A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis

Shahid, Arsalan; Bazargani, Mehran Hossein Zadeh; Banahan, Paul; Namee, Brian Mac; Kechadi, Tahar; Treacy, Ceara; Regan, Gilbert; MacMahon, Peter

doi:10.3390/healthcare10050755

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…Recent studies have shown that de-identification process of DICOM attributes is important and some of them are required to be removed. Likely, a twostage de-identification process for medical images is available in DICOM file format [33]. This process must be taken into account in establishing further datasets.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Privacy Issues in Magnetic Resonance Images

KAPKİÇ

Sağıroğlu

2023

International Journal of Information Security Science

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Privacy in magnetic resonance imaging (MRI) plays an important role due to violations occurring in scanning, storing, transferring, analyzing, and sharing. This paper reviews privacy concerns in MRI and especially Brain MRI in terms of datasets, models, platforms, violations, solutions used in privacy and security in the literature, discusses important issues based on risks, techniques, policies, rules, and existing and missing points in MRIs. Even if there have been rules, regulations, policies, and laws available for preserving privacy with the available techniques anonymization, differential privacy, federated learning, pseudonymization, synthetic data generation, privacy-utility or anonymization-utility dilemma is still on novel privacy-enhancing, or preserving techniques are always required to handle sensitive data with care. This paper focuses on these issues with some suggestions, and also discusses these issues for future directions.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Privacy Issues in Magnetic Resonance Images

KAPKİÇ

Sağıroğlu

2023

International Journal of Information Security Science

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show abstract

“…This includes information on the modality used, scan parameters, patient information, and institutional information. This information is stored in a single file as metadata that can be accessed using tags [20]. Most tags have keywords to get specific information and its value.…”

Section: Methods 21 Dicom Standardmentioning

confidence: 99%

“…Leakage of important information can result in privacy violations, as well as 331 intrusion into the system [15], [16]. Therefore, tools are needed to protect sensitive information, one of which is the anonymization of DICOM [17]- [20]. While several tools are available for de-identifying DICOM data, including those integrated into DICOM Viewer software [21]- [24], many of these tools are commercial programs and may not be accessible to everyone.…”

Section: Introductionmentioning

confidence: 99%

Software development to anonymize patient information from clinical computed tomography images

Anam,

Naufal,

Saragih

et al. 2024

IJPHS

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A free software for digital imaging and communications in medicine (DICOM) image anonymization is needed to protect patient information from third parties. This study aimed to develop a software for the anonymization of patient information in computed tomography (CT) image. There was a total of 17 informations to be anonymized, such as Patient’s Name, Other Patient Names, Patient’s ID, Patient’s Birth Date, Patient’s Sex, Patient’s Age, Study ID, Study Description, Series Description, Institution Name, Institution Adress, Referring Phisician’s Name, Consulting Physician’s Name, Performing Physician’s Name, Name of Physician(s) Reading Study, Operator’s Name, and Protocol Name. In every information, its initial value was replaced with a dummy value with the string value of "N/A". For testing, patient CT images from four different hospitals with different scanners were collected. It is found that each scanner had different information stored in DICOM information. However, the anonymization process in the four hospitals works well with accuracy of 100%. The developed software can anonymize DICOM images flexibly and successfully. This software can be used for anonymization of patient information in order to protect patient information.

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A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey

Vahdati,

Khosravi,

Mahmoudi

et al. 2024

J Digit Imaging. Inform. med.

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In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

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A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis

Cited by 9 publications

References 33 publications

Privacy Issues in Magnetic Resonance Images

Privacy Issues in Magnetic Resonance Images

Software development to anonymize patient information from clinical computed tomography images

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey

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