Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset

• Published in: Journal of digital imaging Vol. 33; no. 2; pp. 490 - 496
• Main Authors: Filice, Ross W., Stein, Anouk, Wu, Carol C., Arteaga, Veronica A., Borstelmann, Stephen, Gaddikeri, Ramya, Galperin-Aizenberg, Maya, Gill, Ritu R., Godoy, Myrna C., Hobbs, Stephen B., Jeudy, Jean, Lakhani, Paras C., Laroia, Archana, Nayak, Sundeep M., Parekh, Maansi R., Prasanna, Prasanth, Shah, Palmi, Vummidi, Dharshan, Yaddanapudi, Kavitha, Shih, George
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-04-2020; Springer Nature B.V
• Subjects: Algorithms; Annotations; Artificial Intelligence; Chest; Crowdsourcing; Datasets; Datasets as Topic; Humans; Image analysis; Image detection; Image processing; Imaging; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; Original Paper; Pneumothorax; Pneumothorax - diagnostic imaging; Radiographs; Radiography; Radiology; X-Rays; Chest radiograph; Machine learning annotations; Public datasets; Pneumothorax; Artificial intelligence; Challenge
• ISSN: 0897-1889; 1618-727X
• DOI: 10.1007/s10278-019-00299-9

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.