Quality assurance framework for rapid automatic analysis deployment in medical imaging

• Published in: Physica medica 2023-12, Vol.116, p.103173-103173, Article 103173
• Main Authors: Peltonen, Juha I, Honkanen, Ari-Pekka, Kortesniemi, Mika
• Format: Article
• Language: English
• Subjects: Algorithms; Diagnostic Imaging; Image Processing, Computer-Assisted; Quality Assurance, Health Care - methods; Radiography; Software
• ISSN: 1120-1797; 1724-191X
• DOI: 10.1016/j.ejmp.2023.103173

Automatic image analysis algorithms have an increasing role in clinical quality assurance (QA) in medical imaging. Although the implementation of QA calculation algorithms may be straightforward at the development level, actual deployment of a new method to clinical routine may require substantial additional effort from supporting services. We sought to develop a multimodal system that enables rapid implementation of new QA analysis methods in clinical practice. The QA system was built using freely available open-source software libraries. The included features were results database, database interface, interactive user interface, e-mail error dispatcher, data processing backend, and DICOM server. An in-house database interface was built, providing the developers of analyses with simple access to the results database. An open-source DICOM server was used for image traffic and automatic initiation of modality-specific QA image analyses. The QA framework enabled rapid adaptation of new analysis methods to automatic image processing workflows. The system provided online data review via an easily accessible user interface. In case of deviations, the system supported simultaneous review of the results for the user and QA expert to trigger corrective actions. In particular, embedded error thresholds, trend analyses, and error-feedback channels were provided to facilitate continuous monitoring and to enable pre-emptive corrective actions. An effective and novel QA framework incorporating easy adaptation and scalability to automated image analysis methods was developed. The framework provides an efficient and responsive web-based tool to manage the normal operation, trends, errors, and abnormalities in medical image quality.