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

Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety

While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain are by and large lacking. The absence of publicly available databases that host well-curated datasets that can be used to develop and validate AI /ML models represents one likely barrier. Thus, we took three previously published datasets, which we further cleaned and annotated, and made them publicly available in a repository called Cornell Food Safety ML Repository. The selected datasets include (i) presence or absence of Listeria spp. in soil samples collected across the U.S. with paired metadata for soil properties, geolocation, climate, and surrounding land use, (ii) presence or absence of Salmonella and Campylobacter in young chicken carcasses tested in processing facilities with associated meteorological and temporal metadata, and (iii) presence or absence of fecal contamination as well as E. coli concentration in New York watersheds with associated metadata for land use, water attributes, and meteorological factors. These datasets can serve as benchmark datasets for developing ML models. To demonstrate the utility of the repository, we developed customizable scripts as well as LazyPredict (a quick screening method) scripts for training different types of ML models using the shared datasets. While this repository provides an important starting point that will allow for the development and testing of ML models to predict foodborne pathogens contamination in different sources, the inclusion of further datasets is clearly needed to advance this field. This paper thus includes a call to action for the deposit of well-curated datasets that can be used for further development of predictive models in food safety. This paper will also discuss the benefits of such public databases, including the assessment of data-sharing scenarios using existing privacy-preserving techniques.

"CLADE-FINDER": Candida auris Lineage Analysis Determination by Fourier Transform Infrared Spectroscopy and Artificial Neural Networks

In 2019, Candida auris became the first fungal pathogen included in the list of the urgent antimicrobial threats by the Centers for Disease Control (CDC). Short tandem repeat (STR) analysis and whole-genome sequencing (WGS) are considered the gold standard, and can be complemented by other molecular methods, for the genomic surveillance and clade classification of this multidrug-resistant yeast. However, these methods can be expensive and require time and expertise that are not always available. The long turnaround time is especially not compatible with the speed needed to manage clonal transmission in healthcare settings. Fourier transform infrared (FTIR) spectroscopy, a biochemical fingerprint approach, has been applied in this study to a set of 74 C. auris isolates belonging to the five clades of C. auris (I-V) in combination with an artificial neural network (ANN) algorithm to create and validate "CLADE-FINDER", a tool for C. auris clade determination. The CLADE-FINDER classifier allowed us to discriminate the four primary C. auris clades (I-IV) with a correct classification for 96% of the samples in the validation set. This newly developed genotyping scheme can be reasonably applied for the effective epidemiological monitoring and management of C. auris cases in real time.

Application of machine learning based genome sequence analysis in pathogen identification

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

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

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.

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.