scholarly journals Detecting the Disease before it’s Too Late

2021 ◽  
pp. 82-89
Author(s):  
. Utkarsh
Keyword(s):  
Medical Industry ◽  
Modern Technology ◽  
Special Test ◽  
Development Cost ◽  
Security Risk ◽  
Protective Measures ◽  
Early Detection Of Disease ◽  
Artery Disease ◽  
Test Result ◽  
Global Data

Specific trials or examination exists in detecting deadly diseases. Various facilities are provided these days for determining many chronic diseases, like in detecting cancer, tuberculosis, keratin level (dialysis), coronary artery disease, etc.   But delaying in diagnosing emergency cases which need very quick action will lead to adverse situations. The process of this early detection of disease starts with a special test and further process depends on the special test result whether it is positive or negative. The sad reality of modern technology in the medical industry is that there is very less availability of vanguard doctors, who can help patients in diagnosing their disease, which can be treated successfully as soon as possible diagnosis has been done. Therefore, Hong Kong University (HKU) scientists discovered “Biomarker” which is being rapidly used by physicians, Neuroscientists and epidemiologists in measuring the intensity of disease provided with the details of its cause and treatment. Biomarkers possess possibilities in making wishes of doctors and scientists into reality, to identify that person who is at high risk of any disease so that doctors can take protective measures in saving that life within time. Nevertheless, according to Global data “Biomarkers” are a useful instrument in examining COVID-19 vaccine and fastening the process of clinical trials, decreasing the development cost and decreasing patient security risk. They also can be utilized to find the drug which can help in treating Covid-19 patients and can also be used to determine which drugs might be able to treat COVID-19 patients.

scholarly journals Comparison of Kidney Segmentation Under Attention U-Net Architectures

2021 ◽  
Author(s):  
Vasileios Alevizos ◽  
Marcia Hon
Keyword(s):  
Speckle Noise ◽  
Medical Industry ◽  
Rapid Diagnosis ◽  
Complex Structures ◽  
Imaging Analysis ◽  
Early Detection Of Disease ◽  
The Us ◽  
Innovative Solution ◽  
Kidney Segmentation ◽  
Learning Architectures

One of the most prominent machine learning advantages in the medical industry is the early detection of disease. Automatic kidney detection is of great importance for rapid diagnosis and treatment, where related diseases occupy over 73,750 new cases in the US in 2020 [1]. Today, the performance of diagnosis has been by highly trained radiologists. However, the complex structures contribute to speckle noise and inhomogeneous intensity profiles. Thus, there is a necessity to automate segmentation on kidney ultrasounds using U-Net Deep Learning architectures - an innovative solution for Medical Imaging Analysis. In this research, our focus is on the comparison of Attention U-Net in the context of different backbones such as VGG19, ResNet152V2, and EfficientNetB7. By providing this comparison, we will accomplish a survey for future researchers to more effectively decide on which Attention U-Net architecture to utilize for their segmentation projects.

scholarly journals Coronary Perforation- A Nightmare in Cath Lab

2021 ◽  
Author(s):  
Ashok Kumar Thakur ◽  
Puneet Aggarwal ◽  
Rajeev Bharadwaj ◽  
Bhagya Narayan Pandit ◽  
Ranjit Kumar Nath
Keyword(s):  
Coronary Artery ◽  
Standard Of Care ◽  
Coronary Intervention ◽  
Modern Technology ◽  
Coronary Artery Perforation ◽  
Coronary Perforation ◽  
Percutaneous Coronary ◽  
Incidence Risk ◽  
Life Threatening ◽  
Artery Disease

Percutaneous Coronary Intervention (PCI) is now the standard of care in patients with coronary artery disease. With advances in modern technology, the success of PCI has relatively increased, and so is its complication, specifically in complex coronary intervention. Coronary perforation is one of the most dreadful and life-threatening complications of PCI. The most vital step in the management of coronary perforation is its identification and quick action. Multiple methods for management are now recommended in the literature, but the mainstay of treatment is still prevention. This review discusses the incidence, risk factors, prevention, identification, and management of Coronary Artery Perforation (CAP).

scholarly journals Pengembangan Perangkat Pembelajaran Cooperative Integrated Reading And Composition Untuk Meningkatkan Pemahaman Geometri

