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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: 15 June 2025 | Viewed by 6662

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Special Issue Editors

Dr. Xiaoli Li

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Guest Editor

Machine Intellection Department, Institute for Infocomm Research, Singapore 138632, Singapore
Interests: artificial intelligence; machine learning; medical sensors
Special Issues, Collections and Topics in MDPI journals

Dr. Emadeldeen Eldele

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Guest Editor

Scientist, Centre of Frontier AI Research (CFAR), A*STAR, Singapore
Interests: deep learning; self- and semi-supervised learning; domain adaptation; time-series data; biomedical sensory data

Special Issue Information

Dear Colleagues,

In the domain of contemporary medical research, a multitude of biomedical sensors, including ultrasound, chemical analysis, biomaterial, fluid flow, and MRI sensors, have emerged. These sensors are evolving in tandem with cutting-edge time-series data analytics and signal processing techniques. Simultaneously, AI has garnered widespread recognition for its prowess in processing sensor data. Its application range includes disease diagnostics, prognostics, and neurotechnology management for rehabilitation, precision health, treatment strategies, and patient care.

The primary objective of this Special Issue is to present a range of diverse yet complementary contributions that showcase the latest advancements and applications of AI in harnessing the predictive potential of medical sensor data. Moreover, this Special Issue will explore avenues for enhancing the interpretability and explainability of AI-generated insights derived from medical sensor data to ensure that these advanced technologies not only deliver precise predictions but also offer comprehensible and valuable insights to medical professionals and patients alike.

Dr. Xiaoli Li
Dr. Emadeldeen Eldele
Guest Editors

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Published Papers (4 papers)

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Research

22 pages, 9117 KiB

Open AccessArticle

Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data

by Ghada Ben Othman, Amani R. Ynineb, Erhan Yumuk, Hamed Farbakhsh, Cristina Muresan, Isabela Roxana Birs, Alexandra De Raeve, Cosmin Copot, Clara M. Ionescu and Dana Copot

Sensors 2024, 24(17), 5544; https://doi.org/10.3390/s24175544 - 27 Aug 2024

Cited by 2 | Viewed by 1326

Abstract

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient

η

using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast

η

values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter

η

, achieving an

R^{2}

of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast

η

with an

R^{2}

of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Sensing)

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18 pages, 2451 KiB

Open AccessArticle

HRP-OG: Online Learning with Generative Feature Replay for Hypertension Risk Prediction in a Nonstationary Environment

by Shaofu Lin, Haokang Yan, Shiwei Zhou, Ziqian Qiao and Jianhui Chen

Sensors 2024, 24(15), 5033; https://doi.org/10.3390/s24155033 - 3 Aug 2024

Viewed by 1537

Abstract

Hypertension is a major risk factor for many serious diseases. With the aging population and lifestyle changes, the incidence of hypertension continues to rise, imposing a significant medical cost burden on patients and severely affecting their quality of life. Early intervention can greatly reduce the prevalence of hypertension. Research on hypertension early warning models based on electronic health records (EHRs) is an important and effective method for achieving early hypertension warning. However, limited by the scarcity and imbalance of multivisit records, and the nonstationary characteristics of hypertension features, it is difficult to predict the probability of hypertension prevalence in a patient effectively. Therefore, this study proposes an online hypertension monitoring model (HRP-OG) based on reinforcement learning and generative feature replay. It transforms the hypertension prediction problem into a sequential decision problem, achieving risk prediction of hypertension for patients using multivisit records. Sensors embedded in medical devices and wearables continuously capture real-time physiological data such as blood pressure, heart rate, and activity levels, which are integrated into the EHR. The fit between the samples generated by the generator and the real visit data is evaluated using maximum likelihood estimation, which can reduce the adversarial discrepancy between the feature space of hypertension and incoming incremental data, and the model is updated online based on real-time data using generative feature replay. The incorporation of sensor data ensures that the model adapts dynamically to changes in the condition of patients, facilitating timely interventions. In this study, the publicly available MIMIC-III data are used for validation, and the experimental results demonstrate that compared to existing advanced methods, HRP-OG can effectively improve the accuracy of hypertension risk prediction for few-shot multivisit record in nonstationary environments. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Sensing)

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23 pages, 27007 KiB

Open AccessArticle

An Intelligent Hand-Assisted Diagnosis System Based on Information Fusion

by Haonan Li and Yitong Zhou

Sensors 2024, 24(14), 4745; https://doi.org/10.3390/s24144745 - 22 Jul 2024

Cited by 2 | Viewed by 1243

Abstract

This research proposes an innovative, intelligent hand-assisted diagnostic system aiming to achieve a comprehensive assessment of hand function through information fusion technology. Based on the single-vision algorithm we designed, the system can perceive and analyze the morphology and motion posture of the patient’s hands in real time. This visual perception can provide an objective data foundation and capture the continuous changes in the patient’s hand movement, thereby providing more detailed information for the assessment and providing a scientific basis for subsequent treatment plans. By introducing medical knowledge graph technology, the system integrates and analyzes medical knowledge information and combines it with a voice question-answering system, allowing patients to communicate and obtain information effectively even with limited hand function. Voice question-answering, as a subjective and convenient interaction method, greatly improves the interactivity and communication efficiency between patients and the system. In conclusion, this system holds immense potential as a highly efficient and accurate hand-assisted assessment tool, delivering enhanced diagnostic services and rehabilitation support for patients. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Sensing)

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17 pages, 6823 KiB

Open AccessArticle

Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements

by Samira Al-Nasser, Siamak Noroozi, Adrian Harvey, Navid Aslani and Roya Haratian

Sensors 2024, 24(2), 585; https://doi.org/10.3390/s24020585 - 17 Jan 2024

Cited by 5 | Viewed by 1842

Abstract

Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer’s Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Sensing)

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