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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 March 2024) | Viewed by 14980

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Special Issue Editor

Prof. Dr. Josué Álvarez Borrego

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Guest Editor

Department of Optics, Ensenada Center for Scientific Research and Higher Education, Ensenada, C.P. 22860, Mexico
Interests: image processing; pattern recognition

Special Issue Information

Dear Colleagues,

The innovation and development of new technologies based on artificial intelligence and neural networks are applied in conjunction with other modern fields of medicine, generating a range of applications in health such as improvements in the prediction of diseases, support for specialists in decision-making, favoring the early diagnosis of specific pathologies, the development of non-invasive diagnostic tools, and cost savings in health clinics. Artificial intelligence (AI) is a tool with various applications allowing data analysis, image and voice recognition, analysis, and identification. Some of the areas that use AI include Philosophy, Mathematics, Economics, Physics, Optics, Electronics, Neurosciences, Control Theory, etc.

The set of machine learning techniques and algorithms is being applied in research including the medical field to detect diseases, analyze genetic data in bioinformatics, and analyze data science for health care. Other fields include cancer research and research of other diseases, education, and vehicle, aircraft, and drone manufacturing.

Machine learning techniques are used for their great predictive power and their ability to develop advanced algorithms to process large amounts of data quickly.

Prof. Dr. Josué Álvarez Borrego
Guest Editor

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Keywords

artificial neural networks
biomedical computing
image processing
medical diagnostic imaging
fourier spectral analysis
computer-aided diagnosis
CNN
densenet-201
convolution neural networks
computer science

Published Papers (7 papers)

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Research

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20 pages, 2506 KiB

Open AccessArticle

Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

by Esperanza Guerra-Rosas, Luis Felipe López-Ávila, Esbanyely Garza-Flores, Claudia Andrea Vidales-Basurto and Josué Álvarez-Borrego

Appl. Sci. 2023, 13(20), 11425; https://doi.org/10.3390/app132011425 - 18 Oct 2023

Viewed by 980

Abstract

This manuscript proposes the possibility of concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eight lesions were analyzed using some algorithms of artificial intelligence: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASCs). Eleven artificial intelligence models were applied so that eight skin lesions could be classified by analyzing the signatures of each lesion. The database was randomly divided into 80% and 20% for the training and test dataset images, respectively. The metrics that are reported are accuracy, sensitivity, specificity, and precision. Each process was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the averages and ± standard deviations were reported for each metric. Although all the results were very satisfactory, the highest average score for the eight lesions analyzed was obtained using the subspace k-NN model, where the test metrics were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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21 pages, 7610 KiB

Open AccessArticle

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

by Mohamed Talaat, Xiuhua Si and Jinxiang Xi

Appl. Sci. 2023, 13(18), 10270; https://doi.org/10.3390/app131810270 - 13 Sep 2023

Cited by 3 | Viewed by 1416

Abstract

This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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25 pages, 5390 KiB

Open AccessArticle

On the Interplay between Machine Learning, Population Pharmacokinetics, and Bioequivalence to Introduce Average Slope as a New Measure for Absorption Rate

by Vangelis D. Karalis

Appl. Sci. 2023, 13(4), 2257; https://doi.org/10.3390/app13042257 - 9 Feb 2023

Cited by 5 | Viewed by 1450

Abstract

The scientific basis for demonstrating bioequivalence between two drug products relies on the comparison of their extent and rate of absorption. For the absorption extent, the area under the C-t curve (AUCt) is used without a doubt. For absorption rate, the maximum observed plasma concentration (Cmax) is still suggested by the authorities, despite the numerous concerns. In this study, the concept of average slope (AS) is introduced as a metric to express the absorption rate of drugs. Principal component analysis and random forest models were applied to actual and simulated two × two crossover bioequivalence studies to show that AS expresses the appropriate properties for characterizing absorption rate. Several absorption kinetics (slow, typical, fast) and sampling schemes (sparse, typical, dense) were simulated. The two machine learning algorithms, applied to all these scenarios, proved the desired properties of AS while showing the non-desired performances of other metrics currently used or proposed in the literature. The estimation of AS does not require any assumptions, models, or transformations and is as simple as that of AUCt. A modified version of AS, termed “weighted AS”, is also introduced in order to place emphasis on early time points where the C-t profile describes more clearly the absorption process. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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15 pages, 1688 KiB

