1 research outputs found
Automated Prediction of Hepatic Arterial Stenosis
Title from PDF of title page viewed June 20, 2017Thesis advisor: Deendayal DinakarpandianVitaIncludes bibliographical references (pages 41-43)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Several thousand life-saving liver transplants are performed each year. One of the causes
of early transplant failure is arterial stenosis of the anastomotic junction. Early detection of
transplant arterial stenosis can help prevent transplant failure and the need to re-transplant.
Doppler ultrasound with manual measurements is the most common screening method, but it
suffers from poor specificity when thresholded to reduce false negatives. Positive screening
cases proceed to angiography, which is an invasive and expensive procedure. A more accurate
test could decrease the number of normal patients who would have to undergo this invasive
diagnostic procedure. Machine learning models have shown promise in determining stenosis in
the carotid artery; however, they have yet to be tested on the less ideal data hepatic arteries
generate. Software has been created to extract liver artery Doppler ultrasound information in an
automated fashion to predict stenosis. A turnkey approach is utilized to refine the region prior to
extraction. Current methods of extraction generate waveforms with an average percent error per
pixel of 6.5 percent from a human drawn waveform. Single feature models and machine
learning models performed similarly when predicting stenosis; however, when thresholded for
high sensitivity (greater than 0.90), random forest models had the highest specificity at 1.0
sensitivity and 0.60 specificity.Introduction -- Literature review -- Methodology -- Results -- Future wor