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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 12/2015

01.12.2015 | Original Article

Deep venous thrombosis identification from analysis of ultrasound data

verfasst von: Julian Guerrero, James A. McEwen, Bassam A. Masri, Savvakis Nicolaou, Septimiu E. Salcudean

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 12/2015

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Abstract

Purpose

The purpose of this research was to determine whether combined ultrasound- and sensor-based compressibility and augmented blood flow measures yielded better results for DVT detection than for the individual measures alone.

Methods

Twenty-six limbs from 19 patients were scanned using a sensorized ultrasound DVT screening system, and compressibility and flow measures were obtained at 125 locations. Results from conventional compression ultrasound examination were used as gold standard, with seven vessels (four patients) positive for DVT. A classification approach was used to combine the individual DVT measures per vessel and generate an optimal feature for every possible combination of individual measures. Sensitivity and specificity were calculated for the individual measures and for all combined measures, as was a usefulness criteria \(J\) for measuring class separability.

Results

Seven optimal combined features were found with 100 % sensitivity and 100 % specificity, with the best combined feature having a \(J\) value over two orders of magnitude greater than the best individual DVT measure.

Conclusions

The proposed approach for DVT detection combines different aspects of thrombus detection in a novel way generating a quantifiable measure and outperforms any of the individual measures when used independently. All of the combined measures included the flow measure as well as the slope compressibility measure, which uses the magnitude of the force applied by the ultrasound probe, suggesting that these measurements provide important information when characterizing DVT.

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Metadaten
Titel
Deep venous thrombosis identification from analysis of ultrasound data
verfasst von
Julian Guerrero
James A. McEwen
Bassam A. Masri
Savvakis Nicolaou
Septimiu E. Salcudean
Publikationsdatum
01.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 12/2015
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-015-1214-1

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