Evidence map›Paper›PMID 38074777›Full record

ArticleRadiology. Artificial intelligence2023

Anonymizing Radiographs Using an Object Detection Deep Learning Algorithm.

Bardia Khosravi, John P Mickley, Pouria Rouzrokh, Michael J Taunton, A Noelle Larson, Bradley J Erickson, Cody C Wyles

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Bardia Khosravi *From the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.ORCID https://orcid.org/0000-0002-8024-339X
John P Mickley *From the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.ORCID 0000-0001-5975-530X
Pouria RouzrokhFrom the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.ORCID 0000-0003-4664-0751
Michael J TauntonFrom the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.ORCID 0000-0002-7636-0489
A Noelle LarsonFrom the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.
Bradley J EricksonFrom the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.ORCID 0000-0001-7926-6095
Cody C WylesFrom the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.ORCID 0000-0002-8629-7567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiographic markers contain protected health information that must be removed before public release. This work presents a deep learning algorithm that localizes radiographic markers and selectively removes them to enable de-identified data sharing. The authors annotated 2000 hip and pelvic radiographs to train an object detection computer vision model. Data were split into training, validation, and test sets at the patient level. Extracted markers were then characterized using an image processing algorithm, and potentially useful markers (eg, "L" and "R") without identifying information were retained. The model achieved an area under the precision-recall curve of 0.96 on the internal test set. The de-identification accuracy was 100% (400 of 400), with a de-identification false-positive rate of 1% (eight of 632) and a retention accuracy of 93% (359 of 386) for laterality markers. The algorithm was further validated on an external dataset of chest radiographs, achieving a de-identification accuracy of 96% (221 of 231). After fine-tuning the model on 20 images from the external dataset to investigate the potential for improvement, a 99.6% (230 of 231,

Indexed as

Conventional RadiographyConvolutional Neural Network (CNN)Experimental InvestigationsSkeletal-AxialSupervised LearningThoraxTransfer Learning

Identifiers

PMID38074777
PMCPMC10698585

What OpenQuestion holds

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Registered trials

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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.