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.

翻译：生物传感器的响应时间是安全关键应用（如医疗诊断）中的关键指标，早期诊断可显著改善患者预后。然而，生物传感器达到最终平衡状态的速度可能受限于质量传递不良和长分子扩散时间，这会增加目标分子到达生物传感器活性传感区域所需的时间。虽然优化系统和传感器设计可以促使分子更快地到达传感元件，但一种更简单且广泛适用于所有传感器平台的互补性方法是通过时间序列预测来预估最终的稳态传感器响应。在本研究中，我们展示了长短时记忆（LSTM）网络集成能够利用少量初始时变生物传感器测量数据准确预测平衡态生物传感器响应，从而将响应时间平均缩短18.6倍，中位数缩短5.1倍。该模型集成还能同时进行不确定性估计，这对于确保预测可信度及后续安全相关决策至关重要。该方法通过多通道流通池对暴露于缓冲蛋白溶液的多孔硅生物传感器进行实时实验数据验证，该装置可并行自动测量100个多孔硅生物传感器。利用LSTM网络集成及其相关不确定性量化实现的传感器响应时间显著提升，为可靠且更快速的生物传感器铺平了道路，从而能够实现更快速的医疗诊断以改善患者预后和医疗可及性，同时更快地识别食品和环境中的毒素。