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Sensors
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Transforming Healthcare with Smart Sensing and Machine Learning
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Transforming Healthcare with Smart Sensing and Machine Learning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2376

Special Issue Editor

Dr. Mehdi Boukallel

E-Mail Website
Guest Editor
Sensory and Ambient Interfaces Laboratory, Ambient Intelligence and Interactive Systems Department, CEA LIST, 91191 Palaiseau, France
Interests: intelligent sensors; medical computing; medical disorders; microsensors; patient monitoring; patient rehabilitation; patient treatment; force sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of smart sensing technologies and machine learning (ML) is transforming healthcare delivery and patient management across a broad range of clinical contexts. This Special Issue, “Transforming Healthcare with Smart Sensing and Machine Learning”, welcomes original research and comprehensive reviews on the design, development, and application of intelligent sensor systems integrated with ML to support personalised medicine, continuous monitoring, early diagnostics, and clinical decision-making.

Key areas of interest include wearable and implantable sensors, biosignal acquisition and processing, multimodal sensor fusion, AI-assisted diagnostics, remote monitoring platforms, and smart rehabilitation technologies. Contributions addressing challenges such as data interpretability, privacy, system integration, and deployment in real-world healthcare settings are especially welcome.

This Special Issue aligns with the scope of Sensors as it highlights advanced sensor technologies, signal processing techniques, and their applications in biomedical and health-related domains. We encourage submissions from interdisciplinary teams that bridge engineering, data science, medicine, and human-centred design to drive innovation and improve health outcomes.

Dr. Mehdi Boukallel
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 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • smart sensing technologies
  • multimodal sensor fusion
  • machine learning in healthcare
  • digital health transformation
  • personalized medicine

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Published Papers (2 papers)

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Research

12 pages, 1956 KB  
Article
Experimental Development of XR Enteral Feeding Function for an Endotracheal Suctioning Training Environment Simulator
by Noriyo Colley, Shunsuke Komizunai, Atsuko Sato, Takanori Ishikawa, Mayumi Kouchiyama, Kazue Fujimoto, Toshiko Nasu, Ryosuke Nishima, Aiko Shiota, Eri Murata, Yumi Matsuda and Shinji Ninomiya
Sensors 2026, 26(5), 1499; https://doi.org/10.3390/s26051499 - 27 Feb 2026
Viewed by 428
Abstract
Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding [...] Read more.
Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding simulator (ESTE-TF) that integrates sensor-derived performance metrics and two biological-reaction presentation modalities (projection mapping and tablet display). Methods: In Experiment 1, nursing students completed pre- and post-experience questionnaires assessing perceived learning across seven domains, alongside sensor-based measurements of feeding-start timing, dosing-rate characteristics, and total procedure time. Experiment 2 employed a tablet-based version with four learning items assessed for students and post-experience evaluations collected from registered nurses. Participants also compared the two XR presentation methods. Results: Students demonstrated perceived learning gains of small-to-large magnitude across both experiments (Experiment 1: d = 0.455–0.974; Experiment 2: d = 0.014–0.886), with wide 95% confidence intervals reflecting the exploratory nature of this pilot work. Sensor-derived data showed greater dosing-rate variability and longer procedure times among students than nurses. Participants reported that projection mapping offered a more embodied experience, whereas tablet displays provided clearer visibility. Conclusions: These findings indicate the feasibility and preliminary educational potential of integrating sensing technologies with XR-based biological-reaction presentation for gastrostomy-feeding training. Given the small samples and non-validated measures, results should be interpreted as exploratory. Future research will refine sensor accuracy, establish standardized performance metrics, and evaluate learning outcomes using validated instruments and controlled study designs. Full article
(This article belongs to the Special Issue Transforming Healthcare with Smart Sensing and Machine Learning)
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22 pages, 9457 KB  
Article
Enhancing Document Classification Through Multimodal Image-Text Classification: Insights from Fine-Tuned CLIP and Multimodal Deep Fusion
by Hosam Aljuhani, Mohamed Yehia Dahab and Yousef Alsenani
Sensors 2025, 25(24), 7596; https://doi.org/10.3390/s25247596 - 15 Dec 2025
Cited by 2 | Viewed by 1495
Abstract
Foundation models excel on general benchmarks but often underperform in clinical settings due to domain shift between internet-scale pretraining data and medical data. Multimodal deep learning, which jointly leverages medical images and clinical text, is promising for diagnosis, yet it remains unclear whether [...] Read more.
Foundation models excel on general benchmarks but often underperform in clinical settings due to domain shift between internet-scale pretraining data and medical data. Multimodal deep learning, which jointly leverages medical images and clinical text, is promising for diagnosis, yet it remains unclear whether domain adaptation is better achieved by fine-tuning large vision–language models or by training lighter, task-specific architectures. We address this question by introducing PairDx, a balanced dataset of 22,665 image–caption pairs spanning six medical document classes, curated to reduce class imbalance and support fair, reproducible comparisons. Using PairDx, we develop and evaluate two approaches: (i) PairDxCLIP, a fine-tuned CLIP (ViT-B/32), and (ii) PairDxFusion, a custom hybrid model that combines ResNet-18 visual features and GloVe text embeddings with attention-based fusion. Both adapted models substantially outperform a zero-shot CLIP baseline (61.18% accuracy) and a specialized model, BiomedCLIP, which serves as an additional baseline and achieves 66.3% accuracy. Our fine-tuned CLIP (PairDxCLIP) attains 93% accuracy and our custom fusion model (PairDxFusion) reaches 94% accuracy on a held-out test set. Notably, PairDxFusion achieves this high accuracy with 17 min, 55 s of training time, nearly four times faster than PairDxCLIP (65 min, 52 s), highlighting a practical efficiency–performance trade-off for clinical deployment. The testing time also outperforms the specialized model—BiomedCLIP (0.387 s/image). Our results demonstrate that carefully constructed domain-specific datasets and lightweight multimodal fusion can close the domain gap while reducing computational cost in healthcare decision support. Full article
(This article belongs to the Special Issue Transforming Healthcare with Smart Sensing and Machine Learning)
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