Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

Michael R Milne¹, Hassan K Ahmad², Quinlan D Buchlak^2,3,4, Nazanin Esmaili^3,5, Cyril Tang², Jarrel Seah^2,6, Nalan Ektas², Peter Brotchie², Thomas H Marwick⁷, Catherine M Jones^2,8,9

¹Annalise.ai, Sydney, Australia - michael.milne@annalise.ai.

²Annalise.ai, Sydney, Australia.

³School of Medicine, University of Notre Dame Australia, Sydney, Australia.

⁴Department of Neurosurgery, Monash Health, Melbourne, Australia.

⁵Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.

⁶Department of Radiology, Alfred Health, Melbourne, Australia.

⁷Baker Heart and Diabetes Institute, Melbourne, Australia.

⁸I-MED Radiology Network, Brisbane, Australia.

⁹Faculty of Medicine and Health, University of Sydney, Sydney, Australia.

PubMed DOI

Abstract

The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have remained constant for decades. The application of machine learning has the potential to substantially improve clinical workflow efficiency, pathology dete…

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