Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Kainz:2021:10.1101/2021.01.23.21249964,
author = {Kainz, B and Makropoulos, A and Oppenheimer, J and Deane, C and Mischkewitz, S and Al-Noor, F and Rawdin, AC and Stevenson, MD and Mandegaran, R and Heinrich, MP and Curry, N},
doi = {10.1101/2021.01.23.21249964},
publisher = {Cold Spring Harbor Laboratory},
title = {Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning},
url = {http://dx.doi.org/10.1101/2021.01.23.21249964},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:p>Deep Vein Thrombosis (DVT) is a blood clot most found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired.</jats:p><jats:p>We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT.</jats:p><jats:p>We train a deep learning algorithm on ultrasound videos from 246 healthy volunteers and evaluate on a sample size of 51 prospectively enrolled patients from an NHS DVT diagnostic clinic. 32 DVT-positive patients and 19 DVT-negative patients were included. Algorithmic DVT diagnosis results in a sensitivity of 93.8% and a specificity of 84.2%, a positive predictive value of 90.9%, and a negative predictive value of 88.9% compared to the clinical gold standard.</jats:p><jats:p>To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into a diagnostic pathway for DVT. Our approach is estimated to be cost effective at up to $150 per software examination, assuming a willingness to pay $26 000/QALY.</jats:p>
AU  - Kainz,B
AU  - Makropoulos,A
AU  - Oppenheimer,J
AU  - Deane,C
AU  - Mischkewitz,S
AU  - Al-Noor,F
AU  - Rawdin,AC
AU  - Stevenson,MD
AU  - Mandegaran,R
AU  - Heinrich,MP
AU  - Curry,N
DO  - 10.1101/2021.01.23.21249964
PB  - Cold Spring Harbor Laboratory
PY  - 2021///
TI  - Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning
UR  - http://dx.doi.org/10.1101/2021.01.23.21249964
ER  -
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