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

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 9172

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Dr. Shyqyri Haxha

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Department of Electronic Engineering Egham, School of Engineering, Physical and Mathematical Sciences, Royal Holloway University of London, Egham TW20 0EX, UK
Interests: optical image sensors; photonic sensors; optoelectronic biosensors; photonic biosensors; biophotonic sensors; optical sensors; photonic crystal based sensors; metamaterial sensors; nanosensors; wearable medical sensor devices; environmental optical sensors; fiber optic sensors
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Special Issue Information

Dear Colleagues,

The Optical Imaging and Biophotonic Sensors (OIBS) research field is experiencing significant development, primarily in bioimaging, optical spectroscopy imaging, biosensors, nanosensors, integrated photonics and optical lab-on-a-chip sensing systems, relying on the state-of-the-art optical and photonic technology, including instrumentation and measurement biophotonics methods and devices as research tools to understand the cellular origin of diseases. These advanced imaging and photonic-based sensor systems offer major multi-functionalities that deliver greatly increased penetration, resolution, simultaneous sensitivity and selectivity and depth of focus operating in remote environments.

The aim of this Special Issue is to explore the advanced progress in research findings and photonics-based engineering technologies related to OIBS for far-reaching applications in imaging, biosensing, environmental, pharmaceutical, medical (using optical imaging spectroscopy), chemical and nano-optic sensors consisting of biologically or biophysically-derived sensing elements.

Contributions may also include different aspects in terms of sensor design, manufacturing, testing and validation. In other words, the aim of this Special Issue is to provide a clear picture of our recent understanding of optics/photonics involved in sensor design and development.

Review articles and regular original research article related to the above sensors contributions are welcome.

You may choose our Joint Special Issue in Biosensors.

Dr. Shyqyri Haxha
Guest Editor

Manuscript Submission Information

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Keywords

Optical imaging sensors
Raman spectroscopy imaging
Brillouin microscopy imaging
Photonic sensors
Biosensors
Fiber optic sensors
Nanosensors
Biophotonics
Wearable medical nanosensors
Optoelectronic sensors.

Published Papers (5 papers)

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Research

16 pages, 4272 KiB

Open AccessArticle

Liquid Mixing on Falling Films: Marker-Free, Molecule-Sensitive 3D Mapping Using Raman Imaging

by Marcel Nachtmann, Daniel Feger, Felix Wühler, Matthias Rädle and Stephan Scholl

Sensors 2023, 23(13), 5846; https://doi.org/10.3390/s23135846 - 23 Jun 2023

Viewed by 712

Abstract

Following up on a proof of concept, this publication presents a new method for mixing mapping on falling liquid films. On falling liquid films, different surfaces, plain or structured, are common. Regarding mixing of different components, the surface has a significant effect on its capabilities and performance. The presented approach combines marker-free and molecule-sensitive measurements with cross-section mapping to emphasize the mixing capabilities of different surfaces. As an example of the mixing capabilities on falling films, the mixing of sodium sulfate with tap water is presented, followed by a comparison between a plain surface and a pillow plate. The method relies upon point-by-point Raman imaging with a custom-built high-working-distance, low-depth-of-focus probe. To compensate for the long-time measurements, the continuous plant is in its steady state, which means the local mixing state is constant, and the differences are based on the liquids’ position on the falling film, not on time. Starting with two separate streams, the mixing progresses by falling down the surface. In conclusion, Raman imaging is capable of monitoring mixing without any film disturbance and provides detailed information on liquid flow in falling films. Full article

(This article belongs to the Special Issue Optical Imaging and Biophotonic Sensors (OIBS))

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13 pages, 2971 KiB

Open AccessArticle

Machine Learning Assisted Handheld Confocal Raman Micro-Spectroscopy for Identification of Clinically Relevant Atopic Eczema Biomarkers

by Kapil Dev, Chris Jun Hui Ho, Renzhe Bi, Yik Weng Yew, Dinish U. S, Amalina Binte Ebrahim Attia, Mohesh Moothanchery, Steven Thng Tien Guan and Malini Olivo

Sensors 2022, 22(13), 4674; https://doi.org/10.3390/s22134674 - 21 Jun 2022

Cited by 5 | Viewed by 1941

Abstract

Atopic dermatitis (AD) is a common chronic inflammatory skin dermatosis condition due to skin barrier dysfunction that causes itchy, red, swollen, and cracked skin. Currently, AD severity clinical scores are subjected to intra- and inter-observer differences. There is a need for an objective scoring method that is sensitive to skin barrier differences. The aim of this study was to evaluate the relevant skin chemical biomarkers in AD patients. We used confocal Raman micro-spectroscopy and advanced machine learning methods as means to classify eczema patients and healthy controls with sufficient sensitivity and specificity. Raman spectra at different skin depths were acquired from subjects’ lower volar forearm location using an in-house developed handheld confocal Raman micro-spectroscopy system. The Raman spectra corresponding to the skin surface from all the subjects were further analyzed through partial least squares discriminant analysis, a binary classification model allowing the classification between eczema and healthy subjects with a sensitivity and specificity of 0.94 and 0.85, respectively, using stratified K-fold (K = 10) cross-validation. The variable importance in the projection score from the partial least squares discriminant analysis classification model further elucidated the role of important stratum corneum proteins and lipids in distinguishing two subject groups. Full article

