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Special Issue "Systematic Design, Testing and Development of In Vitro Diagnostic Instruments"

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: 10 February 2024 | Viewed by 8337

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Special Issue Editors

Prof. Dr. Lei Wang

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Guest Editor

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Interests: medical laboratory technology based on machine vision and image processing; medical Internet of things and big data application; intelligent in vitro diagnostic instrument
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hai Huang
E-Mail Website
Guest Editor

School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
Interests: digital signal processing, information and network security, healthcare big data analysis technology

Special Issue Information

Dear Colleagues,

The design, testing and development of in vitro diagnostic instruments is a comprehensive and systematic project involving many disciplines. In recent years, with the rapid development of biomedical engineering, information technology, medical testing and other theories and technologies, the requirements for the intelligence, convenience and integration of in vitro diagnostic instruments are increasing. In order to meet the needs of design, testing and development technology and the product market of in vitro diagnostic instruments, relevant personnel in scientific and technological circles and industry have achieved a series of achievements in many fields.

This Special Issue on “Systematic Design, Testing and Development of in Vitro Diagnostic Instruments” aims to gather outstanding research and comprehensive coverage of all aspects related to the design, testing and development technologies of in vitro diagnostic instruments, covering a wide range of multi-domain methods to improve the intelligence and overall performance of in vitro diagnostic instruments. This Special Issue will bring together high-quality research articles on different aspects of systematic design, testing and development technologies of in vitro diagnostic instruments, including the current status and remaining challenges. Topics include, but are not limited to:

Systematic design theories and methods of in vitro diagnostic instruments;
Test standards, procedures and methods of in vitro diagnostic instruments;
Software and hardware integrated development methods of in vitro diagnostic instruments.

Prof. Dr. Lei Wang
Prof. Dr. Hai Huang
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. Processes 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 2400 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.

Keywords

in vitro diagnostic instrument
systematic design theory
test standards
test procedures
software development method
hardware integrated method
intelligent testing

Published Papers (7 papers)

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Research

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Open AccessArticle

Diagnosis of Osteoarthritis at an Early Stage via Infrared Spectroscopy Combined Chemometrics in Human Serum: A Pilot Study

Atiqah Ab Aziz

Veenesh Selvaratnam

Yasmin Fadzlin Binti Ahmad Fikri

Muhamad Shirwan Abdullah Sani

and

Tunku Kamarul

Processes 2023, 11(2), 404; https://doi.org/10.3390/pr11020404 - 29 Jan 2023

Cited by 1 | Viewed by 851

Abstract

Methods applied for early diagnosis of osteoarthritis (OA) are limited. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. The present study aimed to develop a rapid non-destructive detection method for early diagnosis of OA by evaluating infrared (IR) spectroscopy combined chemometrics. Our cohort consisted of (a) 15 patients with osteoarthritis (OA) and (b) 10 without clinical signs of the disease and they were used as controls. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was used to investigate serum samples (50 µL) collected from these patients. A supervised classification algorithm namely discriminant analysis (DA) was applied to evaluate the diagnostic accuracy spectral processing and chemometrics analysis allowed for detecting spectral biomarkers that discriminated the two cohorts. About 250 infrared spectra were statistically important for separating the groups. Peaks at 1000 cm⁻¹ in OA serum were associated mainly with C–O stretching vibration associated with the changes in the proteoglycan contents previously reported in OA. A good overall classification accuracy of 74.47% was obtained from the DA model. Our findings indicated that this discriminating model, which incorporated the ATR-FTIR spectrum, could provide a rapid and cost-effective blood test, thus facilitating the early diagnosis of human OA. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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Open AccessArticle

Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction

and

Processes 2022, 10(11), 2348; https://doi.org/10.3390/pr10112348 - 10 Nov 2022

Cited by 2 | Viewed by 810

Abstract

Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of multiple myocardial infarctions using a bidirectional long short-term memory (BiLSTM) neural network classification was proposed. This algorithm was based on morphological feature extraction, which can greatly improve the diagnostic efficiency of doctors for different kinds of myocardial infarction diseases. The algorithm includes noise reduction and beat segmentation of electrocardiogram (ECG) signals from the Physikalisch-Technische Bundesanstalt (PTB) database. According to the medical diagnosis guide, the distance feature of the whole waveform and the amplitude feature of the branch lead waveform are extracted. According to the extracted features, the long short-term memory network (LSTM) and the BiLSTM neural networks are built to classify and recognize heartbeats. The experimental results show that the accuracy of the morphological feature + BiLSTM algorithm in MI detection is 99.4%. At the same time, among the six common myocardial infarction diseases, the location and recognition rate of the culprit vessel is high. The sensitivity, specificity, PPV, NPV, and F1 score parameters all reach more than 98.4%, and the kappa coefficient also reaches 0.983, while the overall accuracy reaches 98.6%. The accuracy of this algorithm is improved by at least 1% compared with that of other existing algorithms. Thus, this study exhibits a very important clinical application value. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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Open AccessArticle

