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Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge

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A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 20171

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Dr. Li Ren

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Guest Editor

Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China
Interests: microfluidic; biosensors; organ-on-a-chip technology

Prof. Dr. Benoît Piro

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Guest Editor

Chemistry Department, Université Paris Cité, F-75006 Paris, France
Interests: bioelectrochemistry; biosensors; bioelectronics; transistor; nanomaterials
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Dear Colleagues,

In recent decades, biosensors have become an indispensable technology in fields such as disease diagnosis, environmental monitoring, and food safety. However, traditional biosensors face challenges in sensitivity, selectivity, and real-time monitoring capabilities. With the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, the field of biosensors has ushered in a revolution. The emergence of AI and ML technologies provides new possibilities for improving the performance of biosensors. By efficiently extracting signal features and accurately distinguishing differences between signals, AI algorithms greatly improve the efficiency of data analysis and can optimize sensor design and performance through large-scale simulation predictions. In addition, the application of AI and ML has provided new ideas for the intelligence and automation of biosensors, promoting the development of biosensor technology to a higher level.

This Special Issue will cover the basic principles and key technologies of AI and ML in biosensors, including deep learning, pattern recognition, and predictive modeling, as well as the application of cases of these technologies in disease diagnosis, treatment monitoring, and patient management. In particular, AI and ML face challenges in biosensors, such as data privacy, model interpretability, and the need for interdisciplinary collaboration. Finally, we will summarize the future development directions of AI and ML in biosensors, including integrating more modalities of data, improving the generalization ability of models, and developing more efficient computing frameworks. This Special Issue will provide a platform for researchers to exchange knowledge of the applications of AI and ML in the field of biosensors, promote interdisciplinary cooperation, and drive the innovation and application of related technologies. We hope that this Special Issue can provide valuable references for researchers in the field of biosensors and stimulate new research ideas.

A wide range of studies addressing these issues is welcome, including both original research and review articles that investigate, but are not limited to, the following topics:

The application of AI and ML in improving the sensitivity and selectivity of biosensors.
Utilizing AI and ML for denoising and improving the detection accuracy of biosensor data.
The role of AI and ML in highly specific recognition, classification, as well as in the detection of biosensors.
Application of AI and ML algorithms in biosensor networks and multi-sensor data fusion.
Application of AI and ML in single-molecule bioelectric sensors, and their potential in single-molecule sequencing, low abundance detection, and real-time dynamic monitoring.

Dr. Li Ren
Prof. Dr. Benoît Piro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

biosensors
artificial intelligence
machine learning
precision medicine
biomarkers
point-of-care testing
digital health
smart connected healthcare
quality assessment and monitoring
diagnosis and prognosis

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Published Papers (10 papers)

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Research

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15 pages, 6831 KB

Open AccessArticle

Multi-Class Arrhythmia Detection from PPG Signals Based on VGG-BiLSTM Hybrid Deep Learning Model

by Shiyong Li, Jiaying Mo, Jiating Pan, Zhengguang Zheng, Qunfeng Tang and Zhencheng Chen

Biosensors 2026, 16(5), 235; https://doi.org/10.3390/bios16050235 - 23 Apr 2026

Viewed by 424

Abstract

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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16 pages, 5700 KB

Open AccessArticle

A Deep Learning-Based EIT System for Robust Gesture Recognition Under Confounding Factors

by Hancong Wu, Guanghong Huang, Wentao Wang and Yuan Wen

Biosensors 2026, 16(4), 200; https://doi.org/10.3390/bios16040200 - 1 Apr 2026

Viewed by 457

Abstract

Gesture recognition with electrical impedance tomography (EIT) is an enormous potential tool for human–machine interaction because of its low cost, low complexity and high temporal resolution. Although high-precision EIT-based gesture recognition has been achieved in ideal scenarios, ensuring its consistent performance under interference remains challenging. This article presents a novel method to alleviate the effect of confounding factors on EIT gesture recognition. An EIT armband was designed to mitigate the effect of contact impedance variation based on equivalent circuit analysis, and a spatial–temporal fusion network, named the Fold Atrous Spatial Pyramid Pooling-Gated Recurrent Unit (FASPP-GRU), was developed for robust gesture classification. The results showed that the proposed two-layer electrode maintained a stable contact impedance when its contact force with the skin was changed. Although confounding factors caused significant changes in baseline forearm impedance, FASPP-GRU achieved 80% accuracy under the effect of limb position changes and dynamic changes in muscle state over time, which outperforms conventional classifiers. With an 87 μs inference time, the proposed system shows enormous potential in real-time applications. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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12 pages, 2780 KB

Open AccessArticle

A Deep-Learning-Enhanced Ultrasonic Biosensing System for Artifact Suppression in Sow Pregnancy Diagnosis

by Xiaoying Wang, Jundong Wang, Ziming Gao, Xinjie Luo, Zitong Ding, Yiyang Chen, Zhe Zhang, Hao Yin, Yifan Zhang, Xuan Liang and Qiangqiang Ouyang

