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Bioengineering
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Artificial Intelligence in Auto-Diagnosis and Clinical Applications 2nd Edition
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Artificial Intelligence in Auto-Diagnosis and Clinical Applications 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3595

Special Issue Editors

Dr. Teng Grace Zhang

E-Mail Website
Guest Editor
Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, No. 3 Sassoon Road, Hong Kong
Interests: bioengineering; digital orthopedics; clinical medicine; spine deformity; disease progression prediction; artificial intelligence and modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Jason Pui Yin Cheung

E-Mail Website
Guest Editor
Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, No. 3 Sassoon Road, Hong Kong
Interests: computer-aided medicine; computational orthopedics; clinical medicine; pediatric spine deformity; scoliosis genetics; artificial intelligence and modeling; medical imaging and clinical phenotyping
Special Issues, Collections and Topics in MDPI journals
Dr. Tianjiao Zeng

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: computational imaging; inverse imaging problems; deep learning; neuroimaging
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Meng

E-Mail Website
Guest Editor
Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, No. 3 Sassoon Road, Hong Kong
Interests: low-level vision; multi-view vision; medical image analysis; intelligent orthopaedics

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence (AI) techniques have revolutionized many fields, including healthcare, disease diagnosis, staging, treatment, surgical planning, etc., and they have the potential to transform the way we approach medical diagnosis and treatment. This boost in AI technology has led to great advancements in the translation of AI algorithms from research to real clinical practice. In this context, AI has demonstrated itself to be a powerful tool to gain novel insights, pursue different objectives, and seek alternative solutions that are applicable to solve medical and clinical problems in a more accurate, instant, and automated way. This Special Issue, therefore, seeks original contributions (articles, reviews, comments, etc.) involving the processing of medical and clinical images, AI algorithm development, medical device and software development, and image quality improvements.

Topics include, but are not limited to, the following:

  • Medical data processing and analysis;
  • Deep learning and machine learning for bioengineering;
  • Deep learning and machine learning for clinical applications;
  • Biomedical and health informatics;
  • Synthetic medical image generation;
  • Medical auto-analysis and disease progression predictions;
  • Explainable AI in medicine;
  • Artificial intelligence medical systems.

Sincerely,

Dr. Teng Grace Zhang
Dr. Jason Pui Yin Cheung
Dr. Tianjiao Zeng
Dr. Nan Meng
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
  • deep learning
  • machine learning
  • computer-aided analysis
  • progress prediction
  • image processing
  • interpretability for collecting review reports

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Published Papers (3 papers)

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Research

23 pages, 2302 KiB  
Article
CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images
by Tewodros Gizaw Tohye, Zhiguang Qin, Mugahed A. Al-antari, Chiagoziem C. Ukwuoma, Zenebe Markos Lonseko and Yeong Hyeon Gu
Bioengineering 2024, 11(9), 887; https://doi.org/10.3390/bioengineering11090887 - 31 Aug 2024
Viewed by 800
Abstract
Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed [...] Read more.
Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications 2nd Edition)
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17 pages, 2168 KiB  
Article
Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients
by Abhinav Nair, M. Abdulhadi Alagha, Justin Cobb and Gareth Jones
Bioengineering 2024, 11(8), 824; https://doi.org/10.3390/bioengineering11080824 - 12 Aug 2024
Viewed by 868
Abstract
Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to [...] Read more.
Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687–0.781) and 0.747 (95% CI 0.701–0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features. Full article
(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications 2nd Edition)
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19 pages, 7835 KiB  
Article
Auxiliary Diagnosis of Dental Calculus Based on Deep Learning and Image Enhancement by Bitewing Radiographs
by Tai-Jung Lin, Yen-Ting Lin, Yuan-Jin Lin, Ai-Yun Tseng, Chien-Yu Lin, Li-Ting Lo, Tsung-Yi Chen, Shih-Lun Chen, Chiung-An Chen, Kuo-Chen Li and Patricia Angela R. Abu
Bioengineering 2024, 11(7), 675; https://doi.org/10.3390/bioengineering11070675 - 2 Jul 2024
Cited by 2 | Viewed by 1224
Abstract
In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive [...] Read more.
In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services. Full article
(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications 2nd Edition)
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