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Bioengineering
Special Issues
ML and AI for Augmented Biosensing Applications
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ML and AI for Augmented Biosensing Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 4215

Special Issue Editor

Dr. Rossana Madrid

E-Mail Website
Guest Editor
Laboratorio de Medios e Interfases (LAMEIN), DBI, FACET, Universidad Nacional de Tucumán, Av. Independencia 1800, Tucumán 4000, Argentina
Interests: biosensors and bioelectronics; microfluidics; nanotechnology and nanobiosensors; electrochemical impedance spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence is already with us. It is a powerful tool that allows tasks that traditionally require a high level of human cognition to be performed more intelligently and efficiently. The development of biosensors, whether for biomedical, environmental, food, or agricultural applications, has escalated enormously in recent years. The systematic application of modern computer methods and machine learning (ML) on a large scale and massive amounts of data in the field and in the laboratory allows generating revolutionary advances in the knowledge and detection of diseases, quantification and evaluation of contaminants in foods and in the environment, as well as the study of specific markers for various applications, which are just being considered and are of great economic importance and social impact. The application of these new IT tools is undoubtedly enabling the development of new systems that are much more intelligent and efficient and allow many of the decisions to be offloaded to automatic and autonomous systems. However, there is still a long way to go in the area of augmented biosensor technology with this modern approach to computing. We propose this Special Issue on this 10th anniversary to showcase all the advances in the area, which will undoubtedly be of wide interest to our readers.

Topics include, but are not limited to, the following:

  • Deep learning and machine learning for biosensing applications;
  • Intelligent wearables and implantable biosensors;
  • AI for environmental biosensor applications;
  • AI biosensors for precision agriculture;
  • AI-/ML-assisted development of novel bioreceptors and substrates for biosensing applications.

Dr. Rossana Madrid
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Bioengineering 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 2700 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.

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Published Papers (2 papers)

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13 pages, 3189 KiB  
Article
Enhancing Fermentation Process Monitoring through Data-Driven Modeling and Synthetic Time Series Generation
by Hyun J. Kwon, Joseph H. Shiu, Celina K. Yamakawa and Elmer C. Rivera
Bioengineering 2024, 11(8), 803; https://doi.org/10.3390/bioengineering11080803 - 8 Aug 2024
Cited by 1 | Viewed by 2150
Abstract
Soft sensors based on deep learning regression models are promising approaches to predict real-time fermentation process quality measurements. However, experimental datasets are generally sparse and may contain outliers or corrupted data. This leads to insufficient model prediction performance. Therefore, datasets with a fully [...] Read more.
Soft sensors based on deep learning regression models are promising approaches to predict real-time fermentation process quality measurements. However, experimental datasets are generally sparse and may contain outliers or corrupted data. This leads to insufficient model prediction performance. Therefore, datasets with a fully distributed solution space are required that enable effective exploration during model training. In this study, the robustness and predictive capability of the underlying model of a soft sensor was improved by generating synthetic datasets for training. The monitoring of intensified ethanol fermentation is used as a case study. Variational autoencoders were employed to create synthetic datasets, which were then combined with original datasets (experimental) to train neural network regression models. These models were tested on original versus augmented datasets to assess prediction improvements. Using the augmented datasets, the soft sensor predictive capability improved by 34%, and variability was reduced by 82%, based on R2 scores. The proposed method offers significant time and cost savings for dataset generation for the deep learning modeling of ethanol fermentation and can be easily adapted to other fermentation processes. This work contributes to the advancement of soft sensor technology, providing practical solutions for enhancing reliability and robustness in large-scale production. Full article
(This article belongs to the Special Issue ML and AI for Augmented Biosensing Applications)
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Review

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15 pages, 1081 KiB  
Review
Introduction of AI Technology for Objective Physical Function Assessment
by Nobuji Kouno, Satoshi Takahashi, Masaaki Komatsu, Yusuke Sakaguchi, Naoaki Ishiguro, Katsuji Takeda, Kyoko Fujioka, Ayumu Matsuoka, Maiko Fujimori and Ryuji Hamamoto
Bioengineering 2024, 11(11), 1154; https://doi.org/10.3390/bioengineering11111154 - 16 Nov 2024
Cited by 1 | Viewed by 1209
Abstract
Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could [...] Read more.
Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could predict physical function scores from various patient data sources and reviewed methods to measure objective physical function using this technology. This review included relevant articles published in English that were retrieved from PubMed. These studies utilized AI technology to predict physical function indices from patient data extracted from videos, sensors, or electronic health records, thereby eliminating manual measurements. Studies that used AI technology solely to automate traditional evaluations were excluded. These technologies are recommended for future clinical systems that perform repeated objective physical function assessments in all patients without requiring extra time, personnel, or resources. This enables the detection of minimal changes in a patient’s condition, enabling early intervention and enhanced outcomes. Full article
(This article belongs to the Special Issue ML and AI for Augmented Biosensing Applications)
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