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Bioengineering
Special Issues
Machine Learning-Driven Innovations in Biomedical Signal and Image Processing
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Machine Learning-Driven Innovations in Biomedical Signal and Image Processing

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

Deadline for manuscript submissions: closed (15 November 2025) | Viewed by 5212

Special Issue Editors

Dr. Luis Coelho

E-Mail Website
Guest Editor
ISEP—School of Engineering, Polytechnic of Porto, Porto, Portugal
Interests: physiological signal processing; image processing; machine learning; neurodegenerative diseases; virtual reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João Paulo Ramos Teixeira

E-Mail Website
Guest Editor
Research Centre in Digitalization and Intelligent Robotics (CEDRI), Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Interests: speech synthesis; prosody; speech systems; modulation; prediction with neural networks; DNN; LSTM; time series forecast and biological signals analysis; namely EEG; ECG and voice
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João Paulo Pereira do Carmo

E-Mail Website
Guest Editor
Department of Electrical Engineering (SEL), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, Brazil
Interests: analog and digital integrated circuits; micromachining and micro/nanofabrication technologies for mixed-mode/RF systems; solid-state integrated sensors; microactuators and microsystems; micro/nanodevices for industrial and biomedical applications; wireless systems for sensors and actuators; optical sensors and actuators; material technology for microsystems; microprocessor/microcomputer-based instrumentation and data-acquisition systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The dramatic improvement in biomedical sensing technology has allowed us to acquire more and better information about the human body. The data sources encompass an enormous spectrum of areas, ranging from large phenomena, such as human gait analysis from wearable sensors or eye movement analysis for disease detection, to nanoscale phenomena, such as cell identification in histological microscopy or observing bone growth using micro-CT imaging. Hence, signal and image processing techniques have a central role in the extraction of meaningful information from such sources. In fact, advancements in signal and image processing techniques have allowed us to obtain improvements at a faster pace than the evolution of hardware. Such improvements, in such a wide landscape of data sources, have enhanced the need for advanced and specific technologies, tailored to each situation, either to improve quality or to estimate high-level information.

In addition, in recent years, artificial intelligence has been shown to offer high-performance mechanisms to deal with these situations, offering robust data models that are able to cope with large, nonlinear data spaces. Training algorithms have also become increasingly efficient, being able to keep up with the evolution of data models. Good generalization capabilities and high fidelity can be achieved, even with apparently limited or sparse data. Many of these systems outperform human capacities, and their use is becoming an established standard.

However, with such a fast evolution pace, the application landscape continues to grow, while many challenges still remain. For each type of signal or image source, improvements can be pursued in the following areas:

  • Data collection, compression, and visualization;
  • Data exploration;
  • Feature extraction, selection, enhancement, and analysis;
  • Data augmentation;
  • Model selection, tuning, and explainability;
  • Transfer learning;
  • Parameter space exploration.

The possibility of improving disease detection or enhancing therapies, boosting the quality of life of many people, makes this one of the most exciting current research areas.

For this Special Issue, prospective authors are invited to submit innovative research aimed at solving challenges in application areas such as, inter alia, clinical (diagnostic, rehabilitation, and monitoring) and biomedical research (histology, anatomy, physiology) and human–machine interfacing (acquisition technologies and stimulation). Some of the encompassed data sources include, but are not limited to, the following:

  • Signals: EEG, EMG, ECG, EOG, electroretinogram (ERG), evoked potentials, local field potentials, deep brain stimulation (open-/closed-loop), magnetoencephalography (MEG), actigraphy, and gait analysis;
  • Medical imaging: X-ray, PET, CT or micro-CT, PET-CT, MRI, and SPECT;
  • Biological and molecular imaging: photoacoustic/coherence tomography (PAT/OCT), MRS, mass spectrometry, optical imaging, phase-contrast imaging, and laser scanning confocal microscopy (LSCM);
  • Human–machine interaction: wearable data (gaze, dynamics, heart rate), stimulation (touch, vision), emotion, disease, and altered states (drunk, sleepiness). 

Dr. Luis Coelho
Prof. Dr. João Paulo Ramos Teixeira
Prof. Dr. João Paulo Pereira do Carmo
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. 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.

