Endovascular Detection of Catheter-Thrombus Contact by Vacuum Excitation.

Jared Lawson, Madison Veliky,Colette P Abah, Mary S Dietrich,Rohan Chitale,Nabil Simaan

IEEE transactions on bio-medical engineering(2024)

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摘要
OBJECTIVE:The objective of this work is to introduce and demonstrate the effectiveness of a novel sensing modality for contact detection between an off-the-shelf aspiration catheter and a thrombus. METHODS:A custom robotic actuator with a pressure sensor was used to generate an oscillatory vacuum excitation and sense the pressure inside the extracorporeal portion of the catheter. Vacuum pressure profiles and robotic motion data were used to train a support vector machine (SVM) classification model to detect contact between the aspiration catheter tip and a mock thrombus. Validation consisted of benchtop accuracy verification, as well as user study comparison to the current standard of angiographic presentation. RESULTS:Benchtop accuracy of the sensing modality was shown to be 99.67%. The user study demonstrated statistically significant improvement in identifying catheter-thrombus contact compared to the current standard. The odds ratio of successful detection of clot contact was 2.86 (p=0.03) when using the proposed sensory method compared to without it. CONCLUSION:The results of this work indicate that the proposed sensing modality can offer intraoperative feedback to interventionalists that can improve their ability to detect contact between the distal tip of a catheter and a thrombus. SIGNIFICANCE:By offering a relatively low-cost technology that affords off-the-shelf aspiration catheters as clot-detecting sensors, interventionalists can improve the first-pass effect of the mechanical thrombectomy procedure while reducing procedural times and mental burden.
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关键词
aspiration,catheters,machine learning,sensing,stroke,thrombectomy
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