Deep classification algorithm for De-identification of DICOM medical images

• URL: http://arxiv.org/abs/2508.02177v1
• Date: Mon, 04 Aug 2025 08:21:18 GMT
• Title: Deep classification algorithm for De-identification of DICOM medical images
• Authors: Bufano Michele, Kotter Elmar,
• Abstract summary: De-identification of DICOM files is an essential component of medical image research.<n>The most sensible information, like names, history, personal data and institution were successfully recognized.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background : De-identification of DICOM (Digital Imaging and Communi-cations in Medicine) files is an essential component of medical image research. Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHI) need to be hidden or removed due to legal reasons. According to the Health Insurance Portability and Accountability Act (HIPAA) and privacy rules, also full-face photographic images and any compa-rable images are direct identifiers and are considered protected health information that also need to be de-identified. Objective : The study aimed to implement a method that permit to de-identify the PII and PHI information present in the header and burned on the pixel data of DICOM. Methods : To execute the de-identification, we implemented an algorithm based on the safe harbor method, defined by HIPAA. Our algorithm uses input customizable parameter to classify and then possibly de-identify individual DICOM tags. Results : The most sensible information, like names, history, personal data and institution were successfully recognized. Conclusions : We developed a python algorithm that is able to classify infor-mation present in a DICOM file. The flexibility provided by the use of customi-zable input parameters, which allow the user to customize the entire process de-pending on the case (e.g., the language), makes the entire program very promis-ing for both everyday use and research purposes. Our code is available at https://github.com/rtdicomexplorer/deep_deidentification.

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