Comparison of aspiration catheter performance using adaptive pulsatile aspiration in an in vitro thrombectomy model.

OBJECTIVE: Aspiration with a pump or syringe is a mainstay of mechanical thrombectomy (MT) for acute ischemic stroke (AIS), but this technology has seen minimal evolution. Non-continuous adaptive pulsatile aspiration (APA) has been proposed as a potential alternative to standard continuous aspiration as a means of improving revascularization efficiency. METHODS: Using a pathophysiological flow bench model with a synthetic clot, we performed in vitro thrombectomies using the ALGO® Von Vascular, Inc. (Sunrise, FL) APA pump. A total of 25 FDA-approved aspiration catheters were tested, representing inner diameters (ID) from 0.035 in. to 0.088 in. The pump was used in 30 trials with each catheter to remove a simulated M1 occlusion. Revascularization, clot ingestion, time to clot removal, and distal embolization were measured. RESULTS: Among catheters tested using APA, first-pass TICI 3 revascularization was achieved in 100% of the 750 thrombectomy trials using 25 different catheters. There were no distal emboli detected in any trial run. Complete clot ingestion into the pump collection chamber was achieved in 87% to 100% of trials (overall 95%) with clot in the remaining trials corking within the catheter and removed from the model. Time from clot contact to clot removal ranged from 11 s to 90 s (mean 22.6 s, SD 16.8 s), which was negatively correlated with catheter ID (p = 0.007). CONCLUSION: APA via the Von Vascular, Inc. ALGO® pump achieved a high success rate in an in vitro MT model. All catheters tested with the pump achieved complete reperfusion in all trials, and complete clot ingestion into the pump was seen in a majority of trials. The promising in vitro performance of APA using multiple catheters warrants future in vivo investigation.

Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics.

Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting.