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Artificial Intelligence in Medicine and Rehabilitation: Technologies and Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 4973

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Dr. Tomasz Kocejko

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Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Interests: biomedical engineering; human system interaction; medical devices

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a transformative force in the fields of medicine and rehabilitation, offering unprecedented opportunities to enhance diagnosis, treatment, and patient care. This Special Issue aims to explore the latest advancements and applications of AI in various domains within medicine and rehabilitation.

In recent years, the rapid development of medical technologies has facilitated the collection of vast amounts of data, offering unprecedented opportunities for researchers and clinicians to leverage artificial intelligence (AI) algorithms. These advancements have ushered in a new era of medical care, where AI-driven image and signal processing techniques are revolutionizing diagnosis, treatment, and patient management. Moreover, the proliferation of wearable devices, intelligent systems, and personalized robotics has further expanded the scope of AI applications in healthcare.

This Special Issue seeks to explore the synergistic intersection of AI, medical technologies, and rehabilitation, aiming to showcase the latest developments and innovations in these interconnected domains. Researchers, clinicians, and experts are welcome to contribute original research articles, reviews, and case studies that demonstrate the impact of AI in medicine and rehabilitation

Dr. Tomasz Kocejko
Guest Editor

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Keywords

artificial intelligence
machine learning
deep learning
computer vision
image processing
image analysis
signal processing
signal analysis
computer-aided diagnosis
human-computer interface and interaction
human-machine interface and interaction
personalized robotics
translational medicine
intelligent systems

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Published Papers (4 papers)

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Research

13 pages, 282 KiB

Open AccessArticle

A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection

by Tomasz Kocejko

Appl. Sci. 2024, 14(23), 11187; https://doi.org/10.3390/app142311187 - 30 Nov 2024

Viewed by 830

Abstract

This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. The primary objective is to explore an enhanced method for estimating FH probability, surpassing the currently recommended Dutch Lipid Clinic Network (DLCN) Score. The models were trained using the largest Polish cohort of patients enrolled in an FH clinic, all of whom underwent genetic testing for FH-associated mutations. The initial dataset comprised over 100 parameters per patient, which was reduced to 48 clinically accessible features to ensure applicability in routine outpatient settings. To preserve balance, the data were stratified according to DLCN score ranges (<0–2>, <3–5>, <6–8>, and ≥9), representing varying levels of FH likelihood. The dataset was then split into training and test sets with an 80/20 ratio. Machine-learning models were trained, with hyperparameters optimized via grid search. The accuracy of the DLCN score in predicting FH was first evaluated by examining the proportion of patients with positive DNA tests relative to those with a DLCN score of 6 and above, the threshold for genetic testing. The DLCN score demonstrated an accuracy of approximately 40%. In contrast, the CatBoost model and its ensembles achieved over 80% accuracy. While the DLCN score remains a clinically valuable tool, its diagnostic accuracy is limited. The findings indicate that the ML models offer a substantial improvement in the precision of FH diagnosis, demonstrating its potential to enhance clinical decision making in identifying patients with FH. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medicine and Rehabilitation: Technologies and Applications)

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22 pages, 748 KiB

Open AccessArticle

Feature Selection with Small Data Sets: Identifying Feature Importance for Predictive Classification of Return-to-Work Date after Knee Arthroplasty

by Harald H. Rietdijk, Daniël O. Strijbos, Patricia Conde-Cespedes, Talko B. Dijkhuis, Hilbrand K. E. Oldenhuis and Maria Trocan

Appl. Sci. 2024, 14(20), 9389; https://doi.org/10.3390/app14209389 - 15 Oct 2024

Viewed by 895

Abstract

In recent decades, the number of cases of knee arthroplasty among people of working age has increased. The integrated clinical pathway ‘back at work after surgery’ is an initiative to reduce the possible cost of sick leave. The evaluation of this pathway, like many clinical studies, faces the challenge of small data sets with a relatively high number of features. In this study, we investigate the possibility of identifying features that are important in determining the duration of rehabilitation, expressed in the return-to-work period, by using feature selection tools. Several models are used to classify the patient’s data into two classes, and the results are evaluated based on the accuracy and the quality of the ordering of the features, for which we introduce a ranking score. A selection of estimators are used in an optimization step, reorganizing the feature ranking. The results show that for some models, the proposed optimization results in a better ordering of the features. The ordering of the features is evaluated visually and identified by the ranking score. Furthermore, for all models, higher accuracy, with a maximum of 91%, is achieved by applying the optimization process. The features that are identified as relevant for the duration of the return-to-work period are discussed and provide input for further research. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medicine and Rehabilitation: Technologies and Applications)

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14 pages, 1491 KiB

Open AccessArticle

Development of a Deep Learning Model for Predicting Speech Audiometry Using Pure-Tone Audiometry Data

by Jae Sung Shin, Jun Ma, Seong Jun Choi, Sungyeup Kim and Min Hong

Appl. Sci. 2024, 14(20), 9379; https://doi.org/10.3390/app14209379 - 15 Oct 2024

Cited by 1 | Viewed by 1429

Abstract

Speech audiometry is a vital tool in assessing an individual’s ability to perceive and comprehend speech, traditionally requiring specialized testing that can be time-consuming and resource -intensive. This paper approaches a novel use of deep learning to predict speech audiometry using pure-tone audiometry (PTA) data. By utilizing PTA data, which measure hearing sensitivity at specific frequencies, we aim to develop a model that can bypass the need for direct speech testing. This study investigates two neural network architectures: a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1D-CNN). These models are trained to predict key speech audiometry outcomes, including speech recognition thresholds and speech discrimination scores. To evaluate the effectiveness of these models, we employed two key performance metrics: the coefficient of determination (R²) and mean absolute error (MAE). The MLP model demonstrated predictive solid power with an R² score of 88.79% and an average MAE of 7.26, while the 1D-CNN model achieved a slightly higher level of accuracy with an MAE score of 88.35% and an MAE of 6.90. The superior performance of the 1D-CNN model suggests that it captures relevant features from PTA data more effectively than the MLP. These results show that both models hold promise for predicting speech audiometry, potentially simplifying the audiological evaluation process. This approach is applied in clinical settings for hearing loss assessment, the selection of hearing aids, and the development of personalized auditory rehabilitation programs. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medicine and Rehabilitation: Technologies and Applications)

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21 pages, 5770 KiB

Open AccessArticle

Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain

by Pratixita Bhattacharjee and Piotr Augustyniak

Appl. Sci. 2024, 14(19), 8772; https://doi.org/10.3390/app14198772 - 28 Sep 2024

Cited by 1 | Viewed by 1249

Abstract

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medicine and Rehabilitation: Technologies and Applications)

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