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. 2023 Dec 21:7:e42505.
doi: 10.2196/42505.

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation

Giuseppina Esposito  1   2 , Ciro Allarà  1   3 , Marco Randon  1 , Marco Aiello  4 , Marco Salvatore  4 , Giuseppe Aceto  5 , Antonio Pescapè  5
Affiliations

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation

Giuseppina Esposito et al. JMIR Form Res. .

Abstract

Background: Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes.

Objective: The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform.

Methods: Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay.

Results: Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist.

Conclusions: This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.

Keywords: COVID-19; biobank; diagnostics; eHealth; network performance.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Overall architecture of the system. DB: database; XNAT: Extensible Neuroimaging Archive Toolkit.
Figure 2
Figure 2
Screenshots of the interactive case studies. (A) Search filter interface for the researcher use case. (B, C) Image viewer during the annotation process in the radiologist use case.
Figure 3
Figure 3
Network setups considered in the experimental evaluations. (A) Local area network (LAN) setup. (B) Virtual private network (VPN) setup. (C) Wide area network (WAN) setup.
Figure 4
Figure 4
Algorithm 1: General web-based measurement procedure pseudocode. The use_case_subroutine() is implemented according to the specific use case.
Figure 5
Figure 5
Algorithm 2: use_case_subroutine() pseudocode for the researcher use case.
Figure 6
Figure 6
Algorithm 3: use_case_subroutine() pseudocode for the radiologist use case. Interact (ImageSet) includes a sequence of scrolling through slices and change of scan, with varying pace.
Figure 7
Figure 7
Time evolution of central processing unit (CPU; blue) and memory (MEM; red) usage during diagnostic data transfer for the Web (A) and Database (B) containers. (C and D) Relative maximized view of A and B, respectively.
Figure 8
Figure 8
Quartile distribution of average throughput (over the whole duration of activity) for the researcher (A) and radiologist (B) use cases. LAN: local area network; VPN: virtual private network; WAN: wide area network.
Figure 9
Figure 9
Time evolution of central processing unit (CPU; blue) and memory (MEM; red) usage for the Web (A) and Database (B) containers during the researcher use case reproduction and for the (C) Web (C) and Database (D) containers during the radiologist use case reproduction.
See this image and copyright information in PMC

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