara signifikan

scholarly journals Identifying drug-resistant tuberculosis from chest X-ray images using a simple convolutional neural network

2021 ◽  
Vol 2071 (1) ◽  
pp. 012001
Author(s):  
J Ureta ◽  
A Shrestha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Health Concern ◽  
Drug Resistant ◽  
X Rays ◽  
Drug Resistant Tuberculosis ◽  
X Ray ◽  
Resistant Tuberculosis ◽  
Chest X Ray

Abstract Tuberculosis(TB) is one of the top 10 causes of death worldwide, and drug-resistant TB is a major public health concern especially in resource-constrained countries. In such countries, molecular diagnosis of drug-resistant TB remains a challenge; and imaging tools such as X-rays, which are cheaply and widely available, can be a valuable supplemental resource for early detection and screening. This study uses a specialized convolutional neural network to perform binary classification of chest X-ray images to classify drug-resistant and drug-sensitive TB. The models were trained and validated using the TBPortals dataset which contains 2,973 labeled X-ray images from TB patients. The classifiers were able to identify the presence or absence of drug-resistant Tuberculosis with an AUROC between 0.66–0.67, which is an improvement over previous attempts using deep learning networks.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals Lack of In-Brace X-Rays in Compliant AIS Patients Wearing Full-Time TLSO Braces Associates with Failure

2021 ◽  
Author(s):  
Isabel Alvarez ◽  
Kiley Poppino ◽  
Lori Karol ◽  
Amy L McIntosh
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Successful Treatment ◽  
Univariate Analysis ◽  
Full Time ◽  
X Rays ◽  
Skeletally Immature ◽  
X Ray ◽  
Total Treatment ◽  
Registration Date

Abstract BackgroundIn-brace correction and brace compliance with Thoraco-Lumbo-Sacral Orthotic (TLSO) braces are associated with successful treatment of Adolescent Idiopathic Scoliosis (AIS). This paper compares patients who had consistent radiographic documentation of in-brace correction to those who did not.MethodsAll skeletally immature (Risser 0–2) patients were treated for AIS (25°-45°) with full-time TLSO braces that had compliance temperature monitors. All patients wore their braces at least 12 hours a day. Brace failure was defined as curve progression to a surgical magnitude (≥ 50°). All patients were followed until brace discontinuation.Results90 patients (F:82, M:8) with an average age of 12.1(10.1–15.0) years, Risser grade 0(0–2), BMI percentile 48.5(0.0-98.8), and daily brace wear of 16.5(12.1–21.6) hrs/day were treated for 24.3(8.0-66.6) months. Patients went through 1.7(1–4) braces on average. 42/90(46.7%) patients had some amount of brace time with an unknown in-brace correction, which, on average, was 66.1% of their total treatment course (11.5–100). On univariate analysis, patients that did not have a repeat in-brace x-ray with major brace adjustments or new brace fabrication tended to be more skeletally immature (Risser 0 and tri-radiate open, p = 0.028), wear more braces throughout their treatment (2.0 vs 1.4, p < 0.001), were treated for a longer period of time (27 vs 22 months, p = 0.022), and failed bracing more often (47.6% vs 22.9%, p = 0.014).ConclusionsPatients who did not have new in-brace x-rays with major brace adjustments and/or new brace fabrication were 3.1(95% CI 1.2–7.6) times more likely to fail bracing than patients who were re-checked with new in-brace x-rays.Trial Registration:ClinicalTrials.gov - NCT02412137, Initial Registration Date April 2015

scholarly journals Problems with diagnosing early stadia of scoliosis due to discrepancies between ATR and the Cobb angle

2020 ◽  
Author(s):  
Marek Kluszczyński ◽  
Jacek Wąsik ◽  
Dorota Ortenburger
Keyword(s):  
Idiopathic Scoliosis ◽  
Cobb Angle ◽  
Lumbar Lordosis ◽  
Thoracic Kyphosis ◽  
Clinical Parameters ◽  
Trunk Rotation ◽  
X Ray ◽  
False Diagnosis ◽  
Cobb Method ◽  
Threshold Rule

Abstract Background This research analysed discrepancies between the angle of trunk rotation (ATR) and the Cobb angle, in order to study if the commonly used 7° cut-off threshold for ATR helps diagnose scoliosis. In early stadia of scoliosis in children, ATR and the Cobb angle often disagree, increasing the risk of a false diagnosis: while the former does not suggest scoliosis, the latter does. Methods The study analysed ATR clinical parameters and the Cobb angle in the X-ray pictures of 117 (23 boys and 94 girls, aged 6–17 years) children who had not yet started treatment and whose X-ray pictures showed the Cobb angle of at least 10°, indicating idiopathic scoliosis. The degrees of lumbar lordosis and thoracic kyphosis were measured using the Saunders inclinometer, and back asymmetry was measured with Adam’s forward bend test using the Bunnell scoliometer. In the X-ray pictures, the curvature angle was plotted according to the Cobb method. The patients were stratified based on their age, and their ATRs and Cobb angles were compared. Results Although all the children had the Cobb angle over 10°, in 69 out of 117 (59%), ATR was below 7%. So, using the 7° cut-off threshold rule, scoliosis would not be diagnosed in those children. This shows that the two tests often disagree, suggesting that the 7° cut-off threshold or ATR is ineffective in diagnosing scoliosis. Conclusions To improve the method for diagnosing scoliosis based on ATR, consideration should be given to lowering the 7° ATR cut-off threshold.

scholarly journals Deep COVID 19: Deep Learning for COVID 19 Detection from X ray Images

2021 ◽  
Vol 11 (1) ◽  
pp. 1-6
Author(s):  
Ahmed Hashem El Fiky ◽  
Keyword(s):  
Machine Learning Algorithms ◽  
Diagnostic Process ◽  
X Rays ◽  
Virus Spread ◽  
Large Dataset ◽  
X Ray ◽  
Support Mechanism ◽  
Proposed Model ◽  
Speed Up ◽  
First Time

The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy.

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