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Data Sources in Medical Imaging
Published in Johan Helmenkamp, Robert Bujila, Gavin Poludniowski, Diagnostic Radiology Physics with MATLAB®, 2020
Jonas Andersson, Lundman Josef, Gavin Poludniowski, Robert Bujila
Information pertinent to medical imaging can be found from a variety of data sources, consisting of a number of systems that together support the radiological workflow. The main information systems are: A Hospital Information System (HIS), which is used for high-level hospital administration and is also where electronic records of patients' medical history are stored.A Radiological Information System (RIS), which is used for patient scheduling and resource management. It is also common that radiologists record their reports in the RIS.A Picture Archiving and Communication System (PACS), where radiological images are archived. A PACS also provides healthcare professionals access to medical images for review.Imaging systems of various modalities where medical images are generated.Additional sources such as a Content Management System (CMS) containing, for example, an inventory of radiological equipment along with records of maintenance and Quality Control (QC) reports.
Predictive modeling, machine learning, and statistical issues
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
Panagiotis Korfiatis, Timothy L. Kline, Zeynettin Akkus, Kenneth Philbrick, Bradley J. Erickson
Every major medical institution stores imaging data in the PACS or in a separate vendor-neutral archive (VNA), nearly always using digital image communications in medicine (DICOM) (2001). The radiology information system (RIS) is used for the text component of an imaging department, such as the order, the indication for examination, the interpretation, and billing information. The PACS provides an image viewer that enables quick image curation (typically by the radiologic technologist) and image viewing and measurement for examination interpretation (typically by a radiologist).
Imaging
Published in Ian Greaves, Military Medicine in Iraq and Afghanistan, 2018
At the outset of Operation Telic in 2003, land-based radiology comprised conventional plain film X-ray (Figure 13.1), mobile image intensification and rudimentary, portable ultrasound (Figure 13.2). The maritime Primary Casualty Receiving Facility, Royal Fleet Auxiliary (RFA) Argus, also boasted the only deployable CT scanner within the Defence Medical Services. By the end of operations at Camp Bastion in 2014, deployed military radiology had been transformed to include digital radiography (DR), advanced ultrasound and 64 slice CT scanning at the Role 3 Hospital. There was also a deployed Patient Archive and Communications System (PACS), Radiology Information System (RIS) and a tele-radiology capability beyond civilian capabilities in many aspects and beyond the imaging capability of all the other coalition partners.
Current status and challenges in establishing reference intervals based on real-world data
Published in Critical Reviews in Clinical Laboratory Sciences, 2023
Sijia Ma, Juntong Yu, Xiaosong Qin, Jianhua Liu
RIs refer to intervals between the upper and lower reference limits [8] and are usually taken as the reference value of healthy individuals in the middle 95% (i.e. between the 2.5% and 97.5% quantiles) of the reference range. RIs are useful for judging patient health status and making clinical decisions such as diagnosis and treatment [9]. In routine work, most RIs used in laboratories are cited from documents such as industry guidelines, textbooks, and instrument or reagent manufacturers’ instructions. However, the large number of laboratories worldwide, wide variety of instruments and reagents used range of detection methods available, and application of international standards all highlight the need for validation of RIs [10]. Due to differences in population, geography, lifestyle, and epidemiological manifestations of disease, biological RIs also vary significantly between individuals [11], and RIs directly imported from documents may not be applicable to local populations. Therefore, for many test indicators, it is often necessary for different laboratories to establish specific RIs to partition patients based on sex, age, population, ethnicity, and so on.
Reference intervals: past, present, and future
Published in Critical Reviews in Clinical Laboratory Sciences, 2023
While we have discussed RIs as clinical information helpful to clinicians, at the end of the day, RIs have a significant impact on the individuals receiving care. Improving how RIs are calculated and applied to individuals can have a significant impact on healthcare. We may not yet be in the “Golden Age” of RIs. However, with the concerted move to EHRs, clinicians have access to clinical laboratory “Big Data” that never existed before; in addition, the computational ability of computers have progressed to the point that statistical techniques that were once cumbersome or impossible to perform at scale are now widely available. An area for RI expansion is within people living with chronic conditions or diseases. How does an individual manage a direct RI for 60-79 year olds, where 70% are removed due to exclusionary conditions [120], or how does one define a normal thyroid result for a preterm baby [121]? The laboratory community is positioned to create useful data for clinical care, but it will require some rethinking of what an RI is, particularly for clinicians. These realizations have presented the global laboratory community an opportunity to fill gaps that remain for pediatric populations and to develop a goal for common global RIs.
Personalized reference intervals: from theory to practice
Published in Critical Reviews in Clinical Laboratory Sciences, 2022
Abdurrahman Coskun, Sverre Sandberg, Ibrahim Unsal, Mustafa Serteser, Aasne K. Aarsand
Big data are based on the availability of and accessibility to large datasets that are usually stored in laboratory information systems (LIS). It is one of the most powerful tools for clinical medicine in the digital era, and it is used in various fields in laboratory medicine, including recently for derivation of popRIs [91–95]. It is particularly useful in areas where data collection is challenging, such as RIs for pediatric or geriatric age groups. In addition, it can be used to determine BV, either generally [96] or for specific population groups. For ethical and technical reasons, it is difficult to collect serial samples required for BV projects in, e.g. children. The pediatric age is a dynamic period that is characterized by large variations in metabolism and where RIs vary depending on age [97,98]; thus estimating BV in different pediatric age groups is important.