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Application of Artificial Intelligence in Biomedical Informatics
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Application of Artificial Intelligence in Biomedical Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 4435

Special Issue Editor

Dr. Juan Martinez-Romo

E-Mail Website
Guest Editor
Computer Languages and Systems Department, National University of Distance Education, 28006 Madrid, Spain
Interests: machine learning; context-aware inference models; telemedicine applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Welcome to this Special Issue on the "Application of Artificial Intelligence in Biomedical Informatics". In recent years, the intersection of artificial intelligence (AI) and biomedical informatics has revolutionized healthcare by offering innovative solutions to complex problems. This Special Issue aims to explore the latest advancements, challenges, and opportunities in leveraging AI techniques to enhance biomedical informatics research and applications. From disease diagnosis and personalized medicine to healthcare management and drug discovery, AI is reshaping the landscape of biomedical informatics, promising improved patient outcomes and more efficient healthcare delivery. Join us as we delve into the cutting-edge research and developments driving this transformative field forward.

  • Biomedical informatics;
  • Knowledge-based systems;
  • AI-based clinical decision making;
  • Text mining and health informatics;
  • Natural language processing (NLP) in healthcare;
  • Multilingual clinical NLP;
  • Deep learning applied to healthcare.

Dr. Juan Martinez-Romo
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • biomedical informatics
  • decision making
  • healthcare

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Published Papers (5 papers)

