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Bioengineering
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Artificial Intelligence in Biomedical Diagnosis and Prognosis: Second Edition
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Artificial Intelligence in Biomedical Diagnosis and Prognosis: Second Edition

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3666

Special Issue Editor

Prof. Dr. Tae-Seong Kim

E-Mail Website
Guest Editor
Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin 17104, Republic of Korea
Interests: AI deep learning; machine learning; pattern recognition; brain engineering; biomedical imaging/signal analysis; robot intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the fields of biomedical diagnosis and prognosis are being revolutionized with artificial intelligence (AI). These fields are advancing along with the advancements of AI methodologies, especially deep learning methods for disease detection, segmentation, classification, diagnosis and even prognosis, improving their accuracy and reliability. AI methodologies are becoming pervasive in the systems of medical image analysis, computer-aided diagnosis, clinical decision support, health monitoring and even disease prognosis.

This Special Issue intends to share the novel ideas and works of researchers and technical experts in the fields of biomedical diagnosis and prognosis.

This Special Issue is dedicated to high-quality, original research papers in the overlapping fields of the following:

  • Medical diagnosis and prognosis;
  • Medical deep learning;
  • Medical AI;
  • Pervasive AI in biomedicine;
  • Explainable AI for diagnosis and prognosis;
  • Medical image analysis;
  • Health monitoring systems;
  • Clinical decision support systems;
  • Computer-aided diagnosis systems;
  • Robotics for medical diagnosis and prognosis.

You may like to read the papers in the first volume of our Special Issue:

Volume 1: https://www.mdpi.com/journal/bioengineering/special_issues/90KE9ZQCKV.

Prof. Dr. Tae-Seong Kim
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 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

  • medical diagnosis
  • medical prognosis
  • medical AI
  • machine learning
  • deep learning
  • pervasive AI medical systems

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 2517 KiB  
Article
Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis
by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos and Elpiniki Papageorgiou
Bioengineering 2024, 11(10), 957; https://doi.org/10.3390/bioengineering11100957 - 25 Sep 2024
Viewed by 1177
Abstract
Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. [...] Read more.
Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. It investigates the impact of incorporating human clinical judgments into the RF model’s predictive capabilities. We recruited 606 patients from the Department of Nuclear Medicine at the University Hospital of Patras, Greece, from 16 February 2018 to 28 February 2022. Clinical data inputs included age, sex, comprehensive cardiovascular history (including prior myocardial infarction and revascularisation), CAD predisposing factors (such as hypertension, dyslipidemia, smoking, diabetes, and peripheral arteriopathy), baseline ECG abnormalities, and symptomatic descriptions ranging from asymptomatic states to angina-like symptoms and dyspnea on exertion. The diagnostic accuracies of human evaluators and the RF model (when trained with datasets inclusive of human judges’ assessments) were comparable at 79% and 80.17%, respectively. However, the performance of the RF model notably declined to 73.76% when human clinical judgments were excluded from its training dataset. These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnosis and Prognosis: Second Edition)
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17 pages, 4343 KiB  
Article
AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis
by Khadija Azhar, Byoung-Dai Lee, Shi Sub Byon, Kyu Ran Cho and Sung Eun Song
Bioengineering 2024, 11(9), 890; https://doi.org/10.3390/bioengineering11090890 - 1 Sep 2024
Viewed by 2022
Abstract
Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden [...] Read more.
Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists’ annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnosis and Prognosis: Second Edition)
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