Tanzania
Research Article
Machine Learning Algorithms Dramatically Improve the Accuracy and Time
to Diagnosis of Pulmonary Embolisms
Author(s): Youqub Kashif, Mian Zayn and Leventhal GaryYouqub 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 a.. Read More»
DOI:
10.4172/2161-105X.1000408
Pulmonary & Respiratory Medicine received 1690 citations as per Google Scholar report