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Bioengineering
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New Sight of Intelligent Algorithm Model and Medical Device in...
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New Sight of Intelligent Algorithm Model and Medical Device in Bioengineering: Updates and Direction

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2777

Special Issue Editors

Dr. Longjun Liu

E-Mail Website
Guest Editor
Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an 710049, China
Interests: biomedicine; pattern recognition; intelligent systems; robotics, big data processing; image/language processing and recognition; algorithm model
Dr. Luca Mesin

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Polytechnic University of Turin, Turin, Italy
Interests: biomedical signal and image processing and classification; biophysical modelling; clinical studies; mathematical biology and physiology; noninvasive monitoring of the volemic status of patients; nonlinear biomedical signal processing; optimal non-uniform down-sampling; systems for human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The journal Bioengineering would like to compile a collection of papers to report on the advancements in the field of intelligent algorithm models and medical devices in bioengineering.

With the rapid development of technology, the term intelligent algorithm model combined with biomedicine has emerged, which includes pattern recognition, intelligent systems, robotics, big data processing, image/language processing and recognition, etc. It is an interdisciplinary field with great development potential.

The aim of this Special Issue, entitled “New Sight of Intelligent Algorithm Model and Medical Device in Bioengineering: Updates and Direction”, is to make relevant work known to our colleagues in the field. To achieve this, the Special Issue, edited by Dr. Liulongjun and Dr. Luca Mesin, invites scientists to submit research articles, review articles, and short communications focused on this topic.

We look forward to your valuable contributions to make this Special Issue a reference resource for future researchers in the field of intelligent algorithm model and medical device in bioengineering.

Dr. Longjun Liu
Dr. Luca Mesin
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. Bioengineering 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 2700 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 (2 papers)

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Research

31 pages, 13580 KiB  
Article
Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study
by Ayelet Goldstein, Yuval Shahar, Michal Weisman Raymond, Hagit Peleg, Eldad Ben-Chetrit, Arie Ben-Yehuda, Erez Shalom, Chen Goldstein, Shmuel Shay Shiloh and Galit Almoznino
Bioengineering 2024, 11(1), 97; https://doi.org/10.3390/bioengineering11010097 - 19 Jan 2024
Viewed by 1342
Abstract
This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of [...] Read more.
This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski–Harabasz index (CHI), and Davies–Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients. Full article
(This article belongs to the Special Issue New Sight of Intelligent Algorithm Model and Medical Device in Bioengineering: Updates and Direction)
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15 pages, 3403 KiB  
Article
Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization
by Mingzheng Jia, Liang Li, Baolin Xiong, Le Feng, Wenbo Cheng and Wen-Fei Dong
Bioengineering 2023, 10(9), 1079; https://doi.org/10.3390/bioengineering10091079 - 12 Sep 2023
Cited by 1 | Viewed by 1092
Abstract
Quadrupole mass spectrometers (QMS) are widely used for clinical diagnosis and chemical analysis. To obtain the best experimental results, mass spectrometers must be calibrated to an ideal setting before use. However, tuning the current QMS is challenging. Traditional tuning techniques possess low automation [...] Read more.
Quadrupole mass spectrometers (QMS) are widely used for clinical diagnosis and chemical analysis. To obtain the best experimental results, mass spectrometers must be calibrated to an ideal setting before use. However, tuning the current QMS is challenging. Traditional tuning techniques possess low automation levels and rely primarily on skilled engineers. Therefore, in this study, we propose an innovative auto-tuning algorithm for QMS based on the improved particle swarm optimization (PSO) algorithm to automatically find the optimal solution of QMS parameters and make the QMS reach the optimal state. The improved PSO algorithm is combined with simulated annealing, multiple inertia weights, dynamic boundaries, and other methods to prevent the traditional PSO algorithm from the issue of a local optimal solution and premature convergence. According to the characteristics of the mass spectrum peaks, a termination function is proposed to simplify the termination conditions of the PSO algorithm and further improve the automation level of the mass spectrometer. The results of auto-calibration testing of resolution and mass axis show that both resolution and mass axis calibration could effectively meet the requirements of mass spectrometry experiments. By the experiment of auto-optimization testing of lens and ion source parameters, these parameters were all in the vicinity of the optimal solution, which achieved the expected performance. Through numerous experiments, the reproducibility of the algorithm was established as meeting the auto-tuning function of the QMS. The proposed method can automatically tune the mass spectrometer from its non-optimal condition to the optimal one, which can effectively reduce the tuning difficulty of QMS. Full article
(This article belongs to the Special Issue New Sight of Intelligent Algorithm Model and Medical Device in Bioengineering: Updates and Direction)
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