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Biosensors
Special Issues
Medical Imaging and Biosensing
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Medical Imaging and Biosensing

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 7011

Special Issue Editors

Dr. Rihui Li

E-Mail Website
Guest Editor
1. Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China
2. Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA 94305, USA
Interests: multimodal brain imaging; computational neuropsychiatry; neuromodulation
Dr. Dalin Yang

E-Mail Website
Guest Editor
Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
Interests: autism spectrum disorders; medical imaging; fMRI; radiology; deep learning; machine learning; cognitive neuroscience; Python (programming Language); MATLAB; congenital heart disease; computer science; data analysis; neuroscience; medical research; neuroimaging
Special Issues, Collections and Topics in MDPI journals
Dr. Hendrik Santosa

E-Mail Website
Guest Editor
Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: functional near-infrared spectroscopy (fNIRS) data processing; statistical analysis for fNIRS signal; multi-modal neuroimaging; brain-computer interface (BCI) applications
Special Issues, Collections and Topics in MDPI journals
Dr. Yingchun Zhang

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA
Interests: neuroimaging; neural engineering; multimodal brain imaging; neural rehabilitation; computational modeling

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for paper submission for the upcoming Special Issue on Medical Imaging and Biosensing, which is a research area aiming to design and develop new biomedical imaging and biosensing methods, algorithms, devices, biosensors, screening methods, and treatment approaches for clinical diseases.

Advanced medical imaging and biosensing techniques have emerged as effective alternatives to conventional detection and diagnostic methods in healthcare applications. Numerous investigations have demonstrated their potential to provide accurate diagnoses or detection for a wide range of diseases, including brain injuries and disorders, cardiovascular diseases, bacterial/viral infections, inflammatory diseases, and others. There is mounting evidence that certain biomarkers identified using medical imaging techniques can aid in the timely detection, prognosis, and evaluation of various diseases. Similarly, highly sensitive sensing technologies that utilize biomarkers sourced from cerebrospinal fluid, blood, saliva, urine, or organs and tissues can effectively identify changes in medically significant outcomes. By combining medical imaging with biosensing methods, it may be possible to more accurately detect and diagnose diseases, monitor treatment progress in real time, and predict disease outcomes.

The Special Issue will cover a wide range of topics related to the state of the art of medical imaging and biosensing techniques, focusing on the development of new imaging processing methods (e.g., AI-based), portable sensors, fusion of multimodal information, and personalized solutions for precision medicine. These topics include but are not limited to:

  • Artificial intelligence and machine learning approaches for precision medicine;
  • Imaging techniques for disease detection and diagnosis;
  • Image-guided interventions for disease treatment;
  • Imaging-derived biomarkers related to disease progression, response to therapy, and patient outcomes;
  • Point-of-care testing;
  • Wearable biosensors.

Dr. Rihui Li
Dr. Dalin Yang
Dr. Hendrik Santosa
Dr. Yingchun Zhang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Biosensors 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

  • medical imaging
  • medical diagnosis
  • biosensing
  • precision medicine
  • biomarkers
  • medical instruments

Published Papers (3 papers)

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Research

14 pages, 3639 KiB  
Article
Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer
by José Alfonso Cruz-Ramos, Mijaíl Irak Trapero-Corona, Ingrid Aurora Valencia-Hernández, Luz Amparo Gómez-Vargas, María Teresa Toranzo-Delgado, Karla Raquel Cano-Magaña, Emmanuel De la Mora-Jiménez and Gabriela del Carmen López-Armas
Biosensors 2024, 14(2), 94; https://doi.org/10.3390/bios14020094 - 10 Feb 2024
Viewed by 1341
Abstract
Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique [...] Read more.
Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (−PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% −PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman’s r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated. Full article
(This article belongs to the Special Issue Medical Imaging and Biosensing)
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15 pages, 3100 KiB  
Article
Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology
by Minyoung Baik, Sanghoon Shin, Samir Kumar, Dongmin Seo, Inha Lee, Hyun Sik Jun, Ka-Won Kang, Byung Soo Kim, Myung-Hyun Nam and Sungkyu Seo
Biosensors 2023, 13(12), 993; https://doi.org/10.3390/bios13120993 - 21 Nov 2023
Viewed by 1513
Abstract
Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification [...] Read more.
Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland–Altman analysis confirmed the model’s reliability, with a mean bias of −2.29 and 95% limits of agreement between 18.49 and −23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise. Full article
(This article belongs to the Special Issue Medical Imaging and Biosensing)
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13 pages, 2551 KiB  
Article
Noninvasive Estimation of Tumor Interstitial Fluid Pressure from Subharmonic Scattering of Ultrasound Contrast Microbubbles
by Yun Wang, Huimin Lu, Laixin Huang, Deyu Li, Weibao Qiu, Lingling Li, Gang Xu, Min Su, Jianhua Zhou and Fei Li
Biosensors 2023, 13(5), 528; https://doi.org/10.3390/bios13050528 - 08 May 2023
Cited by 1 | Viewed by 1806
Abstract
The noninvasive estimation of interstitial fluid pressure (IFP) using ultrasound contrast agent (UCA) microbubbles as pressure sensors will provide tumor treatments and efficacy assessments with a promising tool. This study aimed to verify the efficacy of the optimal acoustic pressure in vitro in [...] Read more.
The noninvasive estimation of interstitial fluid pressure (IFP) using ultrasound contrast agent (UCA) microbubbles as pressure sensors will provide tumor treatments and efficacy assessments with a promising tool. This study aimed to verify the efficacy of the optimal acoustic pressure in vitro in the prediction of tumor IFPs based on UCA microbubbles’ subharmonic scattering. A customized ultrasound scanner was used to generate subharmonic signals from microbubbles’ nonlinear oscillations, and the optimal acoustic pressure was determined in vitro when the subharmonic amplitude reached the most sensitive to hydrostatic pressure changes. This optimal acoustic pressure was then applied to predict IFPs in tumor-bearing mouse models, which were further compared with the reference IFPs measured using a standard tissue fluid pressure monitor. An inverse linear relationship and good correlation (r = −0.853, p < 0.001) existed between the subharmonic amplitude and tumor IFPs at the optimal acoustic pressure of 555 kPa, and pressure sensitivity was 1.019 dB/mmHg. No statistical differences were found between the pressures measured by the standard device and those estimated via the subharmonic amplitude, as confirmed by cross-validation (mean absolute errors from 2.00 to 3.09 mmHg, p > 0.05). Our findings demonstrated that in vitro optimized acoustic parameters for UCA microbubbles’ subharmonic scattering can be applied for the noninvasive estimation of tumor IFPs. Full article
(This article belongs to the Special Issue Medical Imaging and Biosensing)
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