AKSIOMA ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 23-35
Author(s):  
Andang Andang ◽  
Arnasari Merdekawati Hadi ◽  
Ika Wirahmad
Keyword(s):  
Qualitative Data ◽  
Human Mind ◽  
Mean Value ◽  
Modern Technology ◽  
Student Activity ◽  
Is Research ◽  
Learning Module ◽  
Key Word ◽  
The Mean ◽  
Test Result

Mathematics is a universal science underlying the development of modern technology, has an important role in various disciplines and advance the human mind. Mathematics subjects in addition to having abstract nature it also requires a good understanding. In general at school, the teacher is still the center of the delivery of the material, the source of learning is the teacher’s handbook, concentrating toomuch on the exercise of solvin g a more procedural mechanistic problem rather than inculcating an understanding. The purpose of this research are 1) to produce learning device with cooperative integrated reading and composition in order to increase understanding of gemetry, and 2) know the student’s response to the learning device that has been developed. This type of research is research development with model ploomp. Subjek field trial includes 32 students of class VIII7 in SMPN 1 Bima city. Instrument research used consisted of validation sheet, observation sheet teaching implementation, student activity observation sheet, student’s response haunter, teacher research questionnaire, and concept comprehension test, result of product assesment is a RPP draft, LKS and student learning module by using quantitative and qualitative data analysis. Assessment used by finding the mean value of all aspect of prototypt assessment by validator 4,6 with qualitative criteria is very valid. Date student activity obtained score to idea of 98,84%. Avarage ability of teachers in learning process 4,26 and are in good category, more than 95% of students responded by saying interested and are interested in learning, and the understanding of geometry (circle) avarages is 85,43 with classical completeness equal to 87,09%.  Key Word: Learning device, cooperative CIRC, understanding of geometry.

scholarly journals Comparison of Kidney Segmentation Under Attention U-Net Architectures

2021 ◽  
Author(s):  
Vasileios Alevizos ◽  
Marcia Hon
Keyword(s):  
Speckle Noise ◽  
Medical Industry ◽  
Rapid Diagnosis ◽  
Complex Structures ◽  
Imaging Analysis ◽  
Early Detection Of Disease ◽  
The Us ◽  
Innovative Solution ◽  
Kidney Segmentation ◽  
Learning Architectures

One of the most prominent machine learning advantages in the medical industry is the early detection of disease. Automatic kidney detection is of great importance for rapid diagnosis and treatment, where related diseases occupy over 73,750 new cases in the US in 2020 [1]. Today, the performance of diagnosis has been by highly trained radiologists. However, the complex structures contribute to speckle noise and inhomogeneous intensity profiles. Thus, there is a necessity to automate segmentation on kidney ultrasounds using U-Net Deep Learning architectures - an innovative solution for Medical Imaging Analysis. In this research, our focus is on the comparison of Attention U-Net in the context of different backbones such as VGG19, ResNet152V2, and EfficientNetB7. By providing this comparison, we will accomplish a survey for future researchers to more effectively decide on which Attention U-Net architecture to utilize for their segmentation projects.

Research on Security Risk Analysis and Protective Measures of Power Internet of Things

2021 ◽  
Author(s):  
Zhenwan Zou ◽  
Yang Li ◽  
Huiting Yang ◽  
Congcong Shi ◽  
Ruxia Yang
Keyword(s):  
Internet Of Things ◽  
Risk Analysis ◽  
Security Risk ◽  
Protective Measures

scholarly journals Relation of abnormal cardiac stress testing with outcomes in patients undergoing renal transplantation

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260718
Author(s):  
Kelsey Anderson ◽  
Chirag Bavishi ◽  
Dhaval Kolte ◽  
Reginald Gohh ◽  
James A. Arrighi ◽  
...  
Keyword(s):  
Renal Transplantation ◽  
Renal Transplant ◽  
Stress Testing ◽  
Stress Test ◽  
Major Adverse Cardiovascular Events ◽  
Cardiac Stress ◽  
Cardiac Stress Testing ◽  
Artery Disease ◽  
Test Result

