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A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 14974

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Editors

Dr. Cristina Veres

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Guest Editor

1. Faculty of Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, Nicolae Iorga Street 1, 540088 Târgu Mureș, Romania
2. Interdisciplinary Biomedical Research Center, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, Nicolae Iorga Street 1, 540088 Târgu Mureș, Romania
Interests: management; innovation; healthcare; rehabilitation; quality management
Special Issues, Collections and Topics in MDPI journals

Dr. Adrian Gligor

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Guest Editor

Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Targu Mures, Romania
Interests: AI‑based prediction models; optimization and control methods; data‑driven modeling and simulation; real‑time analytics; numerical simulation and digital‑twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of technology has significantly transformed healthcare, enabling the development of innovative tools to enhance diagnostics, treatments, and patient care. This Special Issue, “Innovative Approaches and Tools for Healthcare and Medical Applications”, aims to showcase cutting-edge advancements and methodologies shaping the future of healthcare.

It spans a broad spectrum of innovations with transformative potential, encompassing areas such as telemedicine and remote monitoring systems, wearable health technologies, robotics in surgery and rehabilitation, nanotechnology applications, smart sensors integrated with the Internet of Things (IoT), bioinformatics and genomics, augmented and virtual reality (AR/VR) for medical training, blockchain solutions for healthcare, cybersecurity in medical systems, 3D printing technologies, biomaterials for advanced therapeutics, and digital therapeutics and mobile health applications.

Each of these domains contributes to a paradigm shift in healthcare by addressing critical challenges such as accessibility, efficiency, and patient-centered care. The aim of this Special Issue is not only to highlight technological advancements but also to explore their practical applications and impacts on clinical practice, ethics, and patient outcomes.

Researchers are invited to contribute original studies, case reports, and reviews that investigate novel systems, technologies, and approaches designed to improve healthcare outcomes.

Dr. Cristina Veres
Dr. Adrian Gligor
Guest Editors

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

healthcare technology
medical applications
artificial intelligence
personalized medicine
telemedicine
diagnostic
imaging technology
sensor technologies
healthcare innovation

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Published Papers (9 papers)

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Research

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15 pages, 2514 KB

Open AccessArticle

Software Tool for Development of Personalized Computational Phantoms of Pregnant Patient in Computational Dosimetry Applications

by Luka Šimić, Dario Faj, Anja Tomić, Ivor Dukić, Hrvoje Brkić, Turk Tajana and Vjekoslav Kopačin

Appl. Sci. 2026, 16(11), 5404; https://doi.org/10.3390/app16115404 - 28 May 2026

Viewed by 355

Abstract

When pregnant patients undergo diagnostic and therapeutic radiological procedures, the unborn child is exposed to an increased risk due to the use of ionizing radiation; therefore, the fetal dose must be estimated and optimized. Tools and methods routinely used for fetal dose estimation lack better personalization of patients. To address this, we developed a software tool for creating phantoms at different pregnancy stages and with varying patient anatomies to further personalize fetal dose estimation using measurements and Monte Carlo simulations. The tool is developed and incorporated into 3DSlicer version 5.6.2 as a plugin. Phantoms are created based on a real patient phantom, Tena, and physiological data for use in radiological protection. Phantoms are developed with only soft, lung, and bone tissue substitutes, represented for the mother and unborn child. This enables the construction of segmented voxel models as well as mesh models (with the ability to export geometries to DICOM format) of the anatomical structures of pregnant women. Additionally, it allows real patient image registration to enable better personalization of the phantom. The tool can help decrease uncertainty in fetal dose estimation, as well as simplify and accelerate the process of fetal dose estimation. It is released publicly to enable further research. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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21 pages, 14302 KB

Open AccessArticle

Audio-Based Device for Automated Surgical Counting, ToolSafe

by Michael R. Gardner, Latifa A. Aladdal, Lama Alshammari, Fatima Aldalgan, Maram A. Alomair, Shahad Alomair and Amani Alrashed

