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

Ganjalizadeh, Vahid; Meena, Gopikrishnan G.; Stott, Matthew A.; Hawkins, Aaron R.; Schmidt, Holger

doi:10.1038/s41598-023-31694-6

Cited by 7 publications

(3 citation statements)

References 65 publications

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“…These methods can help to sort and analyze complex datasets to distinguish signals corresponding to different targets. For example, a neural network-powered optical biosensor was recently reported that can simultaneously detect multiple Klebsiella pneumonia bacterial biomarkers in real time with an accuracy of 99.8% [ 175 ].…”

Section: Artificial Intelligence In Biosensingmentioning

confidence: 99%

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Flynn,

Chang

2024

Diagnostics

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The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

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Section: Artificial Intelligence In Biosensingmentioning

confidence: 99%

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Flynn,

Chang

2024

Diagnostics

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“…Many crucial features of previous instruments are shared by the polarization-maintaining fibercoupled device, such as the comparatively low extra power transmission loss and minimal polarisation phase crosstalk coefficient [28]. Additionally, it has the ability to automatically retain the linearly polarised spectrum created from the fibre transmission's channel signal.…”

Section: Polarized Fiber Couplementioning

confidence: 99%

Enhancing Multimode Fibre Optic Communication through Deep Learning-based Mode Identification

Wang,

Liu

2023

J. Comb. Math. Comb. Comput.

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Multimode fibre optic communication systems, employing mode/mode group multiplexing, present challenges in accurately identifying numerous modes and mode groups for improved performance. In this study, we propose an intelligent identification model utilizing a fully convolutional neural network (CNN) to precisely identify multimode fibre modes and their clusters. The model is simulated and experimentally validated, considering noise influences on linear polarisation modes. Using a platform with OM2 multimode fibre and a multiplane optical conversion mode multiplexer, we capture optical field information for 10 modes and their corresponding mode groups. Extensive data are employed for training and validation, achieving a 100% recognition rate for all modes and mode groups in experiments. Notably, when employing a 44-photodetector array, an impressive 98.3% recognition efficiency is attained, showcasing the potential of deep learning in advancing multimode fibre optic communication systems.

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“…Additionally, AI filtering techniques can address the cross-sensitivity of multiple biomarker sensing, contributing to more accurate and reliable measurements in healthcare applications [23]. Moreover, it was shown that a deep neural network (DNN) for the multiplex detection of single particles and molecular biomarkers can be employed [24]. The AI model could integrate rapid wavelet particle detection with short-time Fourier Transform analysis, followed by DNN identification on a specialized AI device.…”

Section: Introductionmentioning

confidence: 99%

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

Shajari,

Kuruvinashetti,

Komeili

et al. 2023

Sensors

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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.

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

Cited by 7 publications

References 65 publications

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Enhancing Multimode Fibre Optic Communication through Deep Learning-based Mode Identification

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

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