Biosensors 2026, 16(2), 75; https://doi.org/10.3390/bios16020075 - 27 Jan 2026

Viewed by 562

Abstract

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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15 pages, 2570 KB

Open AccessArticle

Machine Learning-Assisted SERS Platform for Rapid and Quantitative Discrimination of Shiga Toxin-Producing E. coli Serotypes

by Yuting Liu, Jiyu Feng, Xinyi Chen, Mingyu Cheng, Jinglan Zhang, Xu Ye, Yiping Zhao and Bin Ai

Biosensors 2025, 15(11), 740; https://doi.org/10.3390/bios15110740 - 4 Nov 2025

Viewed by 1188

Abstract

Rapid, sensitive, and specific detection of pathogenic Escherichia coli serotypes is crucial for food safety and public health. Here, we present a surface-enhanced Raman scattering (SERS) platform utilizing highly ordered silver nanorod (AgNR) arrays functionalized with vancomycin for efficient and selective bacterial capture. The system enables multiplexed, high-throughput analysis using a portable Raman spectrometer, achieving direct molecular fingerprinting of seven clinically relevant E. coli serotypes. Systematic optimization of AgNR length and vancomycin coating maximized SERS enhancement and capture efficiency. Advanced data analysis with linear discriminant analysis (LDA) provided robust discrimination among all serotypes and concentrations, achieving up to 100% classification accuracy in single-concentration models and an overall accuracy of 98.41% when all concentrations and serotypes were evaluated jointly. This integrated SERS approach demonstrates significant promise for rapid, on-site bacterial diagnostics and quantitative pathogen monitoring, paving the way for practical applications in food safety and clinical microbiology. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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23 pages, 5198 KB

Open AccessArticle

A Feasibility Study on Noninvasive Blood Glucose Estimation Using Machine Learning Analysis of Near-Infrared Spectroscopy Data

by Tae Wuk Bae, Byoung Ik Kim, Kee Koo Kwon and Kwang Yong Kim

Biosensors 2025, 15(11), 711; https://doi.org/10.3390/bios15110711 - 25 Oct 2025

Cited by 1 | Viewed by 3257

Abstract

This study explored the feasibility of noninvasive blood glucose (BG) estimation using near-infrared (NIR) spectroscopy with dog blood samples. A sensor module employing three representative wavelengths (770 nm, 850 nm, and 970 nm) was tested on an artificial blood vessel (ABV) and a thin pig skin (TPS) model. BG concentrations were adjusted through dilution and enrichment with injection-grade water and glucose solution, and reference values were obtained from three commercial invasive glucometers. Correlations between NIR spectral responses and glucose variations were quantitatively evaluated using linear, multiple, partial least squares (PLS), logistic regression, regularized linear models, and multilayer perceptron (MLP) analysis. The results revealed distinct negative correlations at 850 nm and 970 nm, identifying these wavelengths as promising candidates for noninvasive glucose sensing. Furthermore, an NIR–glucose database generated from actual dog blood was established, which may serve as a valuable resource for the development of future noninvasive glucose monitoring systems. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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17 pages, 3286 KB

Open AccessArticle

Haptens Optimization Using Molecular Modeling and Paper-Based Immunosensor for On-Site Detection of Carbendazim in Vegetable Products

by Wenjing Chen, Zhuzeyang Yuan, Kangliang Pan, Yu Wang, Xiaoqin Yu, Tian Guan, Jiahong Chen and Hongtao Lei

Biosensors 2025, 15(9), 625; https://doi.org/10.3390/bios15090625 - 19 Sep 2025

Cited by 3 | Viewed by 924

Abstract

Carbendazim is a benzimidazole fungicide widely used in the prevention and control of vegetable diseases. However, if misused, it may result in residues in agricultural products, not only reducing vegetable quality but also posing potential risks to human health. Currently, the on-site rapid detection technology for carbendazim still faces challenges, including insufficient antibody specificity and low sensitivity, which hinder its ability to meet practical regulatory requirements. Therefore, this study screened a rational hapten structure by applying a computer-aided hapten design and obtained a specific antibody. Compared to previous studies, the cross-reactivity rate of the antibody with thiabendazole-methyl was less than 0.1%, and the cross-reactivity rate with 2-aminobenzimidazole was 52.7% lower than that of the existing reported antibodies, which significantly improved the detection specificity of the method. Based on a high-specificity antibody, a gold nanoparticle-based lateral flow immunoassay (AuNPs-LFIA) for carbendazim was established. The detection limits of green beans and leeks are 3.80 μg/kg and 1.80 μg/kg, respectively, which still maintain high specificity in complex samples. Good agreement was also demonstrated between the results of blind samples detected by AuNPs-LFIA and LC-MS/MS, respectively. The establishment of AuNPs-LFIA provides an effective solution for the rapid and specific detection of carbendazim. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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29 pages, 872 KB