A Hybrid Recommendation Approach for Medical Services That Incorporates Knowledge Graphs

and

Processes 2022, 10(8), 1500; https://doi.org/10.3390/pr10081500 - 29 Jul 2022

Viewed by 670

Abstract

At present, there are a large number of growing medical applications in the application market. It is difficult for users to find satisfactory medical services conveniently and efficiently. The classical collaborative filtering algorithm has some problems, such as cold start, unsatisfactory recommendation results, and so on. This paper proposes a hybrid medical service recommendation approach based on knowledge graph to solve the above problems. This approach introduces the open knowledge graph and establishes the semantic link relationship between the mobile application and the knowledge graph entity. It not only enhances the semantic feature of single application for improving the accuracy of recommendation results, but also realizes the in-depth analysis of the semantic relationship among multiple application entities in the knowledge graph through the TransHR model which can alleviate the cold start problem. Then we design a hybrid recommendation algorithm based on multi-dimensional similarity fusion. This algorithm uses the entropy method to organically integrate the calculation results of multi-dimensional semantic similarity, such as feature vector similarity, entity relation similarity, and user rating similarity. It is convenient and efficient to recommend satisfactory medical application services to target users. Finally, we test and analyze the accuracy and effectiveness of our proposed approach by experiment. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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Open AccessArticle

The Trace Gas Monitoring Method Based on Diode Laser Wavelength-Modulation Spectroscopy Technology for the Detection of Clinical Blood Infection

and

Processes 2022, 10(8), 1450; https://doi.org/10.3390/pr10081450 - 25 Jul 2022

Viewed by 841

Abstract

It is important to monitor and evaluate the growth of microorganisms in order to accurately judge the situation of blood microbial infection. In this paper, diode laser wavelength modulation spectroscopy (DLWMS) technology is used to design a set of low-cost, high sensitivity, fast dynamic responses and a non-invasive trace gas measurement system, which can quickly and accurately assess the concentration of carbon dioxide (CO₂) produced by blood microbial reproduction. The measurement principle and spectral processing algorithm of DLWMS are introduced first. The automatic and rapid detection of CO₂ is realized through a self-designed optical system. By using the system to detect blood infection, the accuracy of the technology was verified. Therefore, it also indicates that DLWMS CO₂ monitoring is a highly sensitive, fast-response and non-invasive technology, which can accurately and quickly determine blood infection and meet the clinical application requirements of human septicemia, bacteremia and other diseases. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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Open AccessArticle

Virtual Screening of Drug Proteins Based on the Prediction Classification Model of Imbalanced Data Mining

and

Processes 2022, 10(7), 1420; https://doi.org/10.3390/pr10071420 - 21 Jul 2022

Cited by 2 | Viewed by 981

Abstract

We propose a virtual screening method based on imbalanced data mining in this paper, which combines virtual screening techniques with imbalanced data classification methods to improve the traditional virtual screening process. First, in the actual virtual screening process, we apply k-means and smote heuristic oversampling method to deal with imbalanced data. Meanwhile, to enhance the accuracy of the virtual screening process, a particle swarm optimization algorithm is introduced to optimize the parameters of the support vector machine classifier, and the concept of ensemble learning is brought in. The classification technique based on particle swarm optimization, support vector machine and adaptive boosting is used to screen the molecular docking conformation to improve the accuracy of the prediction. Finally, in the experimental construction and analysis section, the proposed method was validated using relevant data from the protein data bank database and PubChem database. The experimental results indicated that the proposed method can effectively improve the accuracy of virus screening and has practical guidance for new drug development. This research regards virtual screening as a problem of imbalanced data classification, which has obvious guiding significance and also provides a certain reference for the problems faced by virtual screening technology. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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Open AccessArticle

Quantitative Method for Liquid Chromatography–Mass Spectrometry Based on Multi-Sliding Window and Noise Estimation

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Processes 2022, 10(6), 1098; https://doi.org/10.3390/pr10061098 - 01 Jun 2022

Viewed by 1329

Abstract

LC-MS/MS uses information on the mass peaks and peak areas of samples to conduct quantitative analysis. However, in the detection of clinical samples, the spectrograms of the compounds are interfered with for different reasons, which makes the identification of chromatographic peaks more difficult. Therefore, to improve the chromatographic interference problem, this paper first proposes a multi-window-based signal-to-noise ratio estimation algorithm, which contains the steps of raw data denoising, peak identification, peak area calculation and curve fitting to obtain accurate quantitative analysis results of the samples. Through the chromatographic peak identification of an extracted ion chromatogram of VD2 in an 80 ng/mL standard and the spectral peak identification of data from an open-source database, the identification results show that the algorithm has a better peak detection performance. The accuracy of the quantitative analysis was verified using the LC-HTQ-2020 triple quadrupole mass spectrometer produced by our group for the application of steroid detection in human serum. The results show that the algorithm proposed in this paper can accurately identify the peak information of LC-MS/MS chromatographic peaks, which can effectively improve the accuracy and reproducibility of steroid detection results and meet the requirements of clinical testing applications such as human steroid hormone detection. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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Review

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Open AccessReview

Methods and Advances in the Design, Testing and Development of In Vitro Diagnostic Instruments

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Processes 2023, 11(2), 403; https://doi.org/10.3390/pr11020403 - 29 Jan 2023

Cited by 2 | Viewed by 2100

Abstract

With the continuous improvement of medical testing and instrumentation engineering technologies, the design, testing and development methods of in vitro diagnostic instruments are developing rapidly. In vitro diagnostic instruments are also gradually developing into a class of typical high-end medical equipment. The design of in vitro diagnostic instruments involves a variety of medical diagnostic methods and biochemical, physical and other related technologies, and its development process involves complex system engineering. This paper systematically organizes and summarizes the design, testing and development methods of in vitro diagnostic instruments and their development in recent years, focusing on summarizing the related technologies and core aspects of the R&D process, and analyzes the development trend of the in vitro diagnostic instrument market. Full article

(This article belongs to the Special Issue Systematic Design, Testing and Development of In Vitro Diagnostic Instruments)

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