In this paper, a multiple transmitter local drug delivery system associated with encapsulated drug transmitters is investigated. One of the limitations of drug delivery systems is the reservoir capacity. In order to improve the lifetime of drug transmitting nanomachines, and, hence, the longevity of drug delivery scenario, the system is associated with encapsulated drug transmitters. Encapsulated drugs are incapable of reaction with the environment unless they are unpacked in a drug transmitter nanomachine. Therefore, far-reaching transmitters do not have harmful effects on the healthy parts of the body. The advantage of this protocol is to increase the time interval between consecutive administrations without increased toxicity. As a result, it improves the mental health of patients and reduces the costs of treatment. The lifetime of this drug delivery system depends on the distribution and topology of encapsulated drug transmitters rather than their rates. Finally, a lower bound is derived on the expected lifetime of a Poisson distributed random network of nanomachines.

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PurposeDespite the availability of commercial artificial intelligence (AI) tools for large vessel occlusion (LVO) detection, there is paucity of data comparing traditional machine learning and deep learning solutions in a real-world setting. The purpose of this study is to compare and validate the performance of two AI-based tools (RAPID LVO and CINA LVO) for LVO detection.Materials and methodsThis was a retrospective, single center study performed at a comprehensive stroke center from December 2020 to June 2021. CT angiography (n = 263) for suspected stroke were evaluated for LVO. RAPID LVO is a traditional machine learning model which primarily relies on vessel density threshold assessment, while CINA LVO is an end-to-end deep learning tool implemented with multiple neural networks for detection and localization tasks. Reasons for errors were also recorded.ResultsThere were 29 positive and 224 negative LVO cases by ground truth assessment. RAPID LVO demonstrated an accuracy of 0.86, sensitivity of 0.90, specificity of 0.86, positive predictive value of 0.45, and negative predictive value of 0.98, while CINA demonstrated an accuracy of 0.96, sensitivity of 0.76, specificity of 0.98, positive predictive value of 0.85, and negative predictive value of 0.97.ConclusionBoth tools successfully detected most anterior circulation occlusions. RAPID LVO had higher sensitivity while CINA LVO had higher accuracy and specificity. Interestingly, both tools were able to detect some, but not all M2 MCA occlusions. This is the first study to compare traditional and deep learning LVO tools in the clinical setting.

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