The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 13746

Special Issue Editors


E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Engineering Faculty, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; mechanical design; applied mechanics; theoretical kinematics

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tecnológico Nacional de México en Celaya, Celaya 38010, México
Interests: robotics; biomechanics; control systems

E-Mail Website
Guest Editor
Faculty of Engineering, Autonomous University of Queretaro, Cerro de las Campanas S/N, Santiago de Queretaro 76010, Queretaro, Mexico
Interests: image-based diagnosis; artificial intelligence; medical robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical technology is one of the most critical and revolutionary areas today. The integration of artificial intelligence in health systems has allowed the development of innovative technologies to improve the diagnosis and treatment of diseases and the quality of life of patients. In this context, artificial intelligence has allowed the creation of embedded systems that can monitor and control the health status of patients, which has allowed more personalized and efficient care. In addition, these technologies have allowed the development of new automatic diagnostic techniques that have revolutionized the diagnosis of diseases.

This Special Issue aims to show the innovators in the use of artificial intelligence as a main topic for solving problems in biomedical technology through the development of technologies with embedded systems for health and quality of life.

The topics of interest for this Special Issue include but are not limited to:

Artificial intelligence techniques focused on biomedical engineering issues:

  • Machine learning applied to medicine;
  • Deep learning for disease diagnosis;
  • Optimization of autonomous systems by artificial intelligence in medical care;
  • Metaheuristic algorithms for the design of prostheses and medical devices;
  • Diffuse or neural techniques for the analysis of biomedical signals;
  • Mixed techniques for the development of intelligent systems in medical care.

Applications with integrated artificial intelligence:

  • Health monitoring and control;
  • Diagnosis and treatment of diseases;
  • Advanced medical imaging;
  • Prosthetics and intelligent medical devices;
  • Personalized medicine;
  • Digital health;
  • Analysis of massive medical data.

Integrating artificial intelligence into biomedical technology has opened up a world of possibilities to improve people's quality of life. 

Dr. Juvenal Rodriguez-Resendiz
Dr. Gerardo I. Pérez-Soto
Dr. Karla Anhel Camarillo-Gómez
Dr. Saul Tovar-Arriaga
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 11329 KiB  
Article
An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration
by Mohammad Asif Hasan, Fariha Haque, Saifur Rahman Sabuj, Hasan Sarker, Md. Omaer Faruq Goni, Fahmida Rahman and Md Mamunur Rashid
Technologies 2024, 12(4), 56; https://doi.org/10.3390/technologies12040056 - 21 Apr 2024
Viewed by 291
Abstract
To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes [...] Read more.
To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions. Full article
Show Figures

Figure 1

18 pages, 2472 KiB  
Article
Blood Pressure Measurement Device Accuracy Evaluation: Statistical Considerations with an Implementation in R
by Tanvi Chandel, Victor Miranda, Andrew Lowe and Tet Chuan Lee
Technologies 2024, 12(4), 44; https://doi.org/10.3390/technologies12040044 - 25 Mar 2024
Viewed by 681
Abstract
Inaccuracies from devices for non-invasive blood pressure measurements have been well reported with clinical consequences. International standards, such as ISO 81060-2 and the seminal AAMI/ANSI SP10, define protocols and acceptance criteria for these devices. Prior to applying these standards, a sample size of [...] Read more.
Inaccuracies from devices for non-invasive blood pressure measurements have been well reported with clinical consequences. International standards, such as ISO 81060-2 and the seminal AAMI/ANSI SP10, define protocols and acceptance criteria for these devices. Prior to applying these standards, a sample size of N >= 85 is mandatory, that is, the number of distinct subcjects used to calculate device inaccuracies. Often, it is not possible to gather such a large sample. Many studies apply these standards with a smaller sample. The objective of the paper is to introduce a methodology that broadens the method first developed by the AAMI Sphygmomanometer Committee for accepting a blood pressure measurement device. We study changes in the acceptance region for various sample sizes using the sampling distribution for proportions and introduce a methodology for estimating the exact probability of the acceptance of a device. This enables the comparison of the accuracies of existing device development techniques even if they were studied with a smaller sample size. The study is useful in assisting BP measurement device manufacturers. To assist clinicians, we present a newly developed “bpAcc” package in R to evaluate acceptance statistics for various sample sizes. Full article
Show Figures