Appl. Sci. 2026, 16(11), 5181; https://doi.org/10.3390/app16115181 - 22 May 2026

Viewed by 528

Abstract

Manual counting of surgical tools, known as surgical counting, is a time-consuming and error-prone task that increases the risk of retained surgical instruments and extends operating room (OR) time. Presently, in hospitals around the world, surgical counting is often performed manually with paper or tablet checklists, often leading to delays, increased infection risk, and financial cost. RFID, barcode-based, and computer vision solutions exist but are expensive and have challenges with sterilization and signal interference. This paper presents ToolSafe, a low-cost, portable system that classifies surgical tools by their acoustic signatures when dropped into a detection box. A pilot dataset of 4004 audio samples from four tool types (n = 996, tissue forceps; n = 1005, iris scissors; n = 1006, scalpel handle; n = 997, testing needle) was collected using ToolSafe. A convolutional neural network (CNN) was evaluated using stratified five-fold cross-validation on the laboratory dataset, with a k-nearest neighbors (KNN) classifier implemented as a control model. In each fold, both models were trained on 80% of the data and tested on the remaining 20%, ensuring that all samples were used for both training and evaluation. The CNN achieved a mean (±standard deviation) classification accuracy of 99.55% (±0.19%) across the validation folds, outperforming the KNN model, which achieved a mean accuracy of 97.28% (±0.50%). The difference was statistically significant according to a paired t-test across folds (p = 0.0003), indicating CNN’s superior performance on the dataset. For a run of 100 additional samples using the Raspberry Pi-based system, spectrogram generation averaged 0.121 s (±0.025 s), CNN inference averaged 0.180 s (±0.033 s), and total end-to-end latency averaged 1.851 s (±0.253 s) per tool. This pilot study proposes a possible technological solution for surgical counting that reduces human error and enhances patient safety. ToolSafe may be subsequently improved by increasing the number of surgical tools used in the training dataset, testing under more robust OR-like environments, and comparing to other classification algorithms. Further refinement and incorporation of ToolSafe in operating room workflows have the potential to reduce patient risks from extended surgical times and retained surgical instruments. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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13 pages, 2655 KB

Open AccessArticle

Eddy Current Distribution in Magnetotherapy of Bones: A Qualitative and Quantitative Study

by Przemyslaw Syrek, Mikolaj Skowron and Piotr Kapustka

Appl. Sci. 2025, 15(18), 9892; https://doi.org/10.3390/app15189892 - 9 Sep 2025

Cited by 1 | Viewed by 1440

Abstract

Unlike pharmacological and surgical methods, electrical and especially magnetic stimulation are of interest due to their noninvasiveness and high tolerability by patients. Ensuring the repeatability of procedures and achieving the highest possible homogeneity of the eddy current distribution appears to be crucial, particularly in the context of potential clinical trials. This highlights the qualitative aspect of the therapy. Equally important, however, are the outcomes in terms of eddy current generation and their presentation in a psychological context, particularly in relation to patient communication. Many patients undergoing treatment express a desire to understand how the applicator works, what the procedure does to their body, and what sensations or effects are occurring in their limbs during therapy. On the other hand, quantitative analysis enables the rescaling of the magnetic field induced within the applicator, which in turn allows for determining the appropriate level of induced currents in the limb of a specific patient. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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Review

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22 pages, 2229 KB

Open AccessReview

Towards Objective Emotional Monitoring in Children with Cerebral Palsy: A Review of rPPG and Multimodal Approaches

by Martha Xóchitl Nava-Bautista, Víctor H. Castillo-Topete, Alberto J. Molina-Cantero and Isabel M. Gómez-González

