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23 pages, 2632 KB

Open AccessArticle

A Study of SimCLR-Based Self-Supervised Learning for Acne Severity Grading Under Label-Scarce Conditions

by Krittakom Srijiranon, Nanmanat Varisthanist and Tanatorn Tanantong

Technologies 2026, 14(2), 116; https://doi.org/10.3390/technologies14020116 - 12 Feb 2026

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Acne severity grading is an important dermatological task that supports clinical diagnosis, treatment planning, and disease monitoring. Although self-supervised learning (SSL) has gained interest as a means to reduce reliance on large annotated datasets, its effectiveness for fine-grained and ordinal dermatological tasks remains [...] Read more.

Acne severity grading is an important dermatological task that supports clinical diagnosis, treatment planning, and disease monitoring. Although self-supervised learning (SSL) has gained interest as a means to reduce reliance on large annotated datasets, its effectiveness for fine-grained and ordinal dermatological tasks remains unclear. This research systematically evaluates contrastive SSL for acne severity grading by comparing SimCLR-based pretraining with a diverse set of supervised deep learning models, including Convolutional Neural Networks and Vision Transformers, under controlled experimental conditions. The evaluation considers full-data training, label-scarce scenarios, and temperature tuning of the contrastive loss. The results consistently demonstrate the superiority of supervised transfer learning, which achieves Quadratic Weighted Kappa (QWK) scores ranging from 0.7616 to 0.8533. In contrast, SimCLR-based models exhibit substantially lower performance, with QWK values between 0.2343 and 0.4548 after fine-tuning. Although temperature adjustment achieved modest performance gains, it does not close this gap, with the best configuration attaining a QWK of 0.4548 using a ResNet18 backbone. Qualitative analysis using Grad-CAM further reveals that SimCLR-based contrastive SSL tends to exhibit diffuse attention patterns and limited localization of clinically relevant acne regions. Overall, these findings indicate that generic contrastive SSL objectives are poorly aligned with the subtle and localized visual cues required for acne severity grading. The results highlight the need for domain-aware representation learning in fine-grained dermatological image analysis. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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50 pages, 3261 KB

Open AccessArticle

Impact of Internal Validation Protocols on Predictive Maintenance Performance in Biomedical Equipment

by Jihanne Moufid, Rim Koulali, Khalid Moussaid and Noreddine Abghour

Technologies 2026, 14(2), 115; https://doi.org/10.3390/technologies14020115 - 12 Feb 2026

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Abstract

Predictive maintenance (PdM) is a strategic enabler of healthcare digitalization, yet its deployment remains constrained by methodological weaknesses in model evaluation. Biomedical maintenance data, structured around equipment life cycles and repeated interventions, violate the independence and stationarity assumptions of conventional random cross-validation. This [...] Read more.

Predictive maintenance (PdM) is a strategic enabler of healthcare digitalization, yet its deployment remains constrained by methodological weaknesses in model evaluation. Biomedical maintenance data, structured around equipment life cycles and repeated interventions, violate the independence and stationarity assumptions of conventional random cross-validation. This work presents an empirical analysis of internal validation protocol design using a real-world, multi-hospital dataset comprising 3403 maintenance interventions. Three classification models (logistic regression, random forest, histogram-based gradient boosting) are evaluated under four validation schemes: random K-fold, equipment-grouped K-fold, temporal holdout, and roll-forward validation. The results reveal a consistent decrease in apparent predictive performance as validation constraints are progressively strengthened. Random cross-validation overestimates AUROC by approximately 0.03–0.06 compared with temporally constrained protocols. Under deployment-aligned temporal validation, model performance stabilizes at an AUROC of approximately 0.83–0.84. Equipment-grouped and temporal validation effectively mitigate structural bias and yield more stable and interpretable models. These findings highlight the critical role of validation protocol choice in the credible assessment of predictive maintenance models and provide practical guidance for the deployment of PdM systems based on real-world data in resource-limited healthcare environments. The analysis is limited to public hospitals within a single national context and relies on a class-balanced experimental subset, which may affect the direct transferability of absolute performance estimates to other healthcare systems or operational settings. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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25 pages, 4090 KB

