Artificial Intelligence and Big Data Applications in Diagnostics

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 15494

Special Issue Editors


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Guest Editor
Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA
Interests: biomedical engineering; mechatronics systems engineering; robotics and automation; electrical measurements of non-electrical quantities; machine vision and pattern recognition; applications of soft computing; sensors (validation, fusion)
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong BE1410, Brunei Darussalam
Interests: vibration, acoustic emission, and bio-medical signal processing; vibration condition monitoring, feature extraction, intelligent fault diagnosis, and prognosis; pattern recognition, machine learning, and deep learning; mechatronics and bio-mechatronics, instrumentation, and control system; product design, structure analysis, and finite element method
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to publish cutting-edge research on artificial-intelligence-based diagnosis techniques for various diseases, such as brain tumors, cancers, heart failures, strokes, liver malfunction, kidney problems, obesity, and surgery. The surveys of the World Health Organization (WHO) indicate that these diseases are the cause of more than 90% of deaths in the world. Thus, the diagnostics and cure of these diseases at the early stages can save a lot of lives. Hence, scientists, engineers, and medical health professionals are constantly looking for innovative techniques to diagnose various human diseases through their symptoms and clinical tests.

Furthermore, the proposed Special Issue will cover an in-depth analysis of recent developments in AI-based condition monitoring and fault diagnosis techniques used in industry for the protection of electrical drive systems such as motors, generators, and pumps. Due to enormous electric energy consumptions, the reliability of electrical system operation in a harsh industrial environment has been a major requirement in many industrial applications. It is especially important where an unexpected breakdown might result in the interruption of critical services such as military operations, transportation, municipality, aviation, and medical applications. An unexpected breakdown of the electrical system might result in costly maintenance or loss of life in applications where continuous process is needed and where downtime is not tolerable. Although electrical systems are very dependable with a low failure rate and require only basic maintenance, still, they will break down and fail after some time. Unexpected breakdowns of the electrical system cause a great deal of unacceptable production loss, particularly in applications that are vital for the industry. Consequently, detecting initial failures and replacing damaged parts according to schedule will prevent the problems of unexpected breakdowns of machines. The prevention of unscheduled downtime for electrical drive systems has been the goal of every industry for a long time, as this would help in reducing the costs associated with maintenance.

Therefore, this Special Issue will focus on but not be limited to the following topics:

  • Advanced biomedical engineering;
  • The role of artificial intelligence in healthcare;
  • The role of artificial intelligence in medicine chemistry;
  • The fault diagnostics of the medical instruments;
  • The reliability of the medical instruments;
  • Modern health diagnostic techniques;
  • Health prognostics;
  • Signal and image processing for biomedical applications;
  • Robotics for biomedical applications;
  • Modern communication systems for healthcare;
  • Smart machines for smart healthcare;
  • Smart decision making in the health sector;
  • Wearable devices for physical activity monitoring;
  • Vibration analysis techniques for fault analysis;
  • Acoustic emission techniques for failure analysis;
  • Application of signal processing and image processing techniques in machine fault diagnostics;
  • Application of artificial intelligence in machine fault classification;
  • Application of the Internet of Things (IoT) in system design, system management, data security, and fault diagnostics.

Dr. Muhammad Irfan
Dr. Thompson Sarkodie-Gyan
Dr. Wahyu Caesarendra
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. Data 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 (6 papers)

