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

Vcelak, Petr; Kryl, Martin; Kratochvil, Michal; Kleckova, Jana

doi:10.1016/j.ijmedinf.2019.02.011

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…In fact, topograms for CT data and ultrasound images may have patient information burned into the pixel data [ 17 ]. To manage this critical issue, some specific studies and tools are available to the scientific community [ 18 – 21 ]. In addition, it is demonstrated that humans or specific software could identify individual subjects by reconstructing facial images contained in cranial MR or CT [ 22 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

How does DICOM support big data management? Investigating its use in medical imaging community

Aiello

Esposito²,

Pagliari³

et al. 2021

Insights Imaging

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The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

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Section: Introductionmentioning

confidence: 99%

How does DICOM support big data management? Investigating its use in medical imaging community

Aiello

Esposito²,

Pagliari³

et al. 2021

Insights Imaging

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show abstract

“…43 -45 The radiology informatics community has likewise studied ways to de-identify medical images. 46 -48 However, it is not always clear what constitutes “adequate” de-identification. Part of the challenge is that personally identifying details can be found in many different places within highly heterogeneous medical records.…”

Section: Potential Harmsmentioning

confidence: 99%

The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice

Jackson

Crawford

et al. 2021

Academic Pathology

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Growing numbers of artificial intelligence applications are being developed and applied to pathology and laboratory medicine. These technologies introduce risks and benefits that must be assessed and managed through the lens of ethics. This article describes how long-standing principles of medical and scientific ethics can be applied to artificial intelligence using examples from pathology and laboratory medicine.

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“…Most images, directly originating from the acquisition device, will not contain burned in information about the patient. However, it can occur that images contain burned in text [3]. In case of acquisition devices that use video capture, storage of screen shots or secondary captures by the radiologist, or encapsulation of scanned documents, the image part of the DICOM file might contain patient identifiable information [4].…”

Section: Images With Burned-in Textmentioning

confidence: 99%