Ovine Iliac Vein Model for Endovascular Thrombectomy of Acute Deep Venous Thrombosis

An ovine iliac vein thrombosis model was devised to test a wall-contacting rotational thrombectomy device. Thrombosis was successfully induced in 9 sheep with an average clot length of 31 mm ± 12 and >60% vessel occlusion on angiography. The thrombus was subsequently removed, maintaining normal intraoperative pulmonary arterial pressure (5.9 mm Hg ± 3.6) and complete distal reperfusion after thrombectomy. Additionally, the sheep were without signs of vascular trauma or embolic complications on gross necropsy and histopathologic analysis. The findings from this study support the use of an ovine iliac deep vein thrombosis model for testing of a lower extremity thrombectomy device.     

Utilization of and Outcomes Associated with Intravascular Ultrasound during Deep Venous Stent Placement among Medicare Beneficiaries

PurposeTo evaluate temporal trends, practice variation, and associated outcomes with the use of intravascular ultrasound (US) during deep venous stent placement among Medicare beneficiaries.Materials and MethodsAll lower extremity deep venous stent placement procedures performed between January 1, 2017, and December 31, 2019 among Medicare beneficiaries were included. Temporal trends in intravascular US use were stratified by procedural setting and physician specialty. The primary outcome was a composite of 12-month all-cause mortality, all-cause hospitalization, or repeat target vessel intervention. The secondary outcome was a composite of 12-month stent thrombosis, embolization, or restenosis.ResultsAmong the 20,984 deep venous interventions performed during the study period, 15,184 (72.4%) utilized intravascular US. Moderate growth in intravascular US use was observed during the study period in all clinical settings. There was a variation in the use of intravascular US among all operators (median, 77.3% of cases; interquartile range, 20.0%–99.2%). In weighted analyses, intravascular US use during deep venous stent placement was associated with a lower risk of both the primary (adjusted hazard ratio, 0.72; 95% confidence interval [CI], 0.69–0.76; P < .001) and secondary (adjusted hazard ratio, 0.32; 95% CI, 0.27–0.39; P < .001) composite end points.ConclusionsIntravascular US is frequently used during deep venous stent placement among Medicare beneficiaries, with further increase in use from 2017 to 2019. The utilization of intravascular US as part of a procedural strategy was associated with a lower cumulative incidence of adverse outcomes after the procedure, including venous stent thrombosis and embolization.     

Assessment of a novel deep learning-based marker-less motion capture system for gait study

BackgroundMarker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes.Research questionHow does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters?MethodsThe deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters.ResultsThe marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were −3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references.SignificanceThe developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.     

Automatic segmentation of thoracic CT images using three deep learning models

PurposeDeep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.Materials and methodsWe used CT images of 52 patients with breast cancer from a public dataset. Automatic segmentation of the lungs, the heart and a CTV was performed using three models based on the U-Net architecture. Three metrics were used to quantify and compare the segmentation results obtained with these models: the Dice similarity coefficient (DSC), the Jaccard coefficient (J) and the Hausdorff distance (HD).ResultsThe obtained values of DSC, J and HD were presented for each segmented organ and for the three models. Examples of automatic segmentation were presented and compared to the corresponding ground truth delineations. Our values were also compared to recent results obtained by other authors.ConclusionThe performance of three DL models was evaluated for the delineation of the lungs, the heart and a CTV. This study showed clearly that these 2D models based on the U-Net architecture can be used to delineate organs in CT images with a good performance compared to other models. Generally, the three models present similar performances. Using a dataset with more CT images, the three models should give better results.