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Research

15 pages, 2788 KiB  
Article
A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
by Sergi Baena-Miret, Ferran Reverter, Alex Sánchez and Esteban Vegas
Appl. Sci. 2025, 15(7), 3650; https://doi.org/10.3390/app15073650 - 26 Mar 2025
Viewed by 271
Abstract
High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimization techniques, including regularization and constraint-based approaches, have been [...] Read more.
High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimization techniques, including regularization and constraint-based approaches, have been already employed for regression and binary classification problems. Building on these methods, we extended this framework to support multi-class classification. Indeed, applied to a multi-class classification task for breast cancer subtypes, our model achieves accuracy between 73% and 81% under various block-wise missing data scenarios. Additionally, we assess its performance on a regression problem using the exposome dataset, integrating a larger number of omics datasets. Across different missing data scenarios, our model demonstrates a strong correlation (75%) between true and predicted responses. Furthermore, we have updated the bwm R package, which previously supported binary and continuous response types, to also include multi-class response types. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Biomedical Informatics)
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21 pages, 6202 KiB  
Article
Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images
by Mehmet Ali Şimşek, Ahmet Sertbaş, Hadi Sasani and Yaşar Mahsut Dinçel
Appl. Sci. 2025, 15(5), 2752; https://doi.org/10.3390/app15052752 - 4 Mar 2025
Viewed by 846
Abstract
The meniscus is a C-shaped connective tissue with a cartilage-like structure in the knee joint. This study proposes an innovative method based on You Only Look Once (YOLO) series models and ensemble methods for meniscus segmentation from knee magnetic resonance imaging (MRI) images [...] Read more.
The meniscus is a C-shaped connective tissue with a cartilage-like structure in the knee joint. This study proposes an innovative method based on You Only Look Once (YOLO) series models and ensemble methods for meniscus segmentation from knee magnetic resonance imaging (MRI) images to improve segmentation performance and evaluate generalization capability. In this study, five different segmentation models were trained, and masks were created from the YOLO series. These masks are combined with pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting optimized by grid search. Tests were conducted on internal and external sets and various metrics. The dynamic weighted multiple voting method optimized with grid search performed the best on both the test set (DSC: 0.8976 ± 0.0071, PPV: 0.8561 ± 0.0121, Sensitivity: 0.9467 ± 0.0077) and the external set (DSC: 0.9004 ± 0.0064, PPV: 0.8876 ± 0.0134, Sensitivity: 0.9200 ± 0.0119). The proposed ensemble methods offer high accuracy, reliability, and generalization capability for meniscus segmentation. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Biomedical Informatics)
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20 pages, 6725 KiB  
Article
Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty
by Roel Pantonial and Milan Simic
Appl. Sci. 2025, 15(2), 872; https://doi.org/10.3390/app15020872 - 17 Jan 2025
Viewed by 739
Abstract
The application of gait analysis on patients with Hip Osteoarthritis (HOA) before and after Total Hip Arthroplasty (THA) surgery can provide accurate diagnostics, reliable treatment decision making, and proper rehabilitation efforts. Acquired kinematic trajectories provide discriminating features that can be used to determine [...] Read more.
The application of gait analysis on patients with Hip Osteoarthritis (HOA) before and after Total Hip Arthroplasty (THA) surgery can provide accurate diagnostics, reliable treatment decision making, and proper rehabilitation efforts. Acquired kinematic trajectories provide discriminating features that can be used to determine the gait patterns of healthy subjects and the effects of surgical operation. However, there is still a lack of consensus on the best discriminating kinematics to achieve this. Our investigation aims to utilize Deep Learning (DL) methodologies and improve classification results for the kinematic parameters of healthy, HOA, and 6 months post-THA gait cycles. Kinematic angles from the lower limb are used directly as one-dimensional inputs into a DL model. Based on the human gait cycle’s features, a hybrid Long Short-Term Memory–Convolutional Neural Network (HLSTM-CNN) is designed for the classification of healthy/HOA/THA gaits. It was found, from the results, that the sagittal angles of hip and knee, and front angles of FPA and knee, provide the most discriminating results with accuracy above 94% between healthy and HOA gaits. Interestingly, when using the sagittal angles of hip and knee to analyze the THA gaits, common subjects have the same results on the misclassifications. This crucial information provides a glimpse in the determination for the success or failure of THA. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Biomedical Informatics)
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26 pages, 4034 KiB  
Article
Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
by Kaushlesh Singh Shakya, Azadeh Alavi, Julie Porteous, Priti Khatri, Amit Laddi, Manojkumar Jaiswal and Vinay Kumar
Appl. Sci. 2024, 14(23), 11154; https://doi.org/10.3390/app142311154 - 29 Nov 2024
Viewed by 856
Abstract
In orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity [...] Read more.
In orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity and variability in the shapes of sella and the lack of advanced assessment tools make the classification of sella challenging, as it requires extensive training, skills, time, and manpower to detect subtle changes that often may not be apparent. Moreover, existing semi-supervised learning (SSL) methods face key limitations such as shift invariance, inadequate feature representation, overfitting on small datasets, and a lack of generalization to unseen variations in ST morphology. Medical imaging data are often unlabeled, limiting the training of automated classification systems for ST morphology. To address these limitations, a novel semi-supervised deep subspace embedding (SSLDSE) framework is proposed. This approach integrates real-time stochastic augmentation to significantly expand the training dataset and introduce natural variability in the ST morphology, overcoming the constraints of small and non-representative datasets. Non-linear features are extracted and mapped to a non-linear subspace using Kullback–Leibler divergence, which ensures that the model remains consistent despite image transformations, thus resolving issues related to shift invariance. Additionally, fine-tuning the Inception-ResNet-v2 network on these enriched features reduces retraining costs when new unlabeled data becomes available. t-distributed stochastic neighbor embedding (t-SNE) is employed for effective feature representation through manifold learning, capturing complex patterns that previous methods might miss. Finally, a zero-shot classifier is utilized to accurately categorize the ST, addressing the challenge of classifying new or unseen variations. Further, the proposed SSLDSE framework is evaluated through comparative analysis with the existing methods (Active SSL, GAN SSL, Contrastive SSL, Modified Inception-ResNet-v2) for ST classification using various evaluation metrics. The SSLDSE and the existing methods are trained on our dataset (sourced from PGI Chandigarh, India), and a blind test is conducted on the benchmark dataset (IEEE ISBI 2015). The proposed method improves classification accuracy by 15% compared to state-of-the-art models and reduces retraining costs. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Biomedical Informatics)
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13 pages, 2433 KiB  
Article
Multi-Model Gait-Based KAM Prediction System Using LSTM-RNN and Wearable Devices
by Doyun Jung, Cheolwon Lee and Heung Seok Jeon
Appl. Sci. 2024, 14(22), 10721; https://doi.org/10.3390/app142210721 - 19 Nov 2024
Viewed by 959
Abstract
The purpose of this study is to develop an optimized system for predicting Knee Adduction Moment (KAM) using wearable Inertial Measurement Unit (IMU) sensors and Long Short-Term Memory (LSTM) RNN. Traditional KAM measurement methods are limited by the need for complex laboratory equipment [...] Read more.
The purpose of this study is to develop an optimized system for predicting Knee Adduction Moment (KAM) using wearable Inertial Measurement Unit (IMU) sensors and Long Short-Term Memory (LSTM) RNN. Traditional KAM measurement methods are limited by the need for complex laboratory equipment and significant time and cost investments. This study proposes two systems for predicting Knee Adduction Moment based on wearable IMU sensor data and gait patterns: the Multi-model Gait-based KAM Prediction System and the Single-model KAM Prediction System. The Multi-model system pre-classifies different gait patterns and uses specific prediction models tailored for each pattern, while the Single-model system handles all gait patterns with one unified model. Both systems were evaluated using IMU sensor data and GRF data collected from participants in a controlled laboratory environment. The overall performance of the Multi-model Gait-based KAM Prediction System showed an approximately 20% improvement over the Single-model KAM Prediction System. Specifically, the RMSE for the Multi-model system was 6.84 N·m, which is lower than the 8.82 N·m of the Single-model system, indicating a better predictive accuracy. The Multi-model system also achieved a MAPE of 8.47%, compared with 12.95% for the Single-model system, further demonstrating its superior performance. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Biomedical Informatics)
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