scholarly journals Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shengkai Mu ◽  
Jingxu Wang ◽  
Shuyi Gong
Keyword(s):  
Neural Network ◽  
Energy Spectrum ◽  
Medical Imaging ◽  
Negative Group ◽  
Mineral Density ◽  
Lumbar Degenerative Disease ◽  
Diagnosis Of Osteoporosis ◽  
Positive Group ◽  
X Ray ◽  
Good Consistency

Objective. To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy spectrum electronic computed tomography (CT) medical imaging based on the deep convolutional neural network (CNN) in the treatment of lumbar degenerative disease and osteoporosis. Methods. There were 56 cases of suspected lumbar degenerative disease and osteoporosis. A group of 56 subjects were examined using 1.5 TMR spectrum (MRS) and dual-energy X-ray absorptiometry (DXA) to collect the lumbar L3 vertebral body fat ratio (FF) and L1~4 vertebral bone mineral density (BMD) value. We divided the subjects into 2 groups with T value -2.5 as the critical point. Set T value > -2.5 as the negative group and T value ≤ -2.5 as the positive group. Pearson’s method is used for FF-MRS and BMD correlation analyses. A group of all patients underwent GSI-energy spectrum CT scan, and X-ray bone mineral density (DXA) test results (bone density per unit area) were used as the gold standard to analyze the diagnosis of osteoporosis by the GSI-energy spectrum CT scan method value. Results. The differences in FF and BMD between the negative group and the positive group were statistically significant ( P < 0.01 ), and there was a highly negative correlation between the average value of FF and BMD. 30 cases were diagnosed as osteoporosis by DXA. The accuracy of GSI-energy spectrum CT medical imaging in diagnosing osteoporosis is 89.30%. The GSI-energy spectrum CT diagnosis of osteoporosis and DXA examination results have good consistency. Conclusion. Based on the deep convolutional neural network (CNN) MRS technology, GSI-energy spectrum CT medical imaging is used in the clinical diagnosis and treatment of lumbar degenerative lesions and osteoporosis. It has a good advantage in assessing bone quality and has good consistency with DXA examination and has better application value high.

scholarly journals Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images

2021 ◽  
Vol 10 ◽  
Author(s):  
Min Seob Kwak ◽  
Hun Hee Lee ◽  
Jae Min Yang ◽  
Jae Myung Cha ◽  
Jung Won Jeon ◽  
...  
Keyword(s):  
Neural Network ◽  
Colon Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Cancer Tissue ◽  
Negative Group ◽  
Positive Group ◽  
Node Metastasis

BackgroundHuman evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.MethodsWe developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim, Germany. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed.ResultsA total of 164 patients with stages I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (± SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (P &lt; 0.001). In the univariate analyses, the PTS scores for the LNM-positive group were significantly higher than those for the LNM-negative group (P &lt; 0.001). Further, the PTS scores in lymphatic invasion and any one of perineural, lymphatic, or venous invasion were significantly increased in the LNM-positive group (P &lt; 0.001 and P &lt; 0.001).ConclusionWe established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large-scale prospective clinical trials.

scholarly journals Classification among Image Enhancement Techniques for Computed Tomography scan by using CancerNet neural network

2021 ◽  
Vol 12 (3) ◽  
pp. 4938-4941
Author(s):  
SatyasangramSahoo Et. al.
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Medical Imaging ◽  
Network Structure ◽  
Medical Image ◽  
Computed Tomography Imaging ◽  
X Ray ◽  
Tomography Imaging ◽  
Neural Network Structure ◽  
Tomography Scan

Enhancement of cancerous images is a vital section of image preprocessing for Computed Tomography imaging classification. The combination of computer added pictures in X-ray is widely used for medical imaging. Basic enhancement techniques like Pixel wise Enhancements and Local operator based operation on computed Tomography (C.T.) scan are mainly used in preprocessing by using an artificially based model of the medical imaging. The study is focused on selecting the better among basic enhancement methods by using the cancerNet neural network structure. Whereas CancerNet is a widely used Convolutional neural Network structure for classification based study for cancerous medical image.

Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chan-Shien Ho ◽  
Yueh-Peng Chen ◽  
Tzuo-Yau Fan ◽  
Chang-Fu Kuo ◽  
Tzu-Yun Yen ◽  
...  
Keyword(s):  
Neural Network ◽  
Bone Mineral Density ◽  
Deep Learning ◽  
Bone Mineral ◽  
Mineral Density ◽  
X Ray ◽  
Deep Learning Neural Network

Diagnosis of Osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images

2020 ◽  
Vol 28 (5) ◽  
pp. 953-973 ◽  
Author(s):  
S.M. Nazia Fathima ◽  
R. Tamilselvi ◽  
M. Parisa Beham ◽  
D. Sabarinathan
Keyword(s):  
Bone Mineral Density ◽  
Classification Accuracy ◽  
Standard Technique ◽  
Dual Energy ◽  
Mineral Density ◽  
Medical Field ◽  
Current Standard ◽  
Diagnosis Of Osteoporosis ◽  
X Ray ◽  
T Score

BACKGROUND: Osteoporosis, a silent killing disease of fracture risk, is normally determined based on the bone mineral density (BMD) and T-score values measured in bone. However, development of standard algorithms for accurate segmentation and BMD measurement from X-ray images is a challenge in the medical field. OBJECTIVE: The purpose of this work is to more accurately measure BMD from X-ray images, which can overcome the limitations of the current standard technique to measure BMD using Dual Energy X-ray Absorptiometry (DEXA) such as non-availability and inaccessibility of DEXA machines in developing countries. In addition, this work also attempts to analyze the DEXA scan images for better segmentation and measurement of BMD. METHODS: This work employs a modified U-Net with Attention unit for accurate segmentation of bone region from X-Ray and DEXA images. A linear regression model is developed to compute BMD and T-score. Based on the value of T-score, the images are then classified as normal, osteopenia or osteoporosis. RESULTS: The proposed network is experimented with the two internally collected datasets namely, DEXSIT and XSITRAY, comprised of DEXA and X-ray images, respectively. The proposed method achieved an accuracy of 88% on both datasets. The Dice score on DEXSIT and XSITRAY is 0.94 and 0.92, respectively. CONCLUSION: Our modified U-Net with attention unit achieves significantly higher results in terms of Dice score and classification accuracy. The computed BMD and T-score values of the proposed method are also compared with the respective clinical reports for validation. Hence, using the digitized X-Ray images can be used to detect osteoporosis efficiently and accurately.

scholarly journals Modern approaches and possibilities for assessing bone mineral density by quantitative computed tomography (literature review)

2021 ◽  
Vol 23 (4) ◽  
pp. 372-381
Author(s):  
Aleksandr A. Melnikov ◽  
◽  
Viktor V. Diachenko ◽  
Igor V. Shubin ◽  
Aleksei E. Nikitin ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Bone Mineral Density ◽  
Bone Mineral ◽  
Imaging Techniques ◽  
Quantitative Computed Tomography ◽  
Dual Energy ◽  
Mineral Density ◽  
Diagnosis Of Osteoporosis ◽  
X Ray ◽  
Clinically Significant

The review provides the literature data on the basal issues of bone remodeling and the applied use of medical imaging techniques for the prevention of clinically significant consequences of osteoporosis. The article discusses the role and prospects of using the method of quantitative computed tomography and its modifications for the diagnosis of osteoporosis and osteopenic syndrome. It considers the advantages of quantitative computed tomography over widely used medical techniques for assessing bone mineral density (mono- and dual-energy X-ray absorptiometry, mono- and dual-energy isotope absorptiometry).

scholarly journals Different Tools for the Assessment of Bone Mass among Egyptian Adults

2014 ◽  
Vol 2 (4) ◽  
pp. 557-561
Author(s):  
Nayera E. Hassan ◽  
Sahar A. El-Masry ◽  
Rokia A. El-Banna ◽  
Mohamed S. El Hussieny
Keyword(s):  
Gold Standard ◽  
Detection Rate ◽  
Assessment Tool ◽  
Quantitative Computed Tomography ◽  
Mineral Density ◽  
Self Assessment ◽  
Cross Sectional ◽  
Diagnosis Of Osteoporosis ◽  
X Ray ◽  
Better Than

BACKGROUND: Several tools such as, dual X-ray absorptiometry (DXA), quantitative computed tomography (QCT) and self-assessment tool (OST), are being used for diagnosis of osteoporosis.OBJECTIVE: to compare the sensitivity and specify detection rate of bone mineral density (BMD) changes for DXA versus QCT and OST among a sample of Egyptian adults of both sexes.SUBJECTS AND METHODS: This study is a cross sectional one, which included 62 Egyptians, aged 20-65 years.  Each individual was assessed for BMD using DXA at femur and spine sites; QCT and OST which take into account body weight and age. Accordingly they were diagnosed as either osteoporotic/osteopenic or normal.RESULTS: The highest prevalence of osteopenia or osteoporosis was diagnosed among menopause women. DXA at femur has diagnosed more cases of osteoporosis (both osteopenic and osteoporotic) as compared to spine DXA or QCT, but OST is out of rang; as it failed to diagnose any case.CONCLUSION: DXA has been found to be more efficacious than QCT scan in the diagnosis of osteoporosis. DXA in femur is better than DXA-spine and QCT. Generally, DXA is the "gold standard" when assessing osteoporosis. Further studies are needed to modify the equation of OST and confirm its efficiency in Egyptians population.