Cardiovascular risk stratification is often performed in patients considered for renal transplantation. In a single center, we sought to examine the association between abnormal stress testing with imaging and post-renal transplant major adverse cardiovascular events (MACE) using multivariable logistic regression. From January 2006 to May 2016 232 patients underwent renal transplantation and 59 (25%) had an abnormal stress test result. Compared to patients with a normal stress test, patients with an abnormal stress test had a higher prevalence of dyslipidemia, diabetes mellitus, obesity, coronary artery disease (CAD), and heart failure. Among those with an abnormal result, 45 (76%) had mild, 10 (17%) moderate, and 4 (7%) severe ischemia. In our cohort, 9 patients (3.9%) had MACE at 30-days post-transplant, 5 of whom had an abnormal stress test. The long-term MACE rate, at a median of 5 years, was 32%. After adjustment, diabetes (OR 2.37, 95% CI 1.12–5.00, p = 0.02), CAD (OR: 3.05, 95% CI 1.30–7.14, p = 0.01) and atrial fibrillation (OR: 5.86, 95% CI 1.86–18.44, p = 0.002) were independently associated with long-term MACE, but an abnormal stress test was not (OR: 0.83, 95% CI 0.37–1.92, p = 0.68). In conclusion, cardiac stress testing was not an independent predictor of long-term MACE among patients undergoing renal transplant.

scholarly journals Comparison of Kidney Segmentation Under Attention U-Net Architectures

2021 ◽  
Author(s):  
Vasileios Alevizos ◽  
Marcia Hon
Keyword(s):  
Speckle Noise ◽  
Medical Industry ◽  
Rapid Diagnosis ◽  
Complex Structures ◽  
Imaging Analysis ◽  
Early Detection Of Disease ◽  
The Us ◽  
Innovative Solution ◽  
Kidney Segmentation ◽  
Learning Architectures

One of the most prominent machine learning advantages in the medical industry is the early detection of disease. Automatic kidney detection is of great importance for rapid diagnosis and treatment, where related diseases occupy over 73,750 new cases in the US in 2020 [1]. Today, the performance of diagnosis has been by highly trained radiologists. However, the complex structures contribute to speckle noise and inhomogeneous intensity profiles. Thus, there is a necessity to automate segmentation on kidney ultrasounds using U-Net Deep Learning architectures - an innovative solution for Medical Imaging Analysis. In this research, our focus is on the comparison of Attention U-Net in the context of different backbones such as VGG19, ResNet152V2, and EfficientNetB7. By providing this comparison, we will accomplish a survey for future researchers to more effectively decide on which Attention U-Net architecture to utilize for their segmentation projects.

P6187Multicentre diagnostic performance of on-site workstation CT derived fractional flow reserve

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A R Ihdayhid ◽  
S Fujimoto ◽  
S Motoyama ◽  
A Comella ◽  
E Kato ◽  
...  
Keyword(s):  
Fractional Flow Reserve ◽  
Diagnostic Performance ◽  
Pearson Correlation ◽  
Pooled Analysis ◽  
Fractional Flow ◽  
Desktop Computer ◽  
Reduced Order ◽  
Flow Reserve ◽  
Artery Disease ◽  
Global Data

Abstract Background On-site workstation based computed tomography derived fractional flow reserve (CT-FFR) is an emerging method to assess vessel specific ischaemia in coronary artery disease (CAD). Global data on its diagnostic performance when compared with CT coronary angiography (CTA) is limited. Purpose To evaluate the on-site multicentre diagnostic performance of reduced-order CT-FFR at detecting vessel specific ischaemia. Method This is a retrospective pooled analysis of 141 patients (204 vessels) with suspected CAD enrolled from 3 global centres who underwent CTA, onsite CT-FFR and invasive FFR. On-site CT-FFR was performed using a reduced order model on a standard desktop computer with dedicated software. The per vessel diagnostic performance of CT-FFR (≤0.8) for vessel specific ischemia (FFR≤0.8) was compared with CTA (≥50% stenosis). Results Mean age was 65.8±9.9, 70.7% were male. FFR significant stenosis was present in 34.3% (70/204) of vessels. Pearson correlation of CT-FFR for invasive FFR was 0.52, P<0.001. Bland Altman analysis demonstrated a mean difference of 0.06±0.15 (95% limits of agreement −0.22 to 0.35). Per vessel diagnostic accuracy, sensitivity and specificity of CT-FFR and CTA were 79.9% vs 53.5%; 78.6% vs 85.7%; 80.6% vs 35.9% respectively. Diagnostic performance as assessed by area under the receiver operator curve (AUC) for CT FFR was superior to CTA (0.82 [95% CI 0.76–0.88] vs 0.61 [0.55–0.67]; P<0.001). Conclusion On-site workstation CT-FFR demonstrated high per vessel diagnostic performance and was superior when compared with CTA in assessment of vessel specific ischaemia as assessed by invasive FFR in a multicentre setting.

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