Why we fail: mechanisms and co-factors of unsuccessful thrombectomy in acute ischemic stroke

Purpose Mechanical thrombectomy (MT) is an effective treatment for patients suffering from acute ischemic stroke. However, recanalization fails in about 16.5% of interventions. We report our experience with unsuccessful MT and analyze technical reasons plus patient-related parameters for failure. Methods Five hundred ninety-six patients with acute ischemic stroke in the anterior circulation and intention to perform MT with an aspiration catheter and/or stent retriever were analyzed. Failure was defined as 0, 1, or 2a on the mTICI scale. Patients with failing MT were analyzed for interventional progress and compared to patients with successful intervention, whereby parameters included demographics, medical history, stroke presentation, and treatment. Results One hundred of the 596 (16.8%) interventions failed. In 20 cases, thrombus could not be accessed or passed with the device. Peripheral arterial occlusive disease is common in those patients. In 80 patients, true stent retriever failure occurred. In this group, coagulation disorders are associated with poor results, whereas atrial fibrillation is associated with success. The administration of intravenous thrombolysis and intake of nitric oxide donors are associated with recanalization success. Intervention duration was significantly longer in the failing group. Conclusion In 20% of failing MT, thrombus cannot be reached/passed. Direct carotid puncture or surgical arterial access could be considered in these cases. In 80% of failing interventions, thrombus can be passed with the device, but the occluded vessel cannot be recanalized. Rescue techniques can be an option. Development of new devices and techniques is necessary to improve recanalization rates. Assessment of pre-existing illness could sensitize for occurring complications

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions