Nowadays, digital medical imaging in healthcare has become a fundamental tool for medical diagnosis. This growth has been accompanied by the development of technologies and standards, such as the DICOM standard and PACS. This environment led to the creation of collaborative projects where there is a need to share medical data between different institutions for research and educational purposes. In this context, it is necessary to maintain patient data privacy and provide an easy and secure mechanism for authorized personnel access. This paper presents a solution that fully de-identifies standard medical imaging objects, including metadata and pixel data, providing at the same time a reversible de-identifier mechanism that retains search capabilities from the original data. The last feature is important in some scenarios, for instance, in collaborative platforms where data is anonymized when shared with the community but searchable for data custodians or authorized entities. The solution was integrated into an open source PACS archive and validated in a multidisciplinary collaborative scenario.

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

No abstract

Privacy protection is a major requirement for the complete success of EHR systems, becoming even more critical in collaborative scenarios, where data is shared among institutions and practitioners. While textual data can be easily de-identified, patient data in medical images implies a more elaborate approach. In this work we present a solution for sensitive word identification in medical images based on a combination of two machine-learning models, achieving a F1-score of 0.94. Three experts evaluated the system performance. They analyzed the output of the present methodology and categorized the studies in three groups: studies that had their sensitive words removed (true positive), studies with complete patient identity (false negative) and studies with mistakenly removed data (false positive). The experts were unanimous regarding the relevance of the present tool in collaborative medical environments, as it may improve the exchange of anonymized patient data between institutions.

No abstract