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AI and Big Data Analytics for Medical E-Diagnosis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8714

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Special Issue Editors

Dr. Simon Fong

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Guest Editor

Department of Computer and Information Science, University of Macau, Macau 999078, China
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sabah Mohammed

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Guest Editor

Department of Computer Science, Lakehead University, ATAC 5013, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada
Interests: internet of medical things; web intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Victor Chang

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Guest Editor

Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK
Interests: Internet of Things; big data; industry 4.0; security/risk and cloud/edge/fog computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of medical diagnosis is rapidly evolving with the integration of artificial intelligence (AI) and big data analytics. These advanced methods, combined with diverse medical data—such as electronic health records, medical imaging, clinical tests, and sensor-based measurements—offer new opportunities for improving e-diagnosis. However, challenges like data noise, missing values, and class imbalances need to be addressed to ensure effective and reliable AI-driven diagnostics.

AI plays a crucial role in enhancing healthcare outcomes through accurate, timely diagnoses that support effective treatment planning and patient management. By leveraging AI and big data, advancements in early disease detection, personalized treatment, and proactive interventions are possible, potentially improving patient care, reducing costs, and saving lives.

This Special Issue will explore the challenges and opportunities in AI and big data analytics for medical e-diagnosis, with a focus on the role of sensors in providing real-time data to improve diagnostic accuracy. We invite researchers worldwide to contribute their work, aiming to address current challenges and unlock the full potential of AI-enhanced e-diagnosis. The high-quality submissions we have received underscore a strong international interest in advancing this crucial area of healthcare.

Dr. Simon Fong
Prof. Dr. Sabah Mohammed
Prof. Dr. Victor Chang
Guest Editors

Manuscript Submission Information

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Keywords

AI-driven medical diagnostics
big data analytics in healthcare
sensor-based e-diagnosis
healthcare data integration
real-time diagnostic systems

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Published Papers (5 papers)

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Research

15 pages, 389 KB

Open AccessArticle

NURSE-AI: A Nurse-by-Design Framework for Multi-Sensor, AI-Enabled Chronic Wound Assessment in Community Healthcare

by Chiara Barchielli, Sara Jayousi, Riccardo Mari, Beatrice Albanesi, Marco Alaimo, Gianluca Galeotti, Paolo Zoppi and Lorenzo Mucchi

Sensors 2026, 26(10), 2948; https://doi.org/10.3390/s26102948 - 8 May 2026

Viewed by 299

Abstract

Accurate and reproducible chronic wound assessment remains challenging in community healthcare, where environmental variability and subjective visual evaluation may introduce substantial measurement errors. Although multi-sensor technologies, including RGB–D imaging, mobile Light Detection and Ranging (LiDAR), thermal infrared imaging, and hyperspectral sensing, as well as artificial intelligence (AI)-based analytics, have advanced considerably, real-world adoption remains limited because of workflow misalignment, insufficient interpretability, and regulatory complexity. This study presents NURSE-AI, a Nurse-by-Design methodological framework for evaluating and preparing multi-sensor, AI-enabled wound assessment systems for deployment in community healthcare. NURSE-AI is proposed as a pre-implementation methodological framework supported by a feasibility study based on a synthetic dataset; therefore, it is not a clinical validation study, and no patient data were used. The framework integrates: (i) a GDPR-compliant synthetic multimodal dataset including RGB, depth, thermal, and hyperspectral-proxy layers; (ii) workflow-embedded acquisition modeling tailored to Family and Community Nurses (FCNs); (iii) a Wound Bed Preparation (WBP)-aligned interpretability layer; and (iv) a governance-by-design checklist addressing interoperability, metadata traceability, and regulatory readiness under Regulation (EU) 2017/745. A mixed-method feasibility evaluation was conducted with community nurses within AUSL Toscana Centro (Italy). The System Usability Scale (SUS) yielded a mean score of 74.5 ± 6.2, indicating good usability. Synthetic multimodal evaluation demonstrated promising segmentation performance under controlled synthetic conditions, with Intersection over Union (IoU) values ranging from 0.87 to 0.93, and simulated Intraclass Correlation Coefficient (ICC) values ≥ 0.90 for wound area estimation. Agreement between AI-generated WBP mappings and nurse interpretation ranged from κ = 0.80 to κ = 0.84. The NURSE-AI framework proposes a structured and reproducible pathway connecting sensor innovation, AI interpretability, nursing workflow integration, and regulatory preparedness, thereby providing structured groundwork for future clinical validation and scalable deployment in community healthcare. Full article

(This article belongs to the Special Issue AI and Big Data Analytics for Medical E-Diagnosis)

