Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment
share announcement

Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 8104

Special Issue Editors

Dr. Cosimo Ieracitano

E-Mail Website
Guest Editor
DICEAM Department, Mediterranea University of Reggio Calabria, Via Graziella Feo di Vito, 89060 Reggio Calabria, Italy
Interests: information theory; machine learning; deep learning; explainable machine learning; biomedical signal processing; brain computer interface; cybersecurity; computer vision; material informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xuejun Zhang

E-Mail Website
Guest Editor
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Interests: computer-aided diagnosis; medical image processing; artificial intelligence

Special Issue Information

Dear Colleagues,

The integration of biomedical imaging techniques and advanced data analytics has revolutionized the field of disease diagnosis and treatment, offering new insights and tools to improve patient outcomes. The timely and accurate diagnosis of diseases plays a crucial role in effective treatment planning and management. Biomedical imaging modalities, such as MRI, CT, PET, ultrasound, and optical imaging, provide valuable visual information about anatomical structures, physiological functions, and pathological changes within the human body. However, the sheer volume and complexity of imaging data present significant challenges in extracting meaningful information and making accurate diagnoses. This Special Issue aims to bring together researchers and practitioners from various disciplines to showcase the latest advancements in biomedical imaging and data analytics for disease diagnosis and treatment. We invite original research articles, reviews, and case studies that highlight innovative approaches, novel techniques, and practical applications in this field.

Topics of interest for this Special Issue include, but are not limited to:

- Development of advanced imaging technologies for disease detection and characterization;

- Image reconstruction, enhancement, and segmentation techniques for accurate diagnosis;

- Integration of multimodal imaging for comprehensive disease assessment;

- Machine learning and deep learning algorithms for image analysis and pattern recognition;

- Quantitative imaging biomarkers for disease prognosis and treatment response assessment;

  • Data-driven approaches for personalized medicine and precision healthcare

Dr. Cosimo Ieracitano
Prof. Dr. Xuejun 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. Bioengineering 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

  • artificial intelligence
  • machine learning
  • deep learning
  • biomedical engineering
  • medical image processing
  • data analytics

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

13 pages, 2027 KiB  
Article
Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method
by Raj Ponnusamy, Ming Zhang, Yue Wang, Xinyue Sun, Mohammad Chowdhury, Jeffrey B. Driban, Timothy McAlindon and Juan Shan
Bioengineering 2024, 11(4), 374; https://doi.org/10.3390/bioengineering11040374 - 12 Apr 2024
Viewed by 514
Abstract
Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the [...] Read more.
Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson’s correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Figure 1

23 pages, 26689 KiB  
Article
Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction
by Ritipong Wongkhuenkaew, Sansanee Auephanwiriyakul, Marasri Chaiworawitkul, Nipon Theera-Umpon and Uklid Yeesarapat
Bioengineering 2024, 11(3), 254; https://doi.org/10.3390/bioengineering11030254 - 05 Mar 2024
Viewed by 1171
Abstract
Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration [...] Read more.
Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm2, respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Graphical abstract

19 pages, 4480 KiB  
Article
Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers
by Michelle W. Tong, Aniket A. Tolpadi, Rupsa Bhattacharjee, Misung Han, Sharmila Majumdar and Valentina Pedoia
Bioengineering 2024, 11(1), 17; https://doi.org/10.3390/bioengineering11010017 - 24 Dec 2023
Viewed by 1147
Abstract
A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral [...] Read more.
A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral and injured ipsilateral knee images from patients with ACL injuries and reconstruction surgeries acquired across three institutions. Network generalizability was evaluated on 343 knees acquired in a clinical setting and 46 knees from simultaneous bilateral acquisition in a research setting. The deep neural network synthesized high-fidelity reconstructions of T1p maps, preserving textures and local T1p elevation patterns in cartilage with a normalized mean square error of 2.4% and Pearson’s correlation coefficient of 0.93. Analysis of reconstructed T1p maps within cartilage compartments revealed minimal bias (−0.10 ms), tight limits of agreement, and quantification error (5.7%) below the threshold for clinically significant change (6.42%) associated with osteoarthritis. In an out-of-distribution external test set, synthetic maps preserved T1p textures, but exhibited increased bias and wider limits of agreement. This study demonstrates the capability of image synthesis to reduce acquisition time, derive meaningful information from existing datasets, and suggest a pathway for standardizing T1p as a quantitative biomarker for osteoarthritis. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Figure 1