scholarly journals Pneumocare: A Website for Detecting Pneumonia

2021 ◽  
Vol 9 (11) ◽  
pp. 2021-2024
Author(s):  
Aleena Syed
Keyword(s):  
Neural Network ◽  
Medical Imaging ◽  
Convolutional Neural Network ◽  
X Ray ◽  
Fundamental Step ◽  
Human Lungs ◽  
Chest X Ray

Abstract: Pneumonia is a form of a respiration contamination that impacts the lungs. In those acute breathing sicknesses, human lungs which can be made from small sacs referred to as alveoli which can be in air in everyday and wholesome people however in pneumonia those alveoli get filled with fluid or "pus” one of the fundamental step of phenomena detection and treatment is getting the chest X-ray of the (CXR). So Chest X-ray is a first-rate tool in treating pneumonia, similarly to many alternatives taken with the aid of the usage of doctor are dependent on the chest X-ray. Our venture is ready detection of Pneumonia by means of chest X-ray using Convolutional Neural network. on this undertaking, we are able to look at the abilties of 2nd medical imaging to investigate records from the NIH Chest X-ray Dataset and educate a CNN to classify a given chest x-ray for the presence or absence of pneumonia. Keywords: alveoli, CNN, NIH

scholarly journals THE VALUE OF OSTEOPONTIN AS A DIAGNOSTIC BIOMARKER IN POSTMENOPAUSAL OSTEOPOROSIS; A REVIEW.

2020 ◽  
Vol 12 ◽  
pp. 27
Author(s):  
Amira A. Atta ◽  
Keyword(s):  
Bone Mineral Density ◽  
Early Detection ◽  
Postmenopausal Osteoporosis ◽  
Bone Mineral ◽  
Osteoporotic Fractures ◽  
Dual Energy ◽  
Diagnostic Biomarker ◽  
Mineral Density ◽  
Diagnosis Of Osteoporosis ◽  
X Ray

Although increased awareness of morbidity and costs related to osteoporotic fractures, real progress achieved only through early detection of osteoporosis before any fractures occur. Dual Energy X-ray Absorptiometry (DEXA) is commonly used for diagnosis of osteoporosis by measuring bone mineral density (BMD

scholarly journals Convolutional Neural Network-Based Humerus Segmentation and Application to Bone Mineral Density Estimation from Chest X-ray Images of Critical Infants

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1028
Author(s):  
Yung-Chun Liu ◽  
Yung-Chieh Lin ◽  
Pei-Yin Tsai ◽  
Osuke Iwata ◽  
Chuew-Chuen Chuang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bone Mineral Density ◽  
Convolutional Neural Network ◽  
Bone Mineral ◽  
Premature Infants ◽  
Clinical Care ◽  
Dice Similarity Coefficient ◽  
X Rays ◽  
Mineral Density ◽  
X Ray

Measuring bone mineral density (BMD) is important for surveying osteopenia in premature infants. However, the clinical availability of dual-energy X-ray absorptiometry (DEXA) for standard BMD measurement is very limited, and it is not a practical technique for critically premature infants. Developing alternative approaches for DEXA might improve clinical care for bone health. This study aimed to measure the BMD of premature infants via routine chest X-rays in the intensive care unit. A convolutional neural network (CNN) for humeral segmentation and quantification of BMD with calibration phantoms (QRM-DEXA) and soft tissue correction were developed. There were 210 X-rays of premature infants evaluated by this system, with an average Dice similarity coefficient value of 97.81% for humeral segmentation. The estimated humerus BMDs (g/cm3; mean ± standard) were 0.32 ± 0.06, 0.37 ± 0.06, and 0.32 ± 0.09, respectively, for the upper, middle, and bottom parts of the left humerus for the enrolled infants. To our knowledge, this is the first pilot study to apply a CNN model to humerus segmentation and to measure BMD in preterm infants. These preliminary results may accelerate the progress of BMD research in critical medicine and assist with nutritional care in premature infants.

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