Deep venous thrombosis identification from analysis of ultrasound data.

Abstract

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. 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 [Formula: see text] for measuring class separability. Seven optimal combined features were found with 100% sensitivity and 100% specificity, with the best combined feature having a [Formula: see text] value over two orders of magnitude greater than the best individual DVT measure. 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.