Abstract
The advent of imaging methods in medicine has yielded new diagnosing dynamics inside hospitals. Since imaging allows the inspection with few or no intrusiveness, there is a remarked intention in producing medical verdicts from the radiology data by implementing computational algorithms and, therefore preclude the use of the long-lasting analytics that involve manual segmentation or often painful procedures such as histology. Currently, troves of medical-imaging data are stored in the picture archiving and communication system (PACS) – the standard imaging database –. The massive storage is initially created and maintained obeying to the legal regulations, but the resulting repository holds unbeatable conditions to apply artificial intelligence and derive conclusions from hidden patterns, a new mechanism never envisaged before. However, the same regulations that enabled the creation of the medical imaging repository have precluded the quantifications from images stored in PACS.
This paper presents a strategy that empowers PACS so that analytical procedures can run without violating confidentiality policies or creating security breaches. The platform supports unlimited analytical procedures, and, as a prof of concept, the problem of accurately measuring the maximum head circumference in pediatrics is solved and presented.
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Yepes Calderon, F., Rea, N., McComb, J.G. (2018). Enabling the Medical Applications Engine. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_10
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