Keywords

  • signal processing
  • image processing
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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23 pages, 1305 KB  
Article
Constructing Artificial Features with Grammatical Evolution for the Motor Symptoms of Parkinson’s Disease
by Aimilios Psathas, Ioannis G. Tsoulos, Nikolaos Giannakeas, Alexandros Tzallas and Vasileios Charilogis
Bioengineering 2025, 12(12), 1318; https://doi.org/10.3390/bioengineering12121318 - 2 Dec 2025
Viewed by 457
Abstract
People with Parkinson’s disease often show changes in their movement abilities during the day, especially around the time they take medication. Being able to record these variations in an objective way can help doctors adapt treatment and follow disease changes more closely. A [...] Read more.
People with Parkinson’s disease often show changes in their movement abilities during the day, especially around the time they take medication. Being able to record these variations in an objective way can help doctors adapt treatment and follow disease changes more closely. A methodology for quantitative motor assessment is proposed in this work. It employs data from a custom SmartGlove equipped with inertial sensors. A multi-method feature selection scheme is developed, integrating statistical significance, model-based importance, and variance contribution. The most significant features were retained, and higher-level artificial features were generated using Grammatical Evolution (GE). The framework combines multi-criteria feature selection with evolutionary feature construction, providing a compact and interpretable representation of motor behavior. Additionally, the framework highlights nonlinear and composite features as potential digital biomarkers for Parkinson’s monitoring. The method was validated on recordings collected from Parkinson’s patients before and after medication intake. The recordings have been retrieved during four standardized hand motor tasks targeting tremor, bradykinesia, rigidity, and general movement anomalies. The proposed method was compared with five existing machine learning models based on artificial neural networks. GE-based features reduced classification errors to 10–19%, outperforming baseline models. Furthermore, the proposed methodology performs prediction and recall 80–88%. Full article
(This article belongs to the Special Issue Machine Learning-Driven Innovations in Biomedical Signal and Image Processing)
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20 pages, 7466 KB  
Article
Feasibility Study of CLIP-Based Key Slice Selection in CT Images and Performance Enhancement via Lesion- and Organ-Aware Fine-Tuning
by Kohei Yamamoto and Tomohiro Kikuchi
Bioengineering 2025, 12(10), 1093; https://doi.org/10.3390/bioengineering12101093 - 10 Oct 2025
Viewed by 1056
Abstract
Large-scale medical visual question answering (MedVQA) datasets are critical for training and deploying vision–language models (VLMs) in radiology. Ideally, such datasets should be automatically constructed from routine radiology reports and their corresponding images. However, no existing method directly links free-text findings to the [...] Read more.
Large-scale medical visual question answering (MedVQA) datasets are critical for training and deploying vision–language models (VLMs) in radiology. Ideally, such datasets should be automatically constructed from routine radiology reports and their corresponding images. However, no existing method directly links free-text findings to the most relevant 2D slices in volumetric computed tomography (CT) scans. To address this gap, a contrastive language–image pre-training (CLIP)-based key slice selection framework is proposed, which matches each sentence to its most informative CT slice via text–image similarity. This experiment demonstrates that models pre-trained in the medical domain already achieve competitive slice retrieval accuracy and that fine-tuning them on a small dual-supervised dataset that imparts both lesion- and organ-level awareness yields further gains. In particular, the best-performing model (fine-tuned BiomedCLIP) achieved a Top-1 accuracy of 51.7% for lesion-aware slice retrieval, representing a 20-point improvement over baseline CLIP, and was accepted by radiologists in 56.3% of cases. By automating the report-to-slice alignment, the proposed method facilitates scalable, clinically realistic construction of MedVQA resources. Full article
(This article belongs to the Special Issue Machine Learning-Driven Innovations in Biomedical Signal and Image Processing)
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Review

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10 pages, 1210 KB  
Review
Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges
by Sara Salehi, Yashbir Singh, Kelly K. Horst, Quincy A. Hathaway and Bradley J. Erickson
Bioengineering 2025, 12(12), 1303; https://doi.org/10.3390/bioengineering12121303 - 26 Nov 2025
Viewed by 1505
Abstract
The field of radiology is experiencing rapid adoption of large language models (LLMs), yet their tendency to generate hallucinations (plausible but incorrect information) remains a significant barrier to trust. This comprehensive review evaluates emerging agentic artificial intelligence (AI) approaches, including multi-agent role-based systems, [...] Read more.
The field of radiology is experiencing rapid adoption of large language models (LLMs), yet their tendency to generate hallucinations (plausible but incorrect information) remains a significant barrier to trust. This comprehensive review evaluates emerging agentic artificial intelligence (AI) approaches, including multi-agent role-based systems, retrieval-augmented generation (RAG), and uncertainty quantification, to assess their potential for reducing hallucinations in radiology workflows. Evidence from 2024 to 2025 demonstrates that agentic AI can improve diagnostic accuracy and reduce error rates, though these methods remain computationally demanding and lack comprehensive clinical validation. Multi-agent frameworks enable cross-validation through role-based specialization and systematic workflow orchestration, while RAG strategies enhance accuracy by grounding responses in verified medical literature. Within multi-agent systems, uncertainty quantification enables agents to communicate confidence levels to one another, allowing them to appropriately weigh each other’s contributions during collaborative analysis. While multi-agent frameworks and RAG strategies show significant promise, practical deployment will require careful integration with human oversight, robust evaluation metrics tailored to medical imaging tasks, and regulatory adaptation to ensure safe clinical use in diverse patient populations and imaging modalities. Full article
(This article belongs to the Special Issue Machine Learning-Driven Innovations in Biomedical Signal and Image Processing)
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Other

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23 pages, 3725 KB  
Systematic Review
The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis
by David Luengo Gómez, Marta García Cerezo, David López Cornejo, Ángela Salmerón Ruiz, Encarnación González-Flores, Consolación Melguizo Alonso, Antonio Jesús Láinez Ramos-Bossini, José Prados and Francisco Gabriel Ortega Sánchez
Bioengineering 2025, 12(7), 786; https://doi.org/10.3390/bioengineering12070786 - 21 Jul 2025
Cited by 1 | Viewed by 1650
Abstract
Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis [...] Read more.
Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. Results: Sixteen studies (n = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (p = 0.025) indicated potential publication bias. Conclusions: Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice. Full article
(This article belongs to the Special Issue Machine Learning-Driven Innovations in Biomedical Signal and Image Processing)
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