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J Biomed Inform. 2018 Nov 9. pii: S1532-0464(18)30205-3. doi: 10.1016/j.jbi.2018.10.009. [Epub ahead of print]

A Framework for Data-Driven Adaptive GUI Generation Based on DICOM.

Author information

1
Dipartimento dell'Innovazione Industriale e Digitale (DIID), Università degli Studi di Palermo, Viale delle Scienze, Ed.8, 90133 Palermo, Italy. Electronic address: orazio.gambino@unipa.it.
2
Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy; Istituto di Bioimmagini e Fisiologia Molecolare (IBFM), Consiglio Nazionale delle Ricerche (CNR), Contrada Pietrapollastra-Pisciotto, 90015 Cefalù (PA), Italy; Dipartimento dell'Innovazione Industriale e Digitale (DIID), Università degli Studi di Palermo, Viale delle Scienze, Ed.8, 90133 Palermo, Italy.
3
Dipartimento dell'Innovazione Industriale e Digitale (DIID), Università degli Studi di Palermo, Viale delle Scienze, Ed.8, 90133 Palermo, Italy.
4
Dipartimento di Biopatologia e Biotecnologie Mediche (DIBIMED), Università degli Studi di Palermo, via del Vespro 129, 90127 Palermo, Italy.

Abstract

Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investigation following a well-defined workflow. This paper proposes a software framework aimed at addressing the issues outlined before. Specifically, we designed a DICOM based mechanism of data-driven GUI generation, referring to the examined body part and imaging modality as well as to the medical image analysis task to perform. In this way, the self-configuring GUI is generated on-the-fly, so that just specific functionalities are active according to the current clinical scenario. Such a solution provides also a tight integration with the DICOM standard, which considers various aspects of the technology in medicine but does not address GUI specification issues. The proposed workflow is designed for diagnostic workstations with a local file system on an interchange media acting inside or outside the hospital ward. Accordingly, the DICOMDIR conceptual data model, defined by a hierarchical structure, is exploited and extended to include the GUI information thanks to a new Information Object Module (IOM), which reuses the DICOM information model. The proposed framework exploits the DICOM standard representing an enabling technology for an auto-consistent solution in medical diagnostic applications. In this paper we present a detailed description of the framework, its software design, and a proof-of-concept implementation as a suitable plug-in of the OsiriX imaging software.

KEYWORDS:

DICOM; Data-driven GUI generation; Faceted classification; Graphical User Interfaces; Medical diagnostic software

PMID:
30419365
DOI:
10.1016/j.jbi.2018.10.009

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