Open AccessArticle

TPHFC-Net—A Triple-Path Heterogeneous Feature Collaboration Network for Enhancing Motor Imagery Classification

by Yuchen Jin, Chunxu Dou, Dingran Wang and Chao Liu

Technologies 2026, 14(2), 96; https://doi.org/10.3390/technologies14020096 - 2 Feb 2026

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Abstract

Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional methods, predominantly reliant on convolutional neural networks (CNNs), are proficient at extracting local temporal features [...] Read more.

Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional methods, predominantly reliant on convolutional neural networks (CNNs), are proficient at extracting local temporal features but struggle to capture long-range dependencies and global contextual information. To address this limitation, we propose a Triple-path Heterogeneous Feature Collaboration Network (TPHFC-Net), which synergistically integrates three distinct temporal modeling pathways: a multi-scale Temporal Convolutional Network (TCN) to capture fine-grained local dynamics, a Transformer branch to model global dependencies via multi-head self-attention, and a Long Short-Term Memory (LSTM) network to track sequential state evolution. These heterogeneous features are subsequently fused adaptively by a dynamic gating mechanism. In addition, the model’s robustness and discriminative power are further augmented by a lightweight front-end denoising diffusion model for enhanced noisy feature representation and a back-end prototype attention mechanism to bolster the inter-class separability of non-stationary EEG features. Extensive experiments on the BCI Competition IV-2a and IV-2b datasets validate the superiority of the proposed model, achieving mean classification accuracies of 82.45% and 89.49%, respectively, on the subject-dependent MI task and significantly outperforming existing mainstream baselines. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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21 pages, 2865 KB

Open AccessArticle

Multimodal Clustering and Spatiotemporal Analysis of Wearable Sensor Data for Occupational Health Risk Monitoring

by Yangsheng Wang, Shukun Lai, Honglin Mu, Shenyang Xu, Rong Hu and Chih-Yu Hsu

Technologies 2026, 14(1), 38; https://doi.org/10.3390/technologies14010038 - 5 Jan 2026

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Abstract

Accurate interpretation of multimodal wearable data remains challenging in occupational environments due to heterogeneous sensing modalities, motion artifacts, and dynamic work conditions. This study proposes and validates an adaptive multimodal clustering framework for occupational health monitoring. The framework jointly models physiological, activity, and [...] Read more.

Accurate interpretation of multimodal wearable data remains challenging in occupational environments due to heterogeneous sensing modalities, motion artifacts, and dynamic work conditions. This study proposes and validates an adaptive multimodal clustering framework for occupational health monitoring. The framework jointly models physiological, activity, and location data from 24 highway-maintenance workers, incorporating a silhouette-guided feature-weighting mechanism, multi-scale temporal change-point detection, and KDE-based spatial analysis. Specifically, the analysis identified three distinct and interpretable behavioral–physiological states that exhibit significant physiological differences (p < 0.001). Notably, it reveals a predominant yet heterogeneous baseline state alongside acute high-intensity and episodic surge states, offering a nuanced view of occupational risk beyond single-modality thresholds. The integrated framework provides a principled analytical workflow for spatiotemporal health risk assessment in field settings, particularly for vibration-intensive work scenarios, while highlighting the complementary role of physiological indicators in low- or static-motion tasks. This framework is particularly effective for vibration-intensive tasks involving powered tools. However, to mitigate potential biases in detecting static heavy-load activities with limited wrist motion (e.g., lifting or carrying), future extensions should incorporate complementary weighting of physiological indicators such as heart rate variability. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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18 pages, 1314 KB

Open AccessArticle

Opinion Mining-Driven Classification Model for Early Autism Spectrum Disorders Identification Based on Standardized Assessments

by José Roberto Grande-Ramírez, Eduardo Roldán-Reyes, Guillermo Cortés-Robles, Jesús Delgado-Maciel, Marisol Morales-Saucedo and Marco Antonio Díaz-Martínez

Technologies 2026, 14(1), 36; https://doi.org/10.3390/technologies14010036 - 5 Jan 2026

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Abstract

The efforts to achieve early detection of autism spectrum disorders (ASD) are becoming increasingly important due to the high prevalence that continues to persist globally. The World Health Organization (WHO) and other official institutions agree that in marginalized regions, it is urgently necessary [...] Read more.

