Journal article
Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset
Journal of digital imaging, Vol.33(2), pp.490-496
11/25/2019
DOI: 10.1007/s10278-019-00299-9
PMCID: PMC7165201
PMID: 31768897
Abstract
Pneumothorax is a potentially life-threatening condition that requires prompt recognition and often urgent intervention. In the ICU setting, large numbers of chest radiographs are performed and must be interpreted on a daily basis which may delay diagnosis of this entity. Development of artificial intelligence (AI) techniques to detect pneumothorax could help expedite detection as well as localize and potentially quantify pneumothorax. Open image analysis competitions are useful in advancing state-of-the art AI algorithms but generally require large expert annotated datasets. We have annotated and adjudicated a large dataset of chest radiographs to be made public with the goal of sparking innovation in this space. Because of the cumbersome and time-consuming nature of image labeling, we explored the value of using AI models to generate annotations for review. Utilization of this machine learning annotation (MLA) technique appeared to expedite our annotation process with relatively high sensitivity at the expense of specificity. Further research is required to confirm and better characterize the value of MLAs. Our adjudicated dataset is now available for public consumption in the form of a challenge.
Details
- Title: Subtitle
- Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset
- Creators
- Ross W. Filice - MedStar Georgetown University HospitalAnouk Stein - , New York, NY, USA.Carol C. Wu - The University of Texas MD Anderson Cancer CenterVeronica A. Arteaga - University of ArizonaStephen Borstelmann - UCF College of Medicine, 6850 Lake Nona Blvd, Orlando, FL, 32827, USA.Ramya Gaddikeri - Rush University Medical CenterMaya Galperin-Aizenberg - Hospital of the University of PennsylvaniaRitu R. Gill - Beth Israel Deaconess Medical CenterMyrna C. Godoy - The University of Texas MD Anderson Cancer CenterStephen B. Hobbs - University of KentuckyJean Jeudy - University of Maryland, BaltimoreParas C. Lakhani - Thomas Jefferson University HospitalArchana Laroia - University of IowaSundeep M. Nayak - Colorado Permanente Medical GroupMaansi R. Parekh - Thomas Jefferson University HospitalPrasanth Prasanna - Radiology AssociatesPalmi Shah - Rush University Medical CenterDharshan Vummidi - University of Michigan–Ann ArborKavitha Yaddanapudi - University of ArizonaGeorge Shih - Cornell University
- Resource Type
- Journal article
- Publication Details
- Journal of digital imaging, Vol.33(2), pp.490-496
- Publisher
- Springer International Publishing
- DOI
- 10.1007/s10278-019-00299-9
- PMID
- 31768897
- PMCID
- PMC7165201
- ISSN
- 0897-1889
- eISSN
- 1618-727X
- Language
- English
- Date published
- 11/25/2019
- Academic Unit
- Radiology; Internal Medicine
- Record Identifier
- 9984318806402771
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