Machine learning at the edge for AI-enabled multiplexed pathogen detection

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.

Adaptive time modulation technique for multiplexed on-chip particle detection across scales

Integrated optofluidic biosensors have demonstrated ultrasensitivity down to single particle detection and attomolar target concentrations. However, a wide dynamic range is highly desirable in practice and can usually only be achieved by using multiple detection modalities or sacrificing linearity. Here, we demonstrate an analysis technique that uses temporal excitation at two different time scales to simultaneously enable digital and analog detection of fluorescent targets. We demonstrated the seamless detection of nanobeads across eight orders of magnitude from attomolar to nanomolar concentration. Furthermore, a combination of spectrally varying modulation frequencies and a closed-loop feedback system that provides rapid adjustment of excitation laser powers enables multiplex analysis in the presence of vastly different concentrations. We demonstrated this ability to detect across scales via an analysis of a mixture of fluorescent nanobeads at femtomolar and picomolar concentrations. This technique advances the performance and versatility of integrated biosensors, especially toward point-of-use applications.