The efforts to achieve early detection of autism spectrum disorders (ASD) are becoming increasingly important due to the high prevalence that continues to persist globally. The World Health Organization (WHO) and other official institutions agree that in marginalized regions, it is urgently necessary to develop effective alternatives and methods to improve the quality of life of children and their families. This study presents an integrated model for the early detection of ASD, based on the analysis of parental observations and supported by validated diagnostic tools. The proposed approach consists of four sequential modules, aiming to improve early detection through techniques such as natural language processing (NLP) and machine learning (ML) metrics. Records from two Latin American countries were standardized, thereby consolidating a single database comprising 153 records of children aged 2 to 6 years. The Parent Interview Instrument (PII) was administered by specialists to caregivers and subsequently compared with standardized tests. Encouraging results were obtained from the support vector machine (SVM) classification algorithm, yielding an accuracy range of 89.88–91.34%, a maximum precision of 90.02%, a recall of 89.02%, and a maximum F-measure of 91.12%. The results of the case study allow us to identify disorders related to autism, such as the repetition of behaviors, difficulties in social interaction, and issues with verbal expression. This contribution aligns with the United Nations Sustainable Development Goal 3, which promotes health and well-being. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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24 pages, 2551 KB

Open AccessArticle

Towards Intelligent Virtual Clerks: AI-Driven Automation for Clinical Data Entry in Dialysis Care

by Perasuk Worragin, Suepphong Chernbumroong, Kitti Puritat, Phichete Julrode and Kannikar Intawong

Technologies 2025, 13(11), 530; https://doi.org/10.3390/technologies13110530 - 17 Nov 2025

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Abstract

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study [...] Read more.

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study presents the design and evaluation of an AI-enhanced OCR system that integrates advanced image processing, rule-based validation, and large language model-driven anomaly detection to improve data accuracy, workflow efficiency, and user experience. A total of 65 laboratory reports, each containing approximately 35 fields, were processed and compared under two configurations: a basic OCR system and the AI-enhanced OCR system. System performance was evaluated using three key metrics: error detection accuracy across three error categories (Missing Values, Out-of-Range, and Typo/Free-text), workflow efficiency measured by average processing time per record and total completion time, and user acceptance measured using the System Usability Scale (SUS). The AI-enhanced OCR system outperformed the basic OCR system in all metrics, particularly in detecting and correcting Out-of-Range errors, such as decimal placement issues, achieving near-perfect precision and recall. It reduced the average processing time per record by almost 50% (85.2 to 42.1 s) and improved usability, scoring 81.0 (Excellent) compared to 75.0 (Good). These results demonstrate the potential of AI-driven OCR to reduce clerical workload, improve healthcare data quality, and streamline clinical workflows, while maintaining a human-in-the-loop verification process to ensure patient safety and data integrity. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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21 pages, 3338 KB

Open AccessArticle

Enhancing Migraine Classification Through Machine Learning: A Comparative Study of Ensemble Methods

by Raniya R. Sarra, Ayad E. Korial, Ivan Isho Gorial and Amjad J. Humaidi

Technologies 2025, 13(11), 500; https://doi.org/10.3390/technologies13110500 - 1 Nov 2025

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Abstract

A migraine is a common and complex neurological disorder affecting more than 90% of people globally. Traditional migraine diagnostic and classification methods are time-intensive and prone to error. In today’s world, where health and technology are closely connected, there is an urgent need [...] Read more.