Appl. Sci. 2026, 16(11), 5502; https://doi.org/10.3390/app16115502 - 1 Jun 2026

Viewed by 413

Abstract

Non-contact physiological monitoring based on remote PPG (rPPG) offers a viable alternative for the care of pediatric populations, particularly for children with cerebral palsy (CP) who present unique communication and mobility challenges. This paper presents a review of the literature on the use of rPPG for the estimation of vital signs and its application in emotional monitoring. Following the PRISMA 2020 guidelines as a methodological framework for searching and filtering, an exhaustive search was conducted in the IEEE Xplore and Scopus databases covering the period from 2017 to 2024. A total of 35 studies were selected for analysis. The review examines the evolution of rPPG algorithms—from classical mathematical approaches to recent deep-learning-based architectures—identifying critical technical challenges such as motion artifacts caused by spasticity and variations in lighting conditions. The results reveal that while rPPG has reached technical maturity for monitoring core physiological parameters such as heart rate, its application to robust emotion detection in children with CP remains limited. The main limitation identified across the surveyed literature is the critical scarcity of public or clinical datasets featuring pediatric CP cohorts. Finally, the potential of multimodal integration—combining rPPG with eye-tracking and wearable sensors—is discussed as a promising pathway toward objective emotional monitoring. Such an approach could enhance communication, support rehabilitation processes, and ultimately improve the quality of life of children with cerebral palsy and their caregivers. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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17 pages, 307 KB

Open AccessReview

Performance Comparison of Smartphone-Based Portable Slit Lamp Microscopes: A Narrative Review of Medical Devices Applicable to Telemedicine in Ophthalmology

by Eisuke Shimizu, Ryota Yokoiwa and Shintaro Nakayama

Appl. Sci. 2026, 16(9), 4448; https://doi.org/10.3390/app16094448 - 1 May 2026

Cited by 1 | Viewed by 799

Abstract

Smartphone-based portable slit lamp microscopes are increasingly used as low-cost tools for anterior segment imaging in teleophthalmology, yet the literature combines heterogeneous study designs, comparator standards, and deployment contexts. Because the evidence base spans engineering reports, basic science, clinical validation studies, implementation research, and case-based telemedicine, we structured a narrative review rather than a pooled meta-analysis. We searched PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, Cochrane Library, ScienceDirect, and DOAJ for literature available on or before 28 February 2026, supplemented by manual reference list screening and targeted retrieval of relevant technical standards. Peer-reviewed English original studies formed the core evidence base; contextual non-English and gray literature sources were retained only when explicitly labeled as non-core. To improve interpretability, the results were grouped by synthesis domain, clinical task, comparator standard, telemedicine scenario, and artificial intelligence (AI) dataset/validation characteristics. The highest-confidence evidence concerned nuclear cataract grading, tear film breakup time and corneal staining assessment, anterior chamber depth screening, tear meniscus height measurement, allergic conjunctival grading, and selected corneal disorders. Agreement with conventional slit lamp examination or anterior segment optical coherence tomography was generally moderate to high within task-specific comparisons, and telemedicine deployment was feasible for screening, follow-up, remote consultation, emergency triage, house visits, and outreach. However, illumination reporting remains inconsistent, explicit ISO-aligned dosimetry is sparse, and most AI studies remain retrospective, single-center, and device family-specific. Current evidence, therefore, supports smartphone-based portable slit lamp microscopes primarily as adjunctive teleophthalmology tools rather than replacements for comprehensive in-clinic microscopy. The synthesis clarifies where conclusions are supported by comparative validation data, where they remain exploratory, and which methodological gaps should be prioritized in future multicenter studies. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

14 pages, 636 KB

Open AccessReview

Artificial Intelligence in Prostate MRI: Redefining the Patient Journey from Imaging to Precision Care

by Giuseppe Pellegrino, Francesca Arnone, Maria Francesca Girlando, Donatello Berloco, Chiara Perazzo, Sonia Triggiani and Gianpaolo Carrafiello

Appl. Sci. 2026, 16(2), 893; https://doi.org/10.3390/app16020893 - 15 Jan 2026

Cited by 2 | Viewed by 1372

Abstract

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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21 pages, 937 KB

