Literature DB >> 35627892

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

Arsalan Shahid1, Mehran H Bazargani1, Paul Banahan2, Brian Mac Namee1, Tahar Kechadi1, Ceara Treacy3, Gilbert Regan3, Peter MacMahon2.   

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.

Entities:  

Keywords:  DICOM; de-identification; medical image analytics; privacy preservation

Year:  2022        PMID: 35627892      PMCID: PMC9141493          DOI: 10.3390/healthcare10050755

Source DB:  PubMed          Journal:  Healthcare (Basel)        ISSN: 2227-9032


  17 in total

1.  Introduction to the DICOM standard.

Authors:  Peter Mildenberger; Marco Eichelberg; Eric Martin
Journal:  Eur Radiol       Date:  2001-09-15       Impact factor: 5.315

2.  DICOM for Clinical Research: PACS-Integrated Electronic Data Capture in Multi-Center Trials.

Authors:  Daniel Haak; Charles-E Page; Sebastian Reinartz; Thilo Krüger; Thomas M Deserno
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

Review 3.  Beyond the DICOM header: additional issues in deidentification.

Authors:  Jeffrey D Robinson
Journal:  AJR Am J Roentgenol       Date:  2014-12       Impact factor: 3.959

4.  Identification and classification of DICOM files with burned-in text content.

Authors:  Petr Vcelak; Martin Kryl; Michal Kratochvil; Jana Kleckova
Journal:  Int J Med Inform       Date:  2019-03-01       Impact factor: 4.046

Review 5.  Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints.

Authors:  Ohad Oren; Bernard J Gersh; Deepak L Bhatt
Journal:  Lancet Digit Health       Date:  2020-09

6.  Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future.

Authors:  Eyal Lotan; Charlotte Tschider; Daniel K Sodickson; Arthur L Caplan; Mary Bruno; Ben Zhang; Yvonne W Lui
Journal:  J Am Coll Radiol       Date:  2020-04-28       Impact factor: 5.532

7.  Anonymization of DICOM electronic medical records for radiation therapy.

Authors:  Wayne Newhauser; Timothy Jones; Stuart Swerdloff; Warren Newhauser; Mark Cilia; Robert Carver; Andy Halloran; Rui Zhang
Journal:  Comput Biol Med       Date:  2014-07-26       Impact factor: 4.589

8.  DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

Authors:  Thomas Yi; Ian Pan; Scott Collins; Fiona Chen; Robert Cueto; Ben Hsieh; Celina Hsieh; Jessica L Smith; Li Yang; Wei-Hua Liao; Lisa H Merck; Harrison Bai; Derek Merck
Journal:  J Digit Imaging       Date:  2021-11-02       Impact factor: 4.903

9.  Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy.

Authors:  K Y E Aryanto; M Oudkerk; P M A van Ooijen
Journal:  Eur Radiol       Date:  2015-06-03       Impact factor: 5.315

10.  MINC 2.0: A Flexible Format for Multi-Modal Images.

Authors:  Robert D Vincent; Peter Neelin; Najmeh Khalili-Mahani; Andrew L Janke; Vladimir S Fonov; Steven M Robbins; Leila Baghdadi; Jason Lerch; John G Sled; Reza Adalat; David MacDonald; Alex P Zijdenbos; D Louis Collins; Alan C Evans
Journal:  Front Neuroinform       Date:  2016-08-11       Impact factor: 4.081

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