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J. Imaging
Special Issues
Artificial Intelligence and Advanced Computational Techniques for Precision Medicine
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Artificial Intelligence and Advanced Computational Techniques for Precision Medicine

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 8548

Special Issue Editors

Dr. Vincenzo Conti

E-Mail Website
Guest Editor
Faculty of Engineering and Architecture, University of Enna KORE, 94100 Enna, Italy
Interests: biometric recognition systems; bio-inspired processing systems; medical diagnosis support
Special Issues, Collections and Topics in MDPI journals
Dr. Carmelo Militello

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), 90146 Palermo, Italy
Interests: biomedical image analysis; radiomics; machine learning; digital architectures; biometrics; hardware programmable devices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wellington Pinheiro Dos Santos

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Federal University of Pernambuco, UFPE, Recife, Brazil
Interests: computational intelligence in medicine; intelligent health systems; biomedical computing; health informatics; biomedical engineering
Dr. Ganesh Naik

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide, Australia
Interests: blind source separation; independent component analysis; biomedical signal processing; human computer interaction; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Nicole Dalia Cilia

E-Mail Website
Guest Editor
Faculty of Engineering and Architecture, University of Enna KORE, Enna, Italy
Interests: handwriting; neurodegenerative disease; decision support systems

Special Issue Information

Dear Colleagues,

Nowadays, there is a growing interest in healthcare processes that quantitatively analyze biomedical data using computer-assisted techniques. Health informatics make possible to acquire further insights into diseases, thus enabling the development and the implementation of clinical decision support systems (CDSSs).

The great variability of this scenario is linked to both the different diseases that are experienced by patients and the specific clinical reader (inter- and intra- observer dependence) that analyzes and identifies the disease and that decides the most adequate treatment process, and both of these factors must be considered.

Researchers coupled with expert clinicians can play an important role in turning complex medical data into actionable knowledge that ultimately improves patient care.

In recent years, there has been a revolution in which the combined use of different types of data, both imaging (e.g., multi-modality imaging) and clinical data, allowing bio-markers that are capable of characterizing diseases to be identified. On the other hand, the real-time acquisition of physicians (e.g., motion recording analysis) allows researchers to produce a continuous flow of data that can be used to better train machine learning algorithms, offering more precise results.

Among the new tools that are available, such as  bioimages, radiomics has experienced a great deal of diffusion and, if used properly, represents a powerful tool to support treatment and to customize the care of specific patients.

CDSSs—through Artificial Intelligence and computational techniques—enable inter- and intra-observer dependence to be reduced and make it possible to enhance result repeatability. In particular, the use of CDSSs can help clinicians in all phases of the treatment process, from diagnosis to therapeutic treatment planning, as well as to support prognosis.

This Special Issue will provide a forum through which researchers can publish original research papers covering state-of-the-art and novel algorithms, techniques, and applications of Artificial Intelligence methods, ranging from those implementing classic machine-learning to deep-learning, for biomedical data analysis aiming at precision medicine.

Topics of interest include but are not limited to:

  • Health informatics;
  • Biomedical image processing;
  • Machine-learning and deep-learning techniques for image analysis;
  • Artificial intelligence for precision medicine;
  • Clinical decision support systems (c-DSSs);
  • Physiological signal analysis;
  • Radiomic analyses and models for personalized medicine;
  • Deep learning for neuroimaging and oncological imaging analysis;
  • Computational intelligence for single-cell data analysis;
  • Detection, quantification, and characterization of ROIs from multi-modal medical images;
  • Co-registration and fusion of multi-modality imaging for treatment planning and image-guided intervention;
  • Motion and gait analysis in neurodegenerative diseases;
  • Data analytics for healthcare.

Dr. Vincenzo Conti
Dr. Carmelo Militello
Prof. Dr. Wellington Pinheiro Dos Santos
Dr. Ganesh Naik
Dr. Nicole Dalia Cilia
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. Journal of Imaging 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 1800 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

  • biomedical imaging
  • artificial intelligence
  • computational intelligence
  • health informatics
  • radiomics
  • machine learning
  • deep learning
  • precision medicine
  • motion recording analysis
  • clinical decision support systems

Published Papers (4 papers)

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Research

18 pages, 2170 KiB  
Article
Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images
by Francesco Prinzi, Carmelo Militello, Vincenzo Conti and Salvatore Vitabile
J. Imaging 2023, 9(2), 32; https://doi.org/10.3390/jimaging9020032 - 30 Jan 2023
Cited by 6 | Viewed by 1680
Abstract
Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) [...] Read more.
Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Computational Techniques for Precision Medicine)
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21 pages, 6798 KiB  
Article
The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions
by Kenichi Ito, Hiroshi Higashi, Ari Hietanen, Pauli Fält, Kyoko Hine, Markku Hauta-Kasari and Shigeki Nakauchi
J. Imaging 2023, 9(1), 7; https://doi.org/10.3390/jimaging9010007 - 29 Dec 2022
Cited by 2 | Viewed by 1911
Abstract
Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance [...] Read more.
Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 × 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant D65. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Computational Techniques for Precision Medicine)
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14 pages, 2660 KiB  
Article
Z2-γ: An Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images
by Antonella Falini
J. Imaging 2022, 8(11), 301; https://doi.org/10.3390/jimaging8110301 - 5 Nov 2022
Viewed by 1273
Abstract
Brain tumors are abnormal cell growth in the brain tissues that can be cancerous or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool [...] Read more.
Brain tumors are abnormal cell growth in the brain tissues that can be cancerous or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool commonly adopted by neurologists and radiologists to identify and classify any possible anomalies present in the brain anatomy. In the present work, an automatic unsupervised method called Z2-γ, based on the use of adaptive finite-elements and suitable pre-processing and post-processing techniques, is introduced. The adaptive process, driven by a Zienkiewicz-Zhu type error estimator (Z2), is carried out on isotropic triangulations, while the given input images are pre-processed via nonlinear transformations (γ corrections) to enhance the ability of the error estimator to detect any relevant anomaly. The proposed methodology is able to automatically classify whether a given MR image represents a healthy or a diseased brain and, in this latter case, is able to locate the tumor area, which can be easily delineated by removing any redundancy with post-processing techniques based on morphological transformations. The method is tested on a freely available dataset achieving 0.846 of accuracy and F1 score equal to 0.88. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Computational Techniques for Precision Medicine)
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25 pages, 1112 KiB  
Article
Shapley-Additive-Explanations-Based Factor Analysis for Dengue Severity Prediction using Machine Learning
by Shihab Uddin Chowdhury, Sanjana Sayeed, Iktisad Rashid, Md. Golam Rabiul Alam, Abdul Kadar Muhammad Masum and M. Ali Akber Dewan
J. Imaging 2022, 8(9), 229; https://doi.org/10.3390/jimaging8090229 - 26 Aug 2022
Cited by 3 | Viewed by 2749
Abstract
Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities. The development of Dengue Haemorrhagic Fever (DHF) is where [...] Read more.
Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities. The development of Dengue Haemorrhagic Fever (DHF) is where most cases occur, and a large portion of them are detected among children under the age of ten, with severe conditions often progressing to a critical state known as Dengue Shock Syndrome (DSS). In this study, we analysed two separate datasets from two different countries– Vietnam and Bangladesh, which we referred as VDengu and BDengue, respectively. For the VDengu dataset, as it was structured, supervised learning models were effective for predictive analysis, among which, the decision tree classifier XGBoost in particular produced the best outcome. Furthermore, Shapley Additive Explanation (SHAP) was used over the XGBoost model to assess the significance of individual attributes of the dataset. Among the significant attributes, we applied the SHAP dependence plot to identify the range for each attribute against the number of DHF or DSS cases. In parallel, the dataset from Bangladesh was unstructured; therefore, we applied an unsupervised learning technique, i.e., hierarchical clustering, to find clusters of vital blood components of the patients according to their complete blood count reports. The clusters were further analysed to find the attributes in the dataset that led to DSS or DHF. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Computational Techniques for Precision Medicine)
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