ArticleHealthcare (Basel, Switzerland)2022
A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.Healthcare informatics research · 2026Article
- Medical Imaging Data Calls for a Thoughtful and Collaborative Approach to Data Governance.PLOS digital health · 2025Article
- Wearing a fur coat in the summertime: Should digital pathology redefine medical imaging?Journal of pathology informatics · 2025Article
- MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.BMC medical informatics and decision making · 2025Article
- Evaluating the impact of different deface algorithms on deep learning segmentation software performance.Frontiers in oncology · 2025Article
- 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.Journal of imaging informatics in medicine · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients' Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute. In this paper, we first highlight and review the key challenges in the medical image data de-identification process. In this paper, we develop a two-stage de-identification process for CT scan images available in DICOM file format. In the first stage of the de-identification process, the patient's PII-including name, date of birth, etc., are removed at the hospital facility using the export process available in their Picture Archiving and Communication System (PACS). The second stage employs the proposed DICOM de-identification tool for an exhaustive attribute-level investigation to further de-identify and ensure that all PII has been removed. Finally, we provide a roadmap for future considerations to build a semi-automated or automated tool for the DICOM datasets de-identification.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.