Figure 1

14 pages, 2538 KiB  
Article
Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation
by Pedro Moltó-Balado, Silvia Reverté-Villarroya, Victor Alonso-Barberán, Cinta Monclús-Arasa, Maria Teresa Balado-Albiol, Josep Clua-Queralt and Josep-Lluis Clua-Espuny
Technologies 2024, 12(2), 13; https://doi.org/10.3390/technologies12020013 - 23 Jan 2024
Viewed by 1848
Abstract
The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence [...] Read more.
The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA2DS2-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 ± 1.31 (p < 0.001), CHA2DS2-VASc score of 4.62 ± 1.02 (p < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF. Full article
Show Figures

Figure 1

20 pages, 747 KiB  
Article
Fuzzy Logic System for Classifying Multiple Sclerosis Patients as High, Medium, or Low Responders to Interferon-Beta
by Edgar Rafael Ponce de Leon-Sanchez, Jorge Domingo Mendiola-Santibañez, Omar Arturo Dominguez-Ramirez, Ana Marcela Herrera-Navarro, Alberto Vazquez-Cervantes, Hugo Jimenez-Hernandez and Horacio Senties-Madrid
Technologies 2023, 11(4), 109; https://doi.org/10.3390/technologies11040109 - 09 Aug 2023
Viewed by 1624
Abstract
Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on [...] Read more.
Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on the opinion of a neurology expert, to classify relapsing–remitting multiple sclerosis patients as high, medium, or low responders to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated with interferon-beta responses is proposed, for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results showed that the fuzzy system presented 100% efficiency, compared to an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model was evaluated, and 0.8 testing accuracy was achieved. Hence, a pipeline model, including data standardization, data compression, and a learning algorithm, could be a useful tool for getting reliable predictions about responses to interferon-beta. Full article
Show Figures

Figure 1

14 pages, 6253 KiB  
Article
Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET
by Rafael Ortiz-Feregrino, Saul Tovar-Arriaga, Jesus Carlos Pedraza-Ortega and Juvenal Rodriguez-Resendiz
Technologies 2023, 11(4), 97; https://doi.org/10.3390/technologies11040097 - 13 Jul 2023
Viewed by 1565
Abstract
Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has [...] Read more.
Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has been the traditional choice for image analysis tasks. However, the emergence of visual transformers with their unique attention mechanism has proved to be a game-changer. However, visual transformers require a large amount of data and computational power, making them unsuitable for tasks with limited data and resources. To deal with this constraint, we adapted the attention module of visual transformers and integrated it into a CNN-based UNET network, achieving superior performance compared to other models. The model achieved a 0.89 recall, 0.98 AUC, 0.97 accuracy, and 0.97 sensitivity on various datasets, including HRF, Drive, LES-AV, CHASE-DB1, Aria-A, Aria-D, Aria-C, IOSTAR, STARE and DRGAHIS. Moreover, the model can recognize blood vessels accurately, regardless of camera type or the original image resolution, ensuring that it generalizes well. This breakthrough in retinal vein segmentation could improve the early diagnosis of several health conditions. Full article
Show Figures

Figure 1

22 pages, 597 KiB  
Article
Optimizing EMG Classification through Metaheuristic Algorithms
by Marcos Aviles, Juvenal Rodríguez-Reséndiz and Danjela Ibrahimi
Technologies 2023, 11(4), 87; https://doi.org/10.3390/technologies11040087 - 02 Jul 2023
Cited by 11 | Viewed by 1655
Abstract
This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, [...] Read more.
This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications. Full article
Show Figures

Figure 1

18 pages, 3636 KiB  
Article
Radiation Dose Tracking in Computed Tomography Using Data Visualization
by Reem Alotaibi and Felwa Abukhodair
Technologies 2023, 11(3), 74; https://doi.org/10.3390/technologies11030074 - 10 Jun 2023
Viewed by 1726
Abstract
Radiation dose tracking is becoming very important due to the popularity of computerized tomography (CT) scans. One of the challenges of radiation dose tracking is that there are several variables that affect the dose from the patient side, machine side, and procedures side. [...] Read more.
Radiation dose tracking is becoming very important due to the popularity of computerized tomography (CT) scans. One of the challenges of radiation dose tracking is that there are several variables that affect the dose from the patient side, machine side, and procedures side. Although some tracking software programs exists, they are based on static analysis and cause integration errors due to the heterogeneity of Hospital Information Systems (HISs) and prevent users from obtaining accurate answers to their questions. In this paper, a visual analytic approach is utilized to track radiation dose data from computed tomography (CT) through the use of Tableau data visualization software. The web solution is evaluated in real-life scenarios by domain experts. The results show that the visual analytics approach improves the tracking process, as users completed the tasks with a 100% success rate. The process increased user satisfaction and also provided invaluable insight into the analytical process. Full article
Show Figures