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47 pages, 7226 KB

Open AccessArticle

Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes

by Mais Alkhateeb, Rawan AlSaad, Samir Brahim Belhaouari, Sarah Aziz, Arfan Ahmed, Hamda Ali, Dabia Al-Mohanadi, Kawsar Mohamud, Najla Al-Naimi, Arwa Alsaud, Hamad Al-Sharshani, Javaid I. Sheikh, Khaled Baagar and Alaa Abd-Alrazaq

Sensors 2026, 26(8), 2552; https://doi.org/10.3390/s26082552 - 21 Apr 2026

Viewed by 602

Abstract

Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time alerts, these are largely reactive or limited to short prediction horizons, offering insufficient warning under fasting-related behavioural and circadian disruption. This study aims to evaluate whether behaviour-aware, temporally enriched recurrent deep learning models, leveraging multimodal CGM and wearable-derived signals, can forecast hypoglycaemia one hour ahead during Ramadan and the post-fasting period. In an observational, free-living cohort study conducted in Qatar, 33 adults with T1D were monitored using CGM and a wrist-worn wearable during Ramadan 2023 and the subsequent month. Multimodal data were aggregated into hourly features and organised into rolling 36 h sequences. In addition to physiological signals, explicit temporal and circadian proxy features were engineered, including cyclic time encodings, day–night indicators, and Ramadan-specific behavioural windows (e.g., pre-iftar, iftar, post-iftar, and fasting phases). Recurrent models, including LSTM and BiLSTM architectures, were trained using patient-wise, leak-free splits, with focal loss applied to address class imbalance. Model performance was evaluated on a held-out, naturally imbalanced test set using ROC AUC, precision–recall AUC, recall, and probability calibration, alongside cross-phase evaluation between Ramadan and post-fasting periods. Following quality control, 1164 participant-days were retained, with hypoglycaemia accounting for approximately 4% of hourly observations. Temporal feature enrichment and the use of a 36 h lookback window improved both discrimination and calibration, with performance stabilizing beyond this horizon. On the imbalanced test set, the best-performing multimodal model achieved an ROC AUC of 0.867 and a precision–recall AUC of 0.341, identifying 77% of next-hour hypoglycaemic events at a sensitivity-focused operating point (precision = 0.14). The selected BiLSTM model demonstrated good probability calibration (Brier score ≈ 0.03). Models trained using wearable-derived inputs alone achieved comparable discrimination and, in some configurations, higher precision–recall AUC than CGM-only baselines. Notably, models trained on the original imbalanced data outperformed resampled variants, suggesting that temporal and behavioural features provided sufficient discriminatory signal without requiring aggressive class balancing. Cross-phase evaluation indicated robust generalisation, particularly for the BiLSTM model. Overall, behaviour-aware, temporally enriched multimodal models can provide calibrated, hour-ahead hypoglycaemia risk estimates during Ramadan fasting in adults with T1D, enabling proactive intervention beyond reactive CGM alerts. Explicit modelling of circadian and behavioural dynamics enhances predictive performance under real-world class imbalance. Furthermore, integrating wearable-derived behavioural and physiological signals adds predictive value beyond CGM alone, supporting robustness across varying levels of contextual data availability. External validation and prospective clinical evaluation are required prior to deployment. Full article

(This article belongs to the Special Issue AI and Big Data Analytics for Medical E-Diagnosis)

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18 pages, 1092 KB

Open AccessArticle

Sparse Temporal AutoEncoder for ECG Anomaly Detection

by Radia Daci, Abdelmalik Taleb-Ahmed, Luigi Patrono and Cosimo Distante

Sensors 2026, 26(5), 1589; https://doi.org/10.3390/s26051589 - 3 Mar 2026

Viewed by 677

Abstract

Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing various cardiac conditions; however, accurately distinguishing between normal and abnormal ECG signals remains challenging due to high inter-individual variability and the inherent complexity of ECG waveforms. In this study, We propose a novel Sparse Temporal Autoencoder (STAE) for unsupervised ECG anomaly detection that leverages Temporal Convolutional Networks (TCNs) to extract hierarchical features from both time-domain and frequency-domain representations of ECG signals. Unlike traditional approaches requiring annotated abnormal samples, the proposed model is trained exclusively on normal ECG data, making it well-suited for real-world deployment. A STAE integrates a masked signal reconstruction strategy and a hybrid sparse attention mechanism combining sparse block and sparse strided attention to capture critical temporal and spectral patterns efficiently. The proposed method is evaluated on the PTB-XL dataset, where it achieves the highest ROC-AUC of 0.872 among compared unsupervised methods while maintaining a low inference time of 0.009 s, demonstrating that STAE achieves state-of-the-art performance in ECG anomaly detection, highlighting its potential as a powerful tool for automated and intelligent ECG analysis. Full article

