Data Compression as a Base for eHealth Interoperability

2017 ◽  
pp. 266-281
Author(s):  
Martin Žagar ◽  
Branko Mihaljević ◽  
Josip Knezović
Keyword(s):  
Wireless Networks ◽  
Diagnostic Imaging ◽  
Data Compression ◽  
Local Network ◽  
Imaging Data ◽  
Bandwidth Sharing ◽  
Limited Bandwidth ◽  
Remote Diagnostics ◽  
Medical Diagnostic ◽  
Mri Study

eHealth is a set of systems and services that enable the sharing of medical diagnostic imaging data remotely. The application of eHealth solves the problem of the lack of specialized personnel, unnecessary execution of multiple diagnostic imaging and rapid exchange of information and remote diagnostics. Medical imaging generates large amounts of data. An MRI study can contain up to several Gigabytes (GB). The exchange of such large amounts of data in the local network facilities is a significant problem due to bandwidth sharing which is even more significant in mobile and wireless networks. A possible solution to this problem is data compression with the requirement that there is no loss of data. The goal of this chapter is a conceptual compression prototype that will allow faster and more efficient exchange of medical images in systems with limited bandwidth and communication speeds (cellular networks, wireless networks). To obtain this conceptual compression prototype we will use wavelets.

scholarly journals The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1317
Author(s):  
Maria Elena Laino ◽  
Angela Ammirabile ◽  
Alessandro Posa ◽  
Pierandrea Cancian ◽  
Sherif Shalaby ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Diagnostic Imaging ◽  
Clinical Work ◽  
Rapid Expansion ◽  
Chest Imaging ◽  
Imaging Data ◽  
Prognostic Information ◽  
Quantitative Ct ◽  
Work Up

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

Patient Radiation Dose from Medical Diagnostic Imaging Procedures in 2018

Atomic Energy ◽  
2021 ◽  
Author(s):  
A. G. Tsovyanov ◽  
A. G. Sivenkov ◽  
V. E. Zhuravleva ◽  
V. V. Kosterev
Keyword(s):  
Radiation Dose ◽  
Diagnostic Imaging ◽  
Imaging Procedures ◽  
Medical Diagnostic ◽  
Patient Radiation Dose

scholarly journals Identifying a Medical Department Based on Unstructured Data: A Big Data Application in Healthcare

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 25 ◽  
Author(s):  
Veena Bansal ◽  
Abhishek Poddar ◽  
R. Ghosh-Roy
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Medical Science ◽  
Imaging Data ◽  
Medical Department ◽  
User Input ◽  
Medical Diagnostic ◽  
Tertiary Healthcare ◽  
Big Data Application ◽  
And Performance

Health is an individual’s most precious asset and healthcare is one of the vehicles for preserving it. The Indian government’s spend on healthcare system is relatively low (1.2% of GDP). Consequently, Secondary and Tertiary government healthcare centers in India (that are presumed to be of above average ratings) are always crowded. In Tertiary healthcare centers, like the All India Institute of Medical Science (AIIMS), patients are often unable to articulate their problems correctly to the healthcare center’s reception staff, so that these patients to be directed to the correct healthcare department. In this paper, we propose a system that will scan prescriptions, referral letters and medical diagnostic reports of a patient, process the input using OCR (Optical Character Recognition) engines, coupled with image processing tools, to direct the patient to the most relevant department. We have implemented and tested parts of this system wherein a patient enters his symptoms and/or provisional diagnosis; the system suggests a department based on this user input. Our system suggests the correct department 70.19% of the time. On further investigation, we found that one particular department of the hospital was over-represented. We eliminated the department from the data and performance of the system improved to 92.7%. Our system presently makes its suggestions using random forest algorithm that has been trained using two information repositories-symptoms and disease data, functional description of each medical department. It is our informed assumption that, once we have incorporated medicine information and diagnostics imaging data to train the system; and the complete medical history of the patient, performance of the system will improve further.

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