scholarly journals Artificial Intelligence Trained by Deep Learning Can Improve Computed Tomography Diagnosis of Nontraumatic Subarachnoid Hemorrhage by Nonspecialists

2021 ◽  
Author(s):  
Toru NISHI ◽  
Shigeo YAMASHIRO ◽  
Shuichiro OKUMURA ◽  
Mizuki TAKEI ◽  
Atsushi TACHIBANA ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Deep Learning ◽  
Subarachnoid Hemorrhage ◽  
Computed Tomography Diagnosis

scholarly journals Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ilker Ozsahin ◽  
Boran Sekeroglu ◽  
Musa Sani Musa ◽  
Mubarak Taiwo Mustapha ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Chest Radiography ◽  
Area Under The Curve ◽  
Chest Ct ◽  
Chest Computed Tomography ◽  
Classification Tasks ◽  
Sensitivity Specificity

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

scholarly journals Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xi Zhang ◽  
Zhenfang Wang ◽  
Jun Liu ◽  
Lulin Bi ◽  
Weilan Yan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Cerebral Palsy ◽  
Deep Learning ◽  
Ct Imaging ◽  
Control Group ◽  
Imaging Data ◽  
Brain Ct ◽  
Children With Cerebral Palsy ◽  
The Brain

To analyze the brain CT imaging data of children with cerebral palsy (CP), deep learning-based electronic computed tomography (CT) imaging information characteristics were used, thereby providing help for the rehabilitation analysis of children with CP and comorbid epilepsy. The brain CT imaging data of 73 children with CP were collected, who were outpatients or inpatients in our hospital. The images were randomly divided into two groups. One group was the artificial intelligence image group, and hybrid segmentation network (HSN) model was employed to analyze brain images to help the treatment. The other group was the control group, and original images were used to help diagnosis and treatment. The deep learning-based HSN was used to segment the CT image of the head of patients and was compared with other CNN methods. It was found that HSN had the highest Dice score (DSC) among all models. After treatment, six cases in the artificial intelligence image group returned to normal (20.7%), and the artificial intelligence image group was significantly higher than the control group (X2 = 335191, P < 0.001 ). The cerebral hemodynamic changes were obviously different in the two groups of children before and after treatment. The VP of the cerebral artery in the child was (139.68 ± 15.66) cm/s after treatment, which was significantly faster than (131.84 ± 15.93) cm/s before treatment, P < 0.05 . To sum up, the deep learning model can effectively segment the CP area, which can measure and assist the diagnosis of future clinical cases of children with CP. It can also improve medical efficiency and accurately identify the patient’s focus area, which had great application potential in helping to identify the rehabilitation training results of children with CP.

Multislice imaging of gastrointestinal stromal tumors

2018 ◽  
pp. 3-14
Keyword(s):  
Computed Tomography ◽  
Key Words ◽  
Gold Standard ◽  
Gastrointestinal Stromal Tumors ◽  
Mesenchymal Tumors ◽  
Stromal Tumors ◽  
Preferential Localization ◽  
Multislice Imaging ◽  
Computed Tomography Diagnosis

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the digestive tract (1%). These tumors express the CD 117 in 95% of cases. The stomach is the preferential localization (70%). Diagnosis is difficult and sometimes late. Progress of imaging has greatly improved the management and the prognosis. Computed tomography (CT) is the gold standard for diagnosis, staging, and treatment follow-up. The increasing recognition of GIST’s histopathology and the prolonged survival revealed some suggestive imaging aspects. Key words: gastro-intestinal stromal tumors; computed tomography; diagnosis

Computed Tomography Angiography (CTA) Evaluation of Spontaneous Subarachnoid Hemorrhage

2020 ◽  
Vol 6 (2) ◽  
pp. 78-81
Author(s):  
Md Tauhidul Islam Chowdhury ◽  
Mohammad Shah Jahirul Hoque Chowdhury ◽  
Mohammad Sadekur Rahman Sarkar ◽  
KM Ahasan Ahmed ◽  
Md Nazmul Kabir ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Subarachnoid Hemorrhage ◽  
Computed Tomography Angiography ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Multiple Aneurysm ◽  
Traumatic Subarachnoid Hemorrhage ◽  
One Year ◽  
Spontaneous Subarachnoid Hemorrhage

Background: In evaluation of non-traumatic subarachnoid hemorrhage CT angiography (CTA) has 97-98% sensitivity and near 100% specificity. Objective: This study was conducted to evaluate the CTA findings of CT positive non traumatic subarachnoid hemorrhage. Methodology: This is an observational cross sectional study performed in Neurology department of National Institute of Neurosciences and Hospital, Dhaka over one year period (January 2019 to December 2019). Total 87 CT positive subarachnoid hemorrhage cases were purposively included in this study. All CT positive patients underwent CTA of Cerebral vessels for further evaluation. The angiography were evaluated by competent neuro-radiologists blinded about the study. Result: Among 87 patients, 40.2% were male and 59.8% were female. The average age was 53.33±11.1 years. Among the studied patient the source of bleeding was found 78.16% aneurysmal and 21.84% non-aneurysmal. 85.30% patients had single aneurysm and 14.70% had multiple aneurysm. The highest number of patient had Acom aneurysm (41.17%) followed by MCA (22.05%), ICA (13.23%), ACA (7.35%) and vertebral artery (1.14%) in order of frequency. Among the multiple aneurysm group most of the patients had combination of Acom, MCA and ICA aneurysm. Conclusion: From this study, we can conclude that CTA can be used as the primary diagnostic tool in evaluation of spontaneous SAH. Journal of National Institute of Neurosciences Bangladesh, 2020;6(2): 78-81

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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