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ISSN: 2161-105X

Journal of Pulmonary & Respiratory Medicine
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Research Article

Machine Learning Algorithms Dramatically Improve the Accuracy and Time to Diagnosis of Pulmonary Embolisms

Youqub Kashif1, Mian Zayn2* and Leventhal Gary3

1Pulmonary and critical care fellow, Mayo Clinic, Scottsdale Arizona, USA

2Research Intern, Atlantis Research Institute, USA

3Data Analytics and Machine Learning, San Francisco, CA, USA

*Corresponding Author:
Mian Zayn
Atlantis Research Institute, Atlantis Health Systems
390 Enterprise, CT Bloomfield, Michigan, United States
Tel: 4802540478
E-mail: [email protected]

Received date: February 11, 2017; Accepted date: May 23, 2017; Published date: May 26, 2017

Citation: Kashif Y, Zayn M, Gary L (2017) Machine Learning Algorithms DramaticallyImprove the Accuracy and Time to Diagnosis of Pulmonary Embolisms. J Pulm Respir Med 7:408. doi: 10.4172/2161-105X.1000408

Copyright: © 2017 Kashif Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Acute pulmonary embolism is a common diagnostic challenge across the all hospitals in the US. Diagnosis can be delayed due to a number of variables including, but not limited to, the diagnostic time in medical imaging. The presented algorithm offers a solution to such delays by allowing treating physicians an accurate preliminary report. This gained time advantage should translate into a faster treatment response by the ED team. Moreover, the algorithm is designed to accurately depict pulmonary artery and veins and accounts for respiratory artifact during scan acquisition. As second and third pass search is initiated, the algorithm continues to “learn” upon the subsequent pass. Hence, each application is produces greater diagnostic accuracy. We hope this abstract clearly outlines how the latest developments in machine learning algorithms can aid in diagnostic fidelity of acute embolic events.

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