Open AccessReview

Transcranial Brain Stimulation: Technical, Computational, and Clinical Aspects in Contemporary Research

by Przemyslaw Syrek and Mikolaj Skowron

Appl. Sci. 2026, 16(1), 107; https://doi.org/10.3390/app16010107 - 22 Dec 2025

Cited by 2 | Viewed by 2087

Abstract

This article provides a narrative review of the technical, computational and clinical aspects of transcranial brain stimulation (TBS), with an emphasis on transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). The review addresses three central questions: which physical, engineering, and biological principles determine the generation, propagation, and focality of electromagnetic fields in the human head. The second question asks how modeling approaches, stimulation parameters, and hardware design influence the accuracy, safety, and individual variability of brain stimulation. And, finally, how these technical factors translate into current clinical applications, therapeutic efficacy, and practical limitations. The key take-home messages are as follows: for engineers, realistic anatomical head models, precise coil/electrode placement, and reliable numerical solvers remain essential for predicting field distribution and optimizing stimulation protocols; for clinicians, stimulation outcomes are strongly dependent on anatomy-specific field patterns, safety constraints, and device-related parameters that require careful adjustment; and for both groups, despite significant technological progress, effective and reproducible stimulation still demands systematic protocol refinement and individualized planning. Overall, this review integrates contemporary technical knowledge with clinical perspectives to support evidence-based use and future development of TMS and tDCS. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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22 pages, 5034 KB

Open AccessReview

Lean Management Framework in Healthcare: Insights and Achievements on Hazardous Medical Waste

by Adela Dana Ciobanu, Alexandru Ozunu, Maria Tănase, Adrian Gligor and Cristina Veres

Appl. Sci. 2025, 15(12), 6686; https://doi.org/10.3390/app15126686 - 13 Jun 2025

Cited by 5 | Viewed by 3603

Abstract

Hazardous medical waste (HMW) presents significant environmental and public health challenges, particularly in the context of rising healthcare demands and the global push for sustainable resource management. This study investigates the evolution of HMW management through a bibliometric and thematic analysis of 1703 articles published between 2020 and 2025, retrieved from the Web of Science database. Using VOSviewer, co-occurrence mapping and term clustering reveal six major conceptual domains, including thermal treatment technologies, operational optimization, environmental indicators, and behavioral dimensions. This study adds value by applying a dual bibliometric–thematic lens to provide new insights into the operational, technological, and sustainability dimensions of HMW. The analysis identifies a gradual shift from traditional disposal methods to circular models focused on resource valorization through pyrolysis, gasification, and sterilization. Lean management principles—such as process efficiency, waste minimization, and the promotion of recovery and reuse—emerge as complementary to circular economy goals. Additional visualizations outline international collaboration trends, highlighting established research hubs and emerging contributors. The findings emphasize the role of data-driven decision tools, sustainability assessment methods, and cross-sectoral integration in enhancing medical waste systems. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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14 pages, 1629 KB

Open AccessReview

Focused Ultrasounds in the Rehabilitation Setting: A Narrative Review

by Carmelo Pirri, Nicola Manocchio, Daniele Polisano, Andrea Sorbino and Calogero Foti

Appl. Sci. 2025, 15(9), 4743; https://doi.org/10.3390/app15094743 - 24 Apr 2025

Cited by 2 | Viewed by 3141

Abstract

Focused ultrasound (FUS) is an emerging noninvasive technology with significant therapeutic potential across various clinical domains. FUS enables precise targeting of tissues using mechanisms like thermoablation, mechanical disruption, and neuromodulation, minimizing damage to surrounding areas. In movement disorders such as essential tremor and Parkinson’s disease, MR-guided FUS thalamotomy has demonstrated substantial tremor reduction and improved quality of life. Psychiatric applications include anterior capsulotomy for treatment-resistant obsessive-compulsive disorder and major depressive disorder, with promising symptom relief and minimal cognitive side effects. FUS also facilitates blood-brain barrier opening for drug delivery in neurological conditions like Alzheimer’s disease. Musculoskeletal applications highlight its efficacy in managing chronic pain from knee osteoarthritis and lumbar facet joint syndrome through precise thermal ablation. Additionally, FUS has shown potential in neuropathic pain management and peripheral nerve stimulation, offering innovative approaches for amputees and cancer survivors. Cognitive and neuromodulatory research underscores its ability to enhance motor function and interhemispheric cortical balance, benefiting stroke and traumatic brain injury rehabilitation. Despite these conditions frequently leading to various kinds of disabilities, no direct exploration of the possible FUS application in rehabilitation is yet available in the literature. All this considered, this review aims to discuss how FUS could be applied in rehabilitation, exploring the current status of knowledge and highlighting future directions. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

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