(This article belongs to the Special Issue Optical Imaging and Biophotonic Sensors (OIBS))

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12 pages, 1682 KiB

Open AccessArticle

Novel Wireless Bioimpedance Device for Segmental Lymphedema Analysis Post Dual-Site Free Vascularized Lymph Node Transfer: A Prospective Cohort Study

by Chang-Cheng Chang, Wei-Ling Jan, Cheng-Huei Juan, Nai-Hsin Meng, Bor-Shyh Lin and Hung-Chi Chen

Sensors 2021, 21(24), 8187; https://doi.org/10.3390/s21248187 - 8 Dec 2021

Cited by 2 | Viewed by 2345

Abstract

An innovative wireless device for bioimpedance analysis was developed for post-dual-site free vascularized lymph node transfer (VLNT) evaluation. Seven patients received dual-site free VLNT for unilateral upper or lower limb lymphedema. A total of 10 healthy college students were enrolled in the healthy control group. The device was applied to the affected and unaffected limbs to assess segmental alterations in bioimpedance. The affected proximal limb showed a significant increase in bioimpedance at postoperative sixth month (3.3 [2.8, 3.6], p = 0.001) with 10 kHz currents for better penetration, although the difference was not significant (3.3 [3.3, 3.8]) at 1 kHz. The bioimpedance of the affected distal limb significantly increased after dual-site free VLNT surgery, whether passing with the 1 kHz (1.6 [0.7, 3.4], p = 0.030, postoperative first month; 2.8 [1.0, 4.2], p = 0.027, postoperative third month; and 1.3 [1.3, 3.4], p = 0.009, postoperative sixth month) or 10 kHz current ((1.4 [0.5, 2.7], p = 0.049, postoperative first month; 3.2 [0.9, 6.3], p = 0.003, postoperative third month; and 3.6 [2.5, 4.1], p < 0.001, postoperative sixth month). Bioimpedance alterations on the affected distal limb were significantly correlated with follow-up time (rho = 0.456, p = 0.029 detected at 10 kHz). This bioimpedance wireless device could quantitatively monitor the interstitial fluid alterations, which is suitable for postoperative real-time surveillance. Full article

(This article belongs to the Special Issue Optical Imaging and Biophotonic Sensors (OIBS))

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15 pages, 3304 KiB

Open AccessArticle

Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws

by Katsuhiro Mikami, Mitsutaka Nemoto, Takeo Nagura, Masaya Nakamura, Morio Matsumoto and Daisuke Nakashima

Sensors 2021, 21(22), 7553; https://doi.org/10.3390/s21227553 - 13 Nov 2021

Cited by 4 | Viewed by 1899

Abstract

Evaluation of the initial stability of implants is essential to reduce the number of implant failures of pedicle screws after orthopedic surgeries. Laser resonance frequency analysis (L-RFA) has been recently proposed as a viable diagnostic scheme in this regard. In a previous study, L-RFA was used to demonstrate the diagnosis of implant stability of monoaxial screws with a fixed head. However, polyaxial screws with movable heads are also frequently used in practice. In this paper, we clarify the characteristics of the laser-induced vibrational spectra of polyaxial screws which are required for making L-RFA diagnoses of implant stability. In addition, a novel analysis scheme of a vibrational spectrum using L-RFA based on machine learning is demonstrated and proposed. The proposed machine learning-based diagnosis method demonstrates a highly accurate prediction of implant stability (peak torque) for polyaxial pedicle screws. This achievement will contribute an important analytical method for implant stability diagnosis using L-RFA for implants with moving parts and shapes used in various clinical situations. Full article

(This article belongs to the Special Issue Optical Imaging and Biophotonic Sensors (OIBS))

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15 pages, 2800 KiB

Open AccessArticle

Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks

by Nathan J. Knighton, Brian K. Cottle, Bailey E. B. Kelson, Robert W. Hitchcock and Frank B. Sachse

Sensors 2021, 21(18), 6033; https://doi.org/10.3390/s21186033 - 9 Sep 2021

Cited by 1 | Viewed by 1570

Abstract

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall. The aortic sections represented fibrotic tissue. Depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500–1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the CNNs for the classification of tissue constructs from single spectra and combined spectra. Combined spectra, including the spectra from fibers distal from the illumination fiber, typically yielded the highest accuracy. The maximal classification accuracy of the depth detection, volume fraction, and permutated arrangements was (mean ± standard deviation (stddev)) 88.97 ± 2.49%, 76.33 ± 1.51%, and 84.25 ± 1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. The potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis, as well as the assessment of ablation lesions. Full article

(This article belongs to the Special Issue Optical Imaging and Biophotonic Sensors (OIBS))

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