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Sensor-Enhanced Medical Imaging: Machine Learning and Computer Vision for Multimodal Analysis

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1446

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Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania
Interests: computer vision image analysis and processing; application of artificial intelligence technologies in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of sensor technologies has revolutionized medical imaging, enabling novel acquisition techniques and instrumentation for enhanced diagnostic precision. This Special Issue highlights cutting-edge research on sensor-enhanced medical imaging, focusing on innovations that integrate novel sensing principles, instrumentation, or acquisition techniques to advance diagnostic and analytical capabilities. Specifically, we invite original research papers that explore novel sensor-based image acquisition techniques, advanced instrumentation for medical imaging, and their integration with computational methods. Within this context, submissions may address advanced image processing algorithms for noisy or incomplete sensor data, ML models optimized for real-time analysis of sensor streams, and AI-driven frameworks leveraging sensor-derived features.

Contributions must emphasize sensor-enhanced methodologies to ensure differentiation from the journal’s broader scope. Topics of interest include, but are not limited to, the following:

  • Deep Learning for Medical Image Segmentation from Sensor Data;
  • Machine Learning for Disease Diagnosis and Prognosis using Sensor-Derived Features;
  • Computer-Aided Detection and Monitoring Systems based on Sensor Streams;
  • Image Registration and Fusion of Multi-Modal Sensor Data;
  • Three-Dimensional Medical Image Analysis and Visualization from Volumetric Sensor Data;
  • Generative Models for Medical Image Synthesis and Enhancement from Limited Sensor Data;
  • Quantitative Image Analysis and Biomarker Discovery from Sensor Data;
  • AI-driven Image-Guided Interventions and Robotics using Real-time Sensor Feedback.

Dr. Vidas Raudonis
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-anonymized 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

  • sensor-enhanced medical imaging
  • medical sensors
  • sensor fusion
  • AI-driven diagnostics
  • multimodal image analysis
  • real-time sensor analytics

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Published Papers (1 paper)

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Research

25 pages, 6702 KB  
Article
Soft Optical Sensor for Embryo Quality Evaluation Based on Multi-Focal Image Fusion and RAG-Enhanced Vision Transformers
by Domas Jonaitis, Vidas Raudonis, Egle Drejeriene, Agne Kozlovskaja-Gumbriene and Andres Salumets
Sensors 2026, 26(5), 1441; https://doi.org/10.3390/s26051441 - 25 Feb 2026
Viewed by 643
Abstract
Assessing human embryo quality is a critical step in in vitro fertilization (IVF), yet traditional manual grading remains subjective and physically limited by the shallow depth-of-field in conventional microscopy. This study develops a novel “soft optical sensor” architecture that transforms standard optical microscopy [...] Read more.
Assessing human embryo quality is a critical step in in vitro fertilization (IVF), yet traditional manual grading remains subjective and physically limited by the shallow depth-of-field in conventional microscopy. This study develops a novel “soft optical sensor” architecture that transforms standard optical microscopy into an automated, high-precision instrument for embryo quality assessment. The proposed system integrates two key computational innovations: (1) a multi-focal image fusion module that reconstructs lost morphological details from Z-stack focal planes, effectively creating a 3D-aware representation from 2D inputs; and (2) a retrieval-augmented generation (RAG) framework coupled with a Swin Transformer to provide both high-accuracy classification and explainable clinical rationales. Validated on a large-scale clinical dataset of 102,308 images (prior to augmentation), the system achieves a diagnostic accuracy of 94.11%. This performance surpasses standard single-plane analysis methods by 9.43%, demonstrating the critical importance of fusing multi-focal data. Furthermore, the RAG module successfully grounds model predictions in standard ESHRE consensus guidelines, generating natural language explanations. The results demonstrate that this soft sensor approach significantly reduces inter-observer variability and offers a robust tool for standardized morphological assessment, though prospective validation against live birth outcomes remains essential for clinical adoption. Full article
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