Abstract
The response time of a biosensor is a crucial metric in safety-critical applications such as medical diagnostics where an earlier diagnosis can markedly improve patient outcomes. However, the speed at which a biosensor reaches a final equilibrium state can be limited by poor mass transport and long molecular diffusion times that increase the time it takes target molecules to reach the active sensing region of a biosensor. While optimization of system and sensor design can promote molecules reaching the sensing element faster, a simpler and complementary approach for response time reduction that is widely applicable across all sensor platforms is to use time-series forecasting to predict the ultimate steady-state sensor response. In this work, we show that ensembles of long short-term memory (LSTM) networks can accurately predict equilibrium biosensor response from a small quantity of initial time-dependent biosensor measurements, allowing for significant reduction in response time by a mean and median factor of improvement of 18.6 and 5.1, respectively. The ensemble of models also provides simultaneous estimation of uncertainty, which is vital to provide confidence in the predictions and subsequent safety-related decisions that are made. This approach is demonstrated on real-time experimental data collected by exposing porous silicon biosensors to buffered protein solutions using a multi-channel fluidic cell that enables the automated measurement of 100 porous silicon biosensors in parallel. The dramatic improvement in sensor response time achieved using LSTM network ensembles and associated uncertainty quantification opens the door to trustworthy and faster responding biosensors, enabling more rapid medical diagnostics for improved patient outcomes and healthcare access, as well as quicker identification of toxins in food and the environment.
Abstract (translated)
生物传感器响应时间的延迟是一个关键的安全应用指标，如医疗诊断，因为较早的诊断可以显著改善患者的治疗效果。然而，生物传感器达到最终平衡状态的速度可能受到质粒传输和长分子扩散时间的限制，这会延长目标分子到达传感器活性传感区域的时间，从而增加传感器响应时间。虽然对系统和传感器设计的优化可以促进分子更快地到达传感器元件，但简化和互补的方法来降低响应时间，在所有传感器平台上具有广泛的应用，是使用时间序列预测来预测 ultimate steady-state sensor response。 在这项工作中，我们证明了长短期记忆（LSTM）网络的集成可以从初始时间相关生物传感器测量的小量数据准确预测平衡生物传感器响应，从而通过平均和均值改进响应时间。模型的集成还提供了同时估计不确定性，这对于提供对预测和后续安全相关决策的信心至关重要。这种方法通过对使用多通道流体细胞暴露具有较大通量检测100个孔硅生物传感器进行实时实验，证明了其可实现性和有效性。 通过使用LSTM网络集成，我们实现了传感器响应时间的戏剧性改进和相关不确定性量的估计，这为可信和更快响应的生物传感器打开了大门，有助于改善患者的治疗效果和提高医疗资源的利用效率，以及更快速地检测食品和环境中的毒素。
URL
https://arxiv.org/abs/2404.17144
