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Bioengineering
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Artificial Intelligence for Computer-Aided Detection in Biomedical Applications
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Artificial Intelligence for Computer-Aided Detection in Biomedical Applications

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 8696

Special Issue Editor

Dr. Lawrence Chan

E-Mail Website
Guest Editor
Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
Interests: bioinformatics; imaging informatics; clinical decision support

Special Issue Information

Dear Colleagues,

The use of Artificial Intelligence (AI) in Computer-Aided Detection (CAD) has led to significant advancements in biomedical applications. AI encompasses the development of intelligent machines that can simulate human intelligence, enabling them to learn from large datasets and make predictions or decisions based on complex patterns and algorithms. CAD systems, on the other hand, aid healthcare professionals in the identification and analysis of various medical conditions, utilizing computer algorithms to improve accuracy and efficiency.

This Special Issue explores the integration of AI techniques within CAD systems to revolutionize biomedical applications. It aims to present work from researchers and practitioners from multidisciplinary backgrounds and discuss the latest advancements, challenges, and future prospects in this rapidly growing field.

Topics of interest within this Special Issue include, but are not limited to, the development and evaluation of novel AI algorithms for CAD in biomedical imaging, the application of machine learning techniques to enhance detection and diagnosis accuracy, the utilization of deep learning architectures in CAD systems, the integration of AI technologies into medical decision making, the impact of AI on CAD-assisted diagnosis and treatment planning, and ethical considerations surrounding the use of AI in biomedical applications.

The papers contributed to this Special Issue will provide valuable insights into the potential of AI-powered CAD systems in biomedical domains, paving the way for improved detection, diagnosis, prognosis, and personalized treatment strategies. Researchers and practitioners across fields such as computer science, biomedical engineering, radiology, medical imaging, and bioinformatics are encouraged to submit their original research, reviews, and case studies.

Dr. Lawrence Chan
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

  • artificial intelligence
  • computer-aided detection
  • CAD
  • biomedical applications
  • biomedical engineering
  • radiology
  • medical imaging
  • bioinformatics
  • deep learning
  • machine learning
  • disease diagnosis

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Published Papers (6 papers)