Figure 1

Review

Jump to: Research

11 pages, 645 KiB  
Review
Level of Technological Maturity of Telemonitoring Systems Focused on Patients with Chronic Kidney Disease Undergoing Peritoneal Dialysis Treatment: A Systematic Literature Review
by Alejandro Villanueva Cerón, Eduardo López Domínguez, Saúl Domínguez Isidro, María Auxilio Medina Nieto, Jorge De La Calleja and Saúl Eduardo Pomares Hernández
Technologies 2023, 11(5), 129; https://doi.org/10.3390/technologies11050129 - 18 Sep 2023
Viewed by 1536
Abstract
In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The [...] Read more.
In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The TRL scale goes from 1 to 9, with 1 being the lowest level of readiness and 9 being the highest. This paper analyzes works that propose telemonitoring systems focused on patients with CKD undergoing PD treatment to determine their TRL. We also analyzed the requirements and parameters that the systems of the selected works provide to the users to perform telemonitoring of the patient’s treatment undergoing PD. Fourteen works were relevant to the present study. Of these works, eight were classified within TRL 9, two were categorized within TRL 7, three were identified within TRL 6, and one within TRL 4. The works reported with the highest TRL partially cover the requirements for appropriate telemonitoring of patients based on the specialized literature; in addition, those works are focused on the treatment of patients in the automated peritoneal dialysis (APD) modality, which limits the care of patients undergoing the continuous ambulatory peritoneal dialysis (CAPD) modality. Full article
Show Figures

Figure 1

18 pages, 753 KiB  
Review
Digital Technologies to Provide Humanization in the Education of the Healthcare Workforce: A Systematic Review
by María Gonzalez-Moreno, Carlos Monfort-Vinuesa, Antonio Piñas-Mesa and Esther Rincon
Technologies 2023, 11(4), 88; https://doi.org/10.3390/technologies11040088 - 05 Jul 2023
Viewed by 1726
Abstract
Objectives: The need to incentivize the humanization of healthcare providers coincides with the development of a more technological approach to medicine, which gives rise to depersonalization when treating patients. Currently, there is a culture of humanization that reflects the awareness of health professionals, [...] Read more.
Objectives: The need to incentivize the humanization of healthcare providers coincides with the development of a more technological approach to medicine, which gives rise to depersonalization when treating patients. Currently, there is a culture of humanization that reflects the awareness of health professionals, patients, and policy makers, although it is unknown if there are university curricula incorporating specific skills in humanization, or what these may include. Therefore, the objectives of this study are as follows: (1) to identify what type of education in humanization is provided to university students of Health Sciences using digital technologies; and (2) determine the strengths and weaknesses of this education. The authors propose a curriculum focusing on undergraduate students to strengthen the humanization skills of future health professionals, including digital health strategies. Methods: A systematic review, based on the scientific literature published in EBSCO, Ovid, PubMed, Scopus, and Web of Science, over the last decade (2012–2022), was carried out in November 2022. The keywords used were “humanization of care” and “humanization of healthcare” combined both with and without “students”. Results: A total of 475 articles were retrieved, of which 6 met the inclusion criteria and were subsequently analyzed, involving a total of 295 students. Three of them (50%) were qualitative studies, while the other three (50%) involved mixed methods. Only one of the studies (16.7%) included digital health strategies to train humanization. Meanwhile, another study (16.7%) measured the level of humanization after training. Conclusions: There is a clear lack of empirically tested university curricula that combine education in humanization and digital technology for future health professionals. Greater focus on the training of future health professionals is needed, in order to guarantee that they begin their professional careers with the precept of medical humanities as a basis. Full article
Show Figures

Figure 1

Back to TopTop