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Proceedings Paper

Interconnecting smartphone, image analysis server, and case report forms in clinical trials for automatic skin lesion tracking in clinical trials
Author(s): Daniel Haak; Aliaa Doma; Alexander Gombert; Thomas M. Deserno
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Paper Abstract

Today, subject’s medical data in controlled clinical trials is captured digitally in electronic case report forms (eCRFs). However, eCRFs only insufficiently support integration of subject’s image data, although medical imaging is looming large in studies today. For bed-side image integration, we present a mobile application (App) that utilizes the smartphone-integrated camera. To ensure high image quality with this inexpensive consumer hardware, color reference cards are placed in the camera’s field of view next to the lesion. The cards are used for automatic calibration of geometry, color, and contrast. In addition, a personalized code is read from the cards that allows subject identification. For data integration, the App is connected to an communication and image analysis server that also holds the code-study-subject relation. In a second system interconnection, web services are used to connect the smartphone with OpenClinica, an open-source, Food and Drug Administration (FDA)-approved electronic data capture (EDC) system in clinical trials. Once the photographs have been securely stored on the server, they are released automatically from the mobile device. The workflow of the system is demonstrated by an ongoing clinical trial, in which photographic documentation is frequently performed to measure the effect of wound incision management systems. All 205 images, which have been collected in the study so far, have been correctly identified and successfully integrated into the corresponding subject’s eCRF. Using this system, manual steps for the study personnel are reduced, and, therefore, errors, latency and costs decreased. Our approach also increases data security and privacy.

Paper Details

Date Published: 25 March 2016
PDF: 6 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890B (25 March 2016); doi: 10.1117/12.2216657
Show Author Affiliations
Daniel Haak, RWTH Aachen Univ. (Germany)
Aliaa Doma, RWTH Aachen Univ. (Germany)
Alexander Gombert, RWTH Aachen Univ. (Germany)
Thomas M. Deserno, RWTH Aachen Univ. (Germany)


Published in SPIE Proceedings Vol. 9789:
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
Jianguo Zhang; Tessa S. Cook, Editor(s)

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