(This article belongs to the Special Issue AI and Big Data Analytics for Medical E-Diagnosis)

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24 pages, 3287 KB

Open AccessArticle

Neonatal Seizure Detection Based on Spatiotemporal Feature Decoupling and Domain-Adversarial Learning

by Tiannuo Xu and Wei Zheng

Sensors 2026, 26(3), 938; https://doi.org/10.3390/s26030938 - 1 Feb 2026

Viewed by 578

Abstract

Neonatal seizures are a critical early indicator of neurological injury, yet effective automated detection is challenged by significant inter-subject variability in electroencephalogram (EEG) signals. To address this generalization gap, this study introduces the Domain-Adversarial Spatiotemporal Network (DA-STNet) for robust cross-subject seizure detection. Utilizing Short-Time Fourier Transform (STFT) spectrograms, the architecture employs a hierarchical backbone comprising a Channel-Independent CNN (CI-CNN) for local texture extraction, a Spatial Bidirectional Long Short-Term Memory (Bi-LSTM) for modeling topological dependencies, and Attention Pooling to dynamically prioritize pathological channels while suppressing noise. Crucially, a Gradient Reversal Layer (GRL) is integrated to enforce domain-adversarial training, decoupling pathological features from subject-specific identity to ensure domain invariance. Under rigorous 5-fold cross-validation, the model achieves State-of-the-Art performance with an average Area Under the Curve (AUC) of 0.9998 and an F1-score of 0.9952. Data scaling experiments further reveal that optimal generalization is attainable using only 80% of source data, highlighting the model’s superior data efficiency. These findings demonstrate the proposed method’s capability to reduce reliance on extensive clinical annotations while maintaining high diagnostic precision in complex clinical scenarios. Full article

(This article belongs to the Special Issue AI and Big Data Analytics for Medical E-Diagnosis)

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40 pages, 3646 KB

Open AccessArticle

Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images

by Saad Islam, Ravinesh C. Deo, Prabal Datta Barua, Jeffrey Soar and U. Rajendra Acharya

Sensors 2025, 25(14), 4337; https://doi.org/10.3390/s25144337 - 11 Jul 2025

Cited by 10 | Viewed by 5551

Abstract

Glaucoma is a leading cause of irreversible blindness worldwide, yet early detection can prevent vision loss. This paper proposes a novel deep learning approach that combines two ophthalmic imaging modalities, fundus photographs and optical coherence tomography scans, as paired images from the same eye of each patient for automated glaucoma detection. We develop separate convolutional neural network models for fundus and optical coherence tomography images and a fusion model that integrates features from both modalities for each eye. The models are trained and evaluated on a private clinical dataset (Bangladesh Eye Hospital and Institute Ltd.) consisting of 216 healthy eye images (108 fundus, 108 optical coherence tomography) from 108 patients and 200 glaucomatous eye images (100 fundus, 100 optical coherence tomography) from 100 patients. Our methodology includes image preprocessing pipelines for each modality, custom convolutional neural network/ResNet-based architectures for single-modality analysis, and a two-branch fusion network combining fundus and optical coherence tomography feature representations. We report the performance (accuracy, sensitivity, specificity, and area under curve) of the fundus-only, optical coherence tomography-only, and fusion models. In addition to a fixed test set evaluation, we perform five-fold cross-validation, confirming the robustness and consistency of the fusion model across multiple data partitions. On our fixed test set, the fundus-only model achieves 86% accuracy (AUC 0.89) and the optical coherence tomography-only model, 84% accuracy (AUC 0.87). Our fused model reaches 92% accuracy (AUC 0.95), an absolute improvement of 6 percentage points and 8 percentage points over the fundus and OCT baselines, respectively. McNemar’s test on pooled five-fold validation predictions (b = 3, c = 18) yields

χ^{2} = 10.7

(p = 0.001), and on optical coherence tomography-only vs. fused (b_o = 5, c_o = 20)

χ_{o}^{2} = 9.0

(p = 0.003), confirming that the fusion gains are significant. Five-fold cross-validation further confirms these improvements (mean AUC

0.952 \pm 0.011

. We also compare our results with the existing literature and discuss the clinical significance, limitations, and future work. To the best of our knowledge, this is the first time a novel deep learning model has been used on a fusion of paired fundus and optical coherence tomography images of the same patient for the detection of glaucoma. Full article

(This article belongs to the Special Issue AI and Big Data Analytics for Medical E-Diagnosis)

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