A migraine is a common and complex neurological disorder affecting more than 90% of people globally. Traditional migraine diagnostic and classification methods are time-intensive and prone to error. In today’s world, where health and technology are closely connected, there is an urgent need for more advanced tools to accurately predict and classify migraine types. Machine learning (ML) has shown promise in automating migraine diagnoses and classification. However, individual ML classifiers may not always work well, which means that they need to be improved. In this paper, we used three ML classifiers that include decision tree, naïve Bayes, and k-nearest neighbor to classify seven different types of migraines. We also investigated ensemble classifiers like bagging, boosting, stacking, and majority voting to obtain better results. All classifiers were trained on a migraine dataset of 400 patients with 24 features. Before training the classifiers, we pre-processed the data by balancing the classes, removing useless features, and checking for correlations. After evaluating the performance, the results showed that majority voting achieved the highest accuracy improvement (7.59%), followed by boosting (6.55%), bagging (5.86%), and stacking (5.52%). These results indicate that the ensemble methods are effective in improving the classification accuracy of individual ML classifiers when it comes to classifying migraines. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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12 pages, 1053 KB

Open AccessArticle

EEG-Based Music Stimuli Classification Using Artificial Neural Network and the OpenBCI CytonDaisy System

by Jozsef Suto and Rahul Suresh Kumar

Technologies 2025, 13(9), 426; https://doi.org/10.3390/technologies13090426 - 22 Sep 2025

Viewed by 1923

Abstract

This paper presents a comprehensive investigation about the use of electroencephalography (EEG) signals for classifying music stimuli through an artificial neural network (ANN). Employing the 16-channel OpenBCI CytonDaisy sensor, EEG data were gathered from participants while they listened to a variety of music [...] Read more.

This paper presents a comprehensive investigation about the use of electroencephalography (EEG) signals for classifying music stimuli through an artificial neural network (ANN). Employing the 16-channel OpenBCI CytonDaisy sensor, EEG data were gathered from participants while they listened to a variety of music tracks. This study examines the impact of varying time window lengths on classification accuracy, evaluates the neural network’s performance with different time- and frequency-domain features, analyzes the influence of diverse music on brain activity patterns, and reveals how songs of different styles affect various subjects. For the five subjects involved in the study, the recognition rate of the model fluctuated between 61% and 96%. The findings indicate that longer time windows, particularly 30 s, result in the highest classification accuracy. Despite the relatively high recognition rate, this study also highlights the issue of intra-individual variability. A substantial decline in performance can be observed when testing the model on data collected from the same person on a different day, underscoring the challenges posed by inter-session variability. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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15 pages, 562 KB

Open AccessArticle

Predicting Disease Activity Score in Rheumatoid Arthritis Patients Treated with Biologic Disease-Modifying Antirheumatic Drugs Using Machine Learning Models

by Fatemeh Salehi, Sara Zarifi, Sara Bayat, Mahdis Habibpour, Amirreza Asemanrafat, Arnd Kleyer, Georg Schett, Ruth Fritsch-Stork and Bjoern M. Eskofier

Technologies 2025, 13(8), 350; https://doi.org/10.3390/technologies13080350 - 8 Aug 2025

Cited by 1 | Viewed by 1779

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint inflammation and progressive disability. While biological disease-modifying antirheumatic drugs (bDMARDs) have significantly improved disease control, predicting individual treatment response remains clinically challenging. This study presents a machine learning approach to predict 12-month [...] Read more.