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Research

13 pages, 2288 KiB  
Article
A Longitudinal Model for a Dynamic Risk Score to Predict Delayed Cerebral Ischemia after Subarachnoid Hemorrhage
by Jan F. Willms, Corinne Inauen, Stefan Yu Bögli, Carl Muroi, Jens M. Boss and Emanuela Keller
Bioengineering 2024, 11(10), 988; https://doi.org/10.3390/bioengineering11100988 - 30 Sep 2024
Viewed by 467
Abstract
Background: Accurate longitudinal risk prediction for DCI (delayed cerebral ischemia) occurrence after subarachnoid hemorrhage (SAH) is essential for clinicians to administer appropriate and timely diagnostics, thereby improving treatment planning and outcome. This study aimed to develop an improved longitudinal DCI prediction model and [...] Read more.
Background: Accurate longitudinal risk prediction for DCI (delayed cerebral ischemia) occurrence after subarachnoid hemorrhage (SAH) is essential for clinicians to administer appropriate and timely diagnostics, thereby improving treatment planning and outcome. This study aimed to develop an improved longitudinal DCI prediction model and evaluate its performance in predicting DCI between day 4 and 14 after aneurysm rupture. Methods: Two DCI classification models were trained: (1) a static model based on routinely collected demographics and SAH grading scores and (2) a dynamic model based on results from laboratory and blood gas analysis anchored at the time of DCI. A combined model was derived from these two using a voting approach. Multiple classifiers, including Logistic Regression, Support Vector Machines, Random Forests, Histogram-based Gradient Boosting, and Extremely Randomized Trees, were evaluated through cross-validation using anchored data. A leave-one-out simulation was then performed on the best-performing models to evaluate their longitudinal performance using time-dependent Receiver Operating Characteristic (ROC) analysis. Results: The training dataset included 218 patients, with 89 of them developing DCI (41%). In the anchored ROC analysis, the combined model achieved a ROC AUC of 0.73 ± 0.05 in predicting DCI onset, the static and the dynamic model achieved a ROC AUC of 0.69 ± 0.08 and 0.66 ± 0.08, respectively. In the leave-one-out simulation experiments, the dynamic and voting model showed a highly dynamic risk score (intra-patient score range was 0.25 [0.24, 0.49] and 0.17 [0.12, 0.25] for the dynamic and the voting model, respectively, for DCI occurrence over the course of disease. In the time-dependent ROC analysis, the dynamic model performed best until day 5.4, and afterwards the voting model showed the best performance. Conclusions: A machine learning model for longitudinal DCI risk assessment was developed comprising a static and a dynamic sub-model. The longitudinal performance evaluation highlighted substantial time dependence in model performance, underscoring the need for a longitudinal assessment of prediction models in intensive care settings. Moreover, clinicians need to be aware of these performance variations when performing a risk assessment and weight the different model outputs correspondingly. Full article
(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)
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16 pages, 2070 KiB  
Article
Automatic Transcranial Sonography-Based Classification of Parkinson’s Disease Using a Novel Dual-Channel CNXV2-DANet
by Hongyu Kang, Xinyi Wang, Yu Sun, Shuai Li, Xin Sun, Fangxian Li, Chao Hou, Sai-kit Lam, Wei Zhang and Yong-ping Zheng
Bioengineering 2024, 11(9), 889; https://doi.org/10.3390/bioengineering11090889 - 31 Aug 2024
Viewed by 797
Abstract
Transcranial sonography (TCS) has been introduced to assess hyper-echogenicity in the substantia nigra of the midbrain for Parkinson’s disease (PD); however, its subjective and resource-demanding nature has impeded its widespread application. An AI-empowered TCS-based PD classification tool is greatly demanding, yet relevant research [...] Read more.
Transcranial sonography (TCS) has been introduced to assess hyper-echogenicity in the substantia nigra of the midbrain for Parkinson’s disease (PD); however, its subjective and resource-demanding nature has impeded its widespread application. An AI-empowered TCS-based PD classification tool is greatly demanding, yet relevant research is severely scarce. Therefore, we proposed a novel dual-channel CNXV2-DANet for TCS-based PD classification using a large cohort. A total of 1176 TCS images from 588 subjects were retrospectively enrolled from Beijing Tiantan Hospital, encompassing both the left and right side of the midbrain for each subject. The entire dataset was divided into a training/validation/testing set at a ratio of 70%/15%/15%. Development of the proposed CNXV2-DANet was performed on the training set with comparisons between the single-channel and dual-channel input settings; model evaluation was conducted on the independent testing set. The proposed dual-channel CNXV2-DANet was compared against three state-of-the-art networks (ConvNeXtV2, ConvNeXt, Swin Transformer). The results demonstrated that both CNXV2-DANet and ConvNeXt V2 performed more superiorly under dual-channel inputs than the single-channel input. The dual-channel CNXV2-DANet outperformed the single-channel, achieving superior average metrics for accuracy (0.839 ± 0.028), precision (0.849 ± 0.014), recall (0.845 ± 0.043), F1-score (0.820 ± 0.038), and AUC (0.906 ± 0.013) compared with the single channel metrics for accuracy (0.784 ± 0.037), precision (0.817 ± 0.090), recall (0.748 ± 0.093), F1-score (0.773 ± 0.037), and AUC (0.861 ± 0.047). Furthermore, the dual-channel CNXV2-DANet outperformed all other networks (all p-values < 0.001). These findings suggest that the proposed dual-channel CNXV2-DANet may provide the community with an AI-empowered TCS-based tool for PD assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)
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16 pages, 926 KiB  
Article
Adaptive Detection in Real-Time Gait Analysis through the Dynamic Gait Event Identifier
by Yifan Liu, Xing Liu, Qianhui Zhu, Yuan Chen, Yifei Yang, Haoyu Xie, Yichen Wang and Xingjun Wang
Bioengineering 2024, 11(8), 806; https://doi.org/10.3390/bioengineering11080806 - 8 Aug 2024
Viewed by 940
Abstract
The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time [...] Read more.
The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases. Full article
(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)
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12 pages, 1915 KiB  
Article
Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation
by Pranai Tandon, Kim-Anh-Nhi Nguyen, Masoud Edalati, Prathamesh Parchure, Ganesh Raut, David L. Reich, Robert Freeman, Matthew A. Levin, Prem Timsina, Charles A. Powell, Zahi A. Fayad and Arash Kia
Bioengineering 2024, 11(6), 626; https://doi.org/10.3390/bioengineering11060626 - 19 Jun 2024
Viewed by 1019
Abstract
The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data [...] Read more.
The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains. Full article
(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)
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16 pages, 5312 KiB  
Article
Pixel-Wise Interstitial Lung Disease Interval Change Analysis: A Quantitative Evaluation Method for Chest Radiographs Using Weakly Supervised Learning
by Subin Park, Jong Hee Kim, Jung Han Woo, So Young Park, Yoon Ki Cha and Myung Jin Chung
Bioengineering 2024, 11(6), 562; https://doi.org/10.3390/bioengineering11060562 - 2 Jun 2024
Viewed by 655
Abstract
Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise [...] Read more.
Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD. Full article
(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)
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15 pages, 4435 KiB  
Article
Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation
by Luoyi Kong, Mohan Huang, Lingfeng Zhang and Lawrence Wing Chi Chan
Bioengineering 2024, 11(3), 270; https://doi.org/10.3390/bioengineering11030270 - 9 Mar 2024
Cited by 1 | Viewed by 3013
Abstract
Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize [...] Read more.
Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm. Full article
(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)
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