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Research

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27 pages, 7200 KiB  
Article
Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders
by Zeyad A. T. Ahmed, Eid Albalawi, Theyazn H. H. Aldhyani, Mukti E. Jadhav, Prachi Janrao and Mansour Ratib Mohammad Obeidat
Data 2023, 8(11), 168; https://doi.org/10.3390/data8110168 - 3 Nov 2023
Cited by 2 | Viewed by 2957
Abstract
Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis [...] Read more.
Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis as a pivotal precursor to effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology and deep learning algorithms, is investigated for its potential to discriminate between children with ASD and their typically developing (TD) peers. By employing these methods, the research aims to contribute to refining early detection strategies and support mechanisms. This study introduces innovative deep learning models grounded in convolutional neural network (CNN) and recurrent neural network (RNN) architectures, employing an eye-tracking dataset for training. Of note, performance outcomes have been realised, with the bidirectional long short-term memory (BiLSTM) achieving an accuracy of 96.44%, the gated recurrent unit (GRU) attaining 97.49%, the CNN-LSTM hybridising to 97.94%, and the LSTM achieving the most remarkable accuracy result of 98.33%. These outcomes underscore the efficacy of the applied methodologies and the potential of advanced computational frameworks in achieving substantial accuracy levels in ASD detection and classification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications in Diagnostics)
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12 pages, 1128 KiB  
Article
Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer
by Mohamed Ebrahim, Ahmed Ahmed Hesham Sedky and Saleh Mesbah
Data 2023, 8(2), 35; https://doi.org/10.3390/data8020035 - 2 Feb 2023
Cited by 13 | Viewed by 5717
Abstract
Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used [...] Read more.
Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used a dataset obtained from the National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep learning methods were included in the accuracy assessment. Classical decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector machine (SVM), and ensemble techniques (ET) algorithms were used. Probabilistic neural network (PNN), deep neural network (DNN), and recurrent neural network (RNN) methods were used for comparison. Feature selection and its effect on accuracy were also investigated. The results showed that decision trees and ensemble techniques outperformed the other techniques, as they both achieved a 98.7% accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications in Diagnostics)
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9 pages, 2209 KiB  
Article
Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues
by Denis S. Zavorotnyuk, Anatoly A. Sorokin, Stanislav I. Pekov, Denis S. Bormotov, Vasiliy A. Eliferov, Konstantin V. Bocharov, Eugene N. Nikolaev and Igor A. Popov
Data 2023, 8(1), 21; https://doi.org/10.3390/data8010021 - 16 Jan 2023
Viewed by 1743
Abstract
The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and [...] Read more.
The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models’ accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications in Diagnostics)
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8 pages, 245 KiB  
Article
Aggregation of Multimodal ICE-MS Data into Joint Classifier Increases Quality of Brain Cancer Tissue Classification
by Anatoly A. Sorokin, Denis S. Bormotov, Denis S. Zavorotnyuk, Vasily A. Eliferov, Konstantin V. Bocharov, Stanislav I. Pekov, Evgeny N. Nikolaev and Igor A. Popov
Data 2023, 8(1), 8; https://doi.org/10.3390/data8010008 - 27 Dec 2022
Cited by 1 | Viewed by 1474
Abstract
Mass spectrometry fingerprinting combined with multidimensional data analysis has been proposed in surgery to determine if a biopsy sample is a tumor. In the specific case of brain tumors, it is complicated to obtain control samples, leading to model overfitting due to unbalanced [...] Read more.
Mass spectrometry fingerprinting combined with multidimensional data analysis has been proposed in surgery to determine if a biopsy sample is a tumor. In the specific case of brain tumors, it is complicated to obtain control samples, leading to model overfitting due to unbalanced sample cohorts. Usually, classifiers are trained using a single measurement regime, most notably single ion polarity, but mass range and spectral resolution could also be varied. It is known that lipid groups differ significantly in their ability to produce positive or negative ions; hence, using only one polarity significantly restricts the chemical space available for sample discrimination purposes. In this work, we have developed an approach employing mass spectrometry data obtained by eight different regimes of measurement simultaneously. Regime-specific classifiers are trained, then a mixture of experts techniques based on voting or mean probability is used to aggregate predictions of all trained classifiers and assign a class to the whole sample. The aggregated classifiers have shown a much better performance than any of the single-regime classifiers and help significantly reduce the effect of an unbalanced dataset without any augmentation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications in Diagnostics)

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13 pages, 7723 KiB  
Data Descriptor
Datasets of Simulated Exhaled Aerosol Images from Normal and Diseased Lungs with Multi-Level Similarities for Neural Network Training/Testing and Continuous Learning
by Mohamed Talaat, Xiuhua Si and Jinxiang Xi
Data 2023, 8(8), 126; https://doi.org/10.3390/data8080126 - 31 Jul 2023
Cited by 3 | Viewed by 1314
Abstract
Although exhaled aerosols and their patterns may seem chaotic in appearance, they inherently contain information related to the underlying respiratory physiology and anatomy. This study presented a multi-level database of simulated exhaled aerosol images from both normal and diseased lungs. An anatomically accurate [...] Read more.
Although exhaled aerosols and their patterns may seem chaotic in appearance, they inherently contain information related to the underlying respiratory physiology and anatomy. This study presented a multi-level database of simulated exhaled aerosol images from both normal and diseased lungs. An anatomically accurate mouth-lung geometry extending to G9 was modified to model two stages of obstructions in small airways and physiology-based simulations were utilized to capture the fluid-particle dynamics and exhaled aerosol images from varying breath tests. The dataset was designed to test two performance metrics of convolutional neural network (CNN) models when used for transfer learning: interpolation and extrapolation. To this aim, three testing datasets with decreasing image similarities were developed (i.e., level 1, inbox, and outbox). Four network models (AlexNet, ResNet-50, MobileNet, and EfficientNet) were tested and the performances of all models decreased for the outbox test images, which were outside the design space. The effect of continuous learning was also assessed for each model by adding new images into the training dataset and the newly trained network was tested at multiple levels. Among the four network models, ResNet-50 excelled in performance in both multi-level testing and continuous learning, the latter of which enhanced the accuracy of the most challenging classification task (i.e., 3-class with outbox test images) from 60.65% to 98.92%. The datasets can serve as a benchmark training/testing database for validating existent CNN models or quantifying the performance metrics of new CNN models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications in Diagnostics)
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8 pages, 2306 KiB  
Data Descriptor
Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5
by Tobias Schlagenhauf, Jan Wolf and Alexander Puchta
Data 2022, 7(12), 175; https://doi.org/10.3390/data7120175 - 6 Dec 2022
Cited by 1 | Viewed by 1510
Abstract
Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly [...] Read more.
Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications in Diagnostics)
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