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint inflammation and progressive disability. While biological disease-modifying antirheumatic drugs (bDMARDs) have significantly improved disease control, predicting individual treatment response remains clinically challenging. This study presents a machine learning approach to predict 12-month disease activity, measured by DAS28-CRP, in RA patients beginning bDMARD therapy. We trained and evaluated eight regression models, including Ridge, Lasso, Support Vector Regression, and XGBoost, using baseline clinical features from 154 RA patients treated at University Hospital Erlangen. A rigorous nested cross-validation strategy was applied for internal model selection and validation. Importantly, model generalizability was assessed using an independent external dataset from the Austrian BioReg registry, which includes a more diverse, real-world RA patient population from across multiple clinical sites. The Ridge regression model achieved the best internal performance (MAE: 0.633, R²: 0.542) and showed strong external validity when applied to unseen BioReg data (MAE: 0.678, R²: 0.491). These results indicate robust cross-cohort generalization. By predicting continuous DAS28-CRP scores instead of binary remission labels, our approach supports flexible, individualized treatment planning based on local or evolving clinical thresholds. This work demonstrates the feasibility and clinical value of externally validated, data-driven tools for precision treatment planning in RA. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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19 pages, 2771 KB

Open AccessArticle

Dynamic Hypergraph Convolutional Networks for Hand Motion Gesture Sequence Recognition

by Dong-Xing Jing, Kui Huang, Shi-Jian Liu, Zheng Zou and Chih-Yu Hsu

Technologies 2025, 13(6), 257; https://doi.org/10.3390/technologies13060257 - 19 Jun 2025

Viewed by 1076

Abstract

This paper introduces a novel approach to hand motion gesture recognition by integrating the Fourier transform with hypergraph convolutional networks (HGCNs). Traditional recognition methods often struggle to capture the complex spatiotemporal dynamics of hand gestures. HGCNs, which are capable of modeling intricate relationships [...] Read more.

This paper introduces a novel approach to hand motion gesture recognition by integrating the Fourier transform with hypergraph convolutional networks (HGCNs). Traditional recognition methods often struggle to capture the complex spatiotemporal dynamics of hand gestures. HGCNs, which are capable of modeling intricate relationships among joints, are enhanced by Fourier transform to analyze gesture features in the frequency domain. A hypergraph is constructed to represent the interdependencies among hand joints, allowing for dynamic adjustments based on joint movements. Hypergraph convolution is applied to update node features, while the Fourier transform facilitates frequency-domain analysis. The T-Module, a multiscale temporal convolution module, aggregates features from multiple frames to capture gesture dynamics across different time scales. Experiments on the dynamic hypergraph (DHG14/28) and shape retrieval contest (SHREC’17) datasets demonstrate the effectiveness of the proposed method, achieving accuracies of 96.4% and 97.6%, respectively, and outperforming traditional gesture recognition algorithms. Ablation studies further validate the contributions of each component in enhancing recognition performance. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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26 pages, 8159 KB

Open AccessArticle

A Combined Mirror–EMG Robot-Assisted Therapy System for Lower Limb Rehabilitation

by Florin Covaciu, Bogdan Gherman, Calin Vaida, Adrian Pisla, Paul Tucan, Andrei Caprariu and Doina Pisla

Technologies 2025, 13(6), 227; https://doi.org/10.3390/technologies13060227 - 3 Jun 2025

Cited by 2 | Viewed by 4084

Abstract

This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The [...] Read more.

This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The system features a robotic platform specifically engineered for lower limb rehabilitation, which operates in conjunction with a virtual reality (VR) environment. This immersive environment comprises a digital twin of the robotic system alongside a human avatar representing the patient and a set of virtual targets to be reached by the patient. To implement mirror therapy, the proposed protocol utilizes a set of inertial sensors placed on the patient’s healthy limb to capture real-time motion data. The auto-adaptive protocol takes as input the EMG signals (if any) from sensors placed on the impaired limb and performs the required motions to reach the virtual targets in the VR application. By synchronizing the motions of the healthy limb with the digital twin in the VR space, the system aims to promote neuroplasticity, reduce pain perception, and encourage engagement in rehabilitation exercises. Initial laboratory trials demonstrate promising outcomes in terms of improved motor function and subject motivation. This research not only underscores the efficacy of integrating robotics and virtual reality in rehabilitation but also opens avenues for advanced personalized therapies in clinical settings. Future work will investigate the efficiency of the proposed solution using patients, thus demonstrating clinical usability, and explore the potential integration of additional feedback mechanisms to further enhance the therapeutic efficacy of the system. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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