Open AccessArticle

Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning

by Dineo Mpanya, Turgay Celik, Eric Klug and Hopewell Ntsinjana

Appl. Sci. 2023, 13(3), 1509; https://doi.org/10.3390/app13031509 - 23 Jan 2023

Cited by 3 | Viewed by 1922

Abstract

Background: The diagnosis and therapy of heart failure are guided mainly by a single imaging parameter, the left ventricular ejection fraction (LVEF). Recent studies have reported on the value of machine learning in characterising the various phenotypes of heart failure patients. Therefore, this study aims to use unsupervised machine learning algorithms to phenotype heart failure patients into different clusters using multiple clinical parameters. Methods: Seven unsupervised machine learning clustering algorithms were used to cluster heart failure patients hospitalised with acute and chronic heart failure. Results: The agglomerative clustering algorithm identified three clusters with a silhouette score of 0.72. Cluster 1 (uraemic cluster) comprised 229 (36.0%) patients with a mean age of 56.2 ± 17.2 years and a serum urea of 14.5 ± 31.3 mmol/L. Cluster 2 (hypotensive cluster) comprised 117 (18.4%) patients with a minimum systolic and diastolic blood pressure of 91 and 60 mmHg, respectively. In cluster 3 (congestive cluster), patients predominantly had symptoms of fluid overload, and 93 (64.6%) patients had ascites. Among the 636 heart failure patients studied, the median LVEF was 32% (interquartile range: 25–45), and the rate of in-hospital all-cause mortality was 14.5%. Systolic and diastolic blood pressure, age, and the LVEF had the most substantial impact on discriminating between the three clusters. Conclusions: Clinicians without access to echocardiography could potentially rely on blood pressure measurements and age to risk stratify heart failure patients. However, larger prospective studies are mandatory for the validation of these clinical parameters. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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18 pages, 13820 KiB

Open AccessArticle

Depth Estimation for Egocentric Rehabilitation Monitoring Using Deep Learning Algorithms

by Yasaman Izadmehr, Héctor F. Satizábal, Kamiar Aminian and Andres Perez-Uribe

Appl. Sci. 2022, 12(13), 6578; https://doi.org/10.3390/app12136578 - 29 Jun 2022

Cited by 3 | Viewed by 1836

Abstract

Upper limb impairment is one of the most common problems for people with neurological disabilities, affecting their activity, quality of life (QOL), and independence. Objective assessment of upper limb performance is a promising way to help patients with neurological upper limb disorders. By using wearable sensors, such as an egocentric camera, it is possible to monitor and objectively assess patients’ actual performance in activities of daily life (ADLs). We analyzed the possibility of using Deep Learning models for depth estimation based on a single RGB image to allow the monitoring of patients with 2D (RGB) cameras. We conducted experiments placing objects at different distances from the camera and varying the lighting conditions to evaluate the performance of the depth estimation provided by two deep learning models (MiDaS & Alhashim). Finally, we integrated the best performing model for depth-estimation (MiDaS) with other Deep Learning models for hand (MediaPipe) and object detection (YOLO) and evaluated the system in a task of hand-object interaction. Our tests showed that our final system has a 78% performance in detecting interactions, while the reference performance using a 3D (depth) camera is 84%. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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13 pages, 996 KiB

Open AccessArticle

Semantic Segmentation of 12-Lead ECG Using 1D Residual U-Net with Squeeze-Excitation Blocks

by Konrad Duraj, Natalia Piaseczna, Paweł Kostka and Ewaryst Tkacz

Appl. Sci. 2022, 12(7), 3332; https://doi.org/10.3390/app12073332 - 25 Mar 2022

Cited by 10 | Viewed by 3670

Abstract

Analyzing biomedical data is a complex task that requires specialized knowledge. The development of knowledge and technology in the field of deep machine learning creates an opportunity to try and transfer human knowledge to the computer. In turn, this fact influences the development of systems for the automatic evaluation of the patient’s health based on data acquired from sensors. Electrocardiography (ECG) is a technique that enables visualizing the electrical activity of the heart in a noninvasive way, using electrodes placed on the surface of the skin. This signal carries a lot of information about the condition of heart muscle. The aim of this work is to create a system for semantic segmentation of the ECG signal. For this purpose, we used a database from Lobachevsky University available on Physionet, containing 200, 10-second, and 12-lead ECG signals with annotations, and applied one-dimensional U-Net with the addition of squeeze-excitation blocks. The created model achieved a set of parameters indicating high performance (for the test set: accuracy—0.95, AUC—0.99, specificity—0.95, sensitivity—0.99) in extracting characteristic parts of ECG signal such as P and T-waves and QRS complex, regardless of the lead. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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Review

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30 pages, 1773 KiB

Open AccessReview

Detection and Classification of COVID-19 by Radiological Imaging Modalities Using Deep Learning Techniques: A Literature Review

by Albatoul S. Althenayan, Shada A. AlSalamah, Sherin Aly, Thamer Nouh and Abdulrahman A. Mirza

Appl. Sci. 2022, 12(20), 10535; https://doi.org/10.3390/app122010535 - 19 Oct 2022

Cited by 6 | Viewed by 2095

Abstract

Coronavirus disease (COVID-19) is a viral pneumonia that originated in China and has rapidly spread around the world. Early diagnosis is important to provide effective and timely treatment. Thus, many studies have attempted to solve the COVID-19 classification problems of workload classification, disease detection, and differentiation from other types of pneumonia and healthy lungs using different radiological imaging modalities. To date, several researchers have investigated the problem of using deep learning methods to detect COVID-19, but there are still unsolved challenges in this field, which this review aims to identify. The existing research on the COVID-19 classification problem suffers from limitations due to the use of the binary or flat multiclass classification, and building classifiers based on only a few classes. Moreover, most prior studies have focused on a single feature modality and evaluated their systems using a small public dataset. These studies also show a reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays, as explained below. Accordingly, the aim of this review is to examine existing methods and frameworks in the literature that have been used to detect and classify COVID-19, as well as to identify research gaps and highlight the limitations from a critical perspective. The paper concludes with a list of recommendations, which are expected to assist future researchers in improving the diagnostic process for COVID-19 in particular. This should help to develop effective radiological diagnostic data for clinical applications and to open future directions in this area in general. Full article

(This article belongs to the Special Issue New Trends in Machine Learning for Biomedical Data Analysis)

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