Open AccessArticle

The Impact of Heat Stress on Dairy Cattle: Effects on Milk Quality, Rumination Behaviour, and Reticulorumen pH Response Using Machine Learning Models

by Karina Džermeikaitė, Justina Krištolaitytė, Dovilė Malašauskienė, Samanta Arlauskaitė, Akvilė Girdauskaitė and Ramūnas Antanaitis

Biosensors 2025, 15(9), 608; https://doi.org/10.3390/bios15090608 - 15 Sep 2025

Cited by 4 | Viewed by 4447

Abstract

Heat stress has a major impact on dairy cow health and productivity, especially during early lactation. Conventional heat stress monitoring methods frequently rely on single indicators, such as the temperature–humidity index (THI), which may miss subtle physiological and metabolic responses. This study presents a novel threshold-based classification framework that integrates biologically meaningful combinations of environmental, behavioural, and physiological variables to detect early-stage heat stress responses in dairy cows. Six composite heat stress conditions (C1–C6) were developed using real-time THI, milk temperature, reticulorumen pH, rumination time, milk lactose, and milk fat-to-protein ratio. The study applied and assessed five supervised machine learning models (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF0, Neural Network (NN), and an Ensemble approach) trained on daily datasets gathered from early-lactation dairy cows fitted with intraruminal boluses and monitored through milking parlour sensor systems. The dataset comprised approximately 36,000 matched records from 200 cows monitored over 60 days. The highest classification performance was observed for RF and NN models, particularly under C1 (THI > 73 and milk temperature > 38.6 °C) and C6 (THI > 74 and milk temperature > 38.7 °C), with AUC values exceeding 0.90. SHAP analysis revealed that milk temperature, THI, rumination time, and milk lactose were the most informative features across conditions. This integrative approach enhances precision livestock monitoring by enabling individualised heat stress risk classification well before clinical or production-level consequences emerge. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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13 pages, 1462 KB

Open AccessArticle

Attention Affecting Response Inhibition in Overweight Adults with Food Addiction

by Xiaotong Liu, Guangying Pei, Jiayuan Zhao, Mengzhou Xu, Lizhi Cao, Jian Zhang, Tiantian Liu, Jinglong Wu, Shintaro Funahashi, Lei Ding and Li Wang

Biosensors 2025, 15(3), 180; https://doi.org/10.3390/bios15030180 - 13 Mar 2025

Cited by 2 | Viewed by 1799

Abstract

Food addiction is associated with attention bias and response inhibition deficits, while the relationship between these two domains is unclear. Participants with body mass index (BMI) ≥ 25 and exhibiting food addiction behaviors (FA group, n = 20) were compared with healthy controls (HC group, n = 23). We examined attention-inhibition mechanisms using resting EEG microstate analysis, food-cue-evoked event-related potentials (ERPs), and non-food Go/No-Go tasks. Overweight individuals with food addiction behaviors demonstrated attentional deficits, as indicated by abnormalities in microstate D and the P100 component. Importantly, both microstate D and the P100 component significantly predicted No-Go performance, linking neurophysiological markers to behavioral inhibition. This study suggests that attention bias may be an important interaction factor of response inhibition, providing novel mechanistic insights into food addiction. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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Review

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45 pages, 9328 KB

Open AccessReview

Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

by Hao Su, Hongcun Wang, Dandan Sang, Santosh Kumar, Dao Xiao, Jing Sun and Qinglin Wang

Biosensors 2026, 16(1), 58; https://doi.org/10.3390/bios16010058 - 13 Jan 2026

Viewed by 1069

Abstract

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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33 pages, 8681 KB

Open AccessReview

AI-Empowered Electrochemical Sensors for Biomedical Applications: Technological Advances and Future Challenges

by Yafeng Liu, Xiaohui Liu, Xuemei Wang and Hui Jiang

Biosensors 2025, 15(8), 487; https://doi.org/10.3390/bios15080487 - 28 Jul 2025

Cited by 24 | Viewed by 4126

Abstract

Biomarkers play a pivotal role in disease diagnosis, therapeutic efficacy evaluation, prognostic assessment, and drug screening. However, the trace concentrations of these markers in complex physiological environments pose significant challenges to efficient detection. It is necessary to avoid interference from non-specific signals, which may lead to misjudgment of other substances as biomarkers and affect the accuracy of detection results. With the rapid advancements in electrochemical technologies and artificial intelligence (AI) algorithms, intelligent electrochemical biosensors have emerged as a promising approach for biomedical detection, offering speed, specificity, high sensitivity, and accuracy. This review focuses on elaborating the latest applications of AI-empowered electrochemical biosensors in the biomedical field, including disease diagnosis, treatment monitoring, drug development, and wearable devices. AI algorithms can further improve the accuracy, sensitivity, and repeatability of electrochemical sensors through the screening and performance prediction of sensor materials, as well as the feature extraction and noise reduction suppression of sensing signals. Even in complex physiological microenvironments, they can effectively address common issues such as electrode fouling, poor signal-to-noise ratio, chemical interference, and matrix effects. This work may provide novel insights for the development of next-generation intelligent biosensors for precision medicine. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)

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