11 pages, 4874 KiB  
Article
Revolutionizing Patient Monitoring in Age-Related Macular Degeneration: A Comparative Study on the Necessity and Efficiency of the AMD VIEWER
by Hitoshi Tabuchi, Tomofusa Yamauchi, Toshihiko Nagasawa, Hodaka Deguchi, Mao Tanabe, Hayato Tanaka and Tsutomu Yasukawa
Bioengineering 2023, 10(12), 1426; https://doi.org/10.3390/bioengineering10121426 - 15 Dec 2023
Viewed by 1010
Abstract
(1) Background: Age-related Macular Degeneration (AMD) is a critical condition leading to blindness, necessitating lifelong clinic visits for management, albeit with existing challenges in monitoring its long-term progression. This study introduced and assessed an innovative tool, the AMD long-term Information Viewer (AMD VIEWER), [...] Read more.
(1) Background: Age-related Macular Degeneration (AMD) is a critical condition leading to blindness, necessitating lifelong clinic visits for management, albeit with existing challenges in monitoring its long-term progression. This study introduced and assessed an innovative tool, the AMD long-term Information Viewer (AMD VIEWER), designed to offer a comprehensive display of crucial medical data—including visual acuity, central retinal thickness, macular volume, vitreous injection treatment history, and Optical Coherent Tomography (OCT) images—across an individual eye’s entire treatment course. (2) Methods: By analyzing visit frequencies of patients with a history of invasive AMD treatment, a comparative examination between a Dropout group and an Active group underscored the clinical importance of regular visits, particularly highlighting better treatment outcomes and maintained visual acuity in the Active group. (3) Results: The efficiency of AMD VIEWER was proven by comparing it to manual data input by optometrists, showing significantly faster data display with no errors, unlike the time-consuming and error-prone manual entries. Furthermore, an elicited Net Promoter Score (NPS) of 70 from 10 ophthalmologists strongly endorsed AMD VIEWER’s practical utility. (4) Conclusions: This study underscores the importance of regular clinic visits for AMD patients. It suggests the AMD VIEWER as an effective tool for improving treatment data management and display. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Figure 1

11 pages, 968 KiB  
Article
Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning
by Jae-Hyuk Shim, Woongsang Sunwoo, Byung Yoon Choi, Kwang Gi Kim and Young Jae Kim
Bioengineering 2023, 10(11), 1337; https://doi.org/10.3390/bioengineering10111337 - 20 Nov 2023
Viewed by 1009
Abstract
Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME [...] Read more.
Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME diagnosis, prior work did not often specify if pediatric images were used for training, causing uncertainties about their clinical relevance, especially due to important distinctions between the tympanic membranes of small children and adults. We trained cross-validated ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2 models on 1150 pediatric tympanic membrane images from otoendoscopes to classify OME. When assessed using a separate dataset of 100 pediatric tympanic membrane images, the models achieved mean accuracies of 92.9% (ResNet50), 97.2% (DenseNet201), 96.0% (InceptionV3), and 94.8% (InceptionResNetV2), compared to the seven otolaryngologists that achieved accuracies between 84.0% and 69.0%. The results showed that even the worst-performing model trained on fold 3 of InceptionResNetV2 with an accuracy of 88.0% exceeded the accuracy of the highest-performing otolaryngologist at 84.0%. Our findings suggest that these specifically trained deep learning models can potentially enhance the clinical diagnosis of OME using pediatric otoendoscopic tympanic membrane images. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Figure 1

12 pages, 2823 KiB  
Article
Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network
by Hyeong Il Koh, Sungdae Na and Myoung Nam Kim
Bioengineering 2023, 10(11), 1325; https://doi.org/10.3390/bioengineering10111325 - 16 Nov 2023
Viewed by 873
Abstract
Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, [...] Read more.
Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time–frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time–frequency path attempts to compensate for the spectral details via a spectral extension block. These two paths enhance temporal and spectral features via mask functions modeled as LSTM, respectively, offering a comprehensive approach to speech enhancement. Experimental results show that the proposed dual-path LSTM network consistently outperforms conventional single-domain speech enhancement methods in terms of speech quality and intelligibility. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Figure 1

Review

Jump to: Research

14 pages, 2016 KiB  
Review
Label-Free Optical Technologies for Middle-Ear Diseases
by Zeyi Zhou, Rishikesh Pandey and Tulio A. Valdez
Bioengineering 2024, 11(2), 104; https://doi.org/10.3390/bioengineering11020104 - 23 Jan 2024
Viewed by 862
Abstract
Medical applications of optical technology have increased tremendously in recent decades. Label-free techniques have the unique advantage of investigating biological samples in vivo without introducing exogenous agents. This is especially beneficial for a rapid clinical translation as it reduces the need for toxicity [...] Read more.
Medical applications of optical technology have increased tremendously in recent decades. Label-free techniques have the unique advantage of investigating biological samples in vivo without introducing exogenous agents. This is especially beneficial for a rapid clinical translation as it reduces the need for toxicity studies and regulatory approval for exogenous labels. Emerging applications have utilized label-free optical technology for screening, diagnosis, and surgical guidance. Advancements in detection technology and rapid improvements in artificial intelligence have expedited the clinical implementation of some optical technologies. Among numerous biomedical application areas, middle-ear disease is a unique space where label-free technology has great potential. The middle ear has a unique anatomical location that can be accessed through a dark channel, the external auditory canal; it can be sampled through a tympanic membrane of approximately 100 microns in thickness. The tympanic membrane is the only membrane in the body that is surrounded by air on both sides, under normal conditions. Despite these favorable characteristics, current examination modalities for middle-ear space utilize century-old technology such as white-light otoscopy. This paper reviews existing label-free imaging technologies and their current progress in visualizing middle-ear diseases. We discuss potential opportunities, barriers, and practical considerations when transitioning label-free technology to clinical applications. Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop