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

Abstract

Youqub Kashif, Mian Zayn and Leventhal Gary

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.