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Novel Applications of Artificial Intelligence in Medicine and Health

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 16309

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Special Issue Editors

Dr. Hélder P. Oliveira

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Guest Editor

INESC TEC and Faculty of Sciences, University of Porto, 4000-008 Porto, Portugal
Interests: medical image analysis; computer vision; machine learning; data science; computer science photos
Special Issues, Collections and Topics in MDPI journals

Dr. António Cunha

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Guest Editor

Department of Engineering, University of Trás-os-Montes and Alto Douro and INESC TEC, 5000-801 Vila Real, Portugal
Interests: medical image analysis; bio-image analysis; computer vision; image and video processing; machine learning; artificial intelligence, with a focus on the application of computer-aided diagnosis across various imaging modalities, including ophthalmology, endoscopic capsule videos and the computed tomography of the lung
Special Issues, Collections and Topics in MDPI journals

Dr. Tania Pereira

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Guest Editor

Institute for Systems and Computer Engineering, Technology and Science, 4000-008 Porto, Portugal
Interests: medical image analysis; computer vision; machine learning; medical instrumentation; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Innovative solutions that make use of digital technologies, including eHealth, Big Data, and AI, are seen by clinical professional as opportunities to transform healthcare systems and patient health. Stakeholders Communication on enabling the digital transformation of health and care in the Digital Single Market discusses the challenges that healthcare systems are facing and the need for new technologies and approaches that better leverage data. By allowing for the smart, efficient, and safe use of patient data, AI tools can be better utilized to improve healthcare for the benefit of patients and their care givers, health care systems and the economy as a whole.

This Special Issue on “Novel Application of artificial intelligence in Medicine and Health” welcomes the submission of works dedicated to novel AI approaches for medical image analysis by incorporating innovative strategies.

Prof. Dr. Hélder P. Oliveira
Prof. Dr. António Cunha
Dr. Tania Pereira
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

artificial intelligence;
medical imaging;
computer vision;
machine learning;
deep learning;
computational intelligence;
biomedical image analysis;
radiomics; radiogenomics;
bioinformatics;
computational biology;
multiomics data;
eHealth;
Big Data

Published Papers (6 papers)

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Editorial

Jump to: Research

3 pages, 172 KiB

Open AccessEditorial

Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health

by Tania Pereira, António Cunha and Hélder P. Oliveira

Appl. Sci. 2023, 13(2), 881; https://0-doi-org.brum.beds.ac.uk/10.3390/app13020881 - 09 Jan 2023

Cited by 3 | Viewed by 1248

Abstract

Artificial Intelligence (AI) is one of the big hopes for the future of a positive revolution in the use of medical data to improve clinical routine and personalized medicine [...] Full article

(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Medicine and Health)

Research

Jump to: Editorial

14 pages, 3202 KiB

Open AccessArticle

Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs

by Carmela Nappi, Rosario Megna, Fabio Volpe, Andrea Ponsiglione, Elisa Caiazzo, Leandra Piscopo, Ciro Gabriele Mainolfi, Emilia Vergara, Massimo Imbriaco, Michele Klain, Mario Petretta and Alberto Cuocolo

Appl. Sci. 2022, 12(11), 5468; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115468 - 27 May 2022

Cited by 3 | Viewed by 1930

Abstract

Coronary artery calcification and sarcopenia may have a relevant prognostic impact in oncological and non-oncological patients. The use of freeware software is promising for quantitative evaluation of these parameters after whole-body positron emission tomography (PET)/computed tomography (CT) and might be useful for one-stop shop risk stratification without additional radiation ionizing burden and further charges to health care costs. In this study, we compared two semiautomatic freeware software tools (Horos Medical Image software and LIFEx) for the assessment of coronary artery calcium (CAC) score and muscle mass in 40 patients undergoing whole-body PET/CT. The muscle areas obtained by the two software programs were comparable, showing high correlation with Lin’s concordance coefficient (0.9997; 95% confidence intervals: 0.9995–0.9999) and very good agreement with Bland–Altman analysis (mean difference = 0.41 cm², lower limit = −1.06 cm², upper limit = 1.89) was also found. For CAC score, Lin’s concordance correlation coefficient was 0.9976 (95% confidence intervals: 0.9965–0.9984) and in a Bland–Altman analysis an increasing mean difference from 8 to 78 by the mean values (intercept = −0.050; slope = 0.054; p < 0.001) was observed, with a slight overestimation of Horos CAC score as compared to LIFEx, likely due to a different calculation method of the CAC score, with the ROI being equal for the two software programs. Our results demonstrated that off-line analysis performed with freeware software may allow a comprehensive evaluation of the oncological patient, making available the evaluation of parameters, such as muscle mass and calcium score, that may be relevant for the staging and prognostic stratification of these patients, beside standard data obtained by PET/CT imaging. For this purpose, the Horos and LIFEx software seem to be interchangeable. Full article

(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Medicine and Health)

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13 pages, 1042 KiB

Open AccessArticle

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

by Bagyamathi Mathiyazhagan, Joseph Liyaskar, Ahmad Taher Azar, Hannah H. Inbarani, Yasir Javed, Nashwa Ahmad Kamal and Khaled M. Fouad

Appl. Sci. 2022, 12(4), 2020; https://0-doi-org.brum.beds.ac.uk/10.3390/app12042020 - 15 Feb 2022

Cited by 8 | Viewed by 1715

Abstract

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational methods overcome the issues effectually. In computational technique, the peptide residues with anti-HIV-1 activity are predicted by classification method, and the learning process of the classification is improved with significant features. Rough set-based algorithms are capable of dealing with the gaps and imperfections present in real-time data. In this work, feature selection using Rough Set Improved Harmony Search Quick Reduct and Rough Set Improved Harmony Search Relative Reduct with Rough Set Classification framework is implemented to classify Anti-HIV-1 peptides. The primary objective of the proposed methodology is to predict the peptides with an anti-HIV-1 activity using effective feature selection and classification algorithms incorporated in the proposed framework. The results of the proposed algorithms are comparatively studied with existing rough set feature selection algorithms and benchmark classifiers, and the reliability of the algorithms implemented in the proposed framework is measured by validity measures, such as Precision, Recall, F-measure, Kulczynski Index, and Fowlkes–Mallows Index. The final results show that the proposed framework analyzed and classified the peptides with a high predictive accuracy of 96%. In this study, we have investigated the ability of a rough set-based framework with sequence-based numeric features to classify anti-HIV-1 peptides, and the experimentation results show that the proposed framework discloses the most satisfactory solutions, where it rapidly congregates in the problem space and finds the best reduct, which improves the prediction accuracy of the given dataset. Full article

(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Medicine and Health)

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16 pages, 25905 KiB

Open AccessArticle

Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset

by Joana Sousa, Tania Pereira, Francisco Silva, Miguel C. Silva, Ana T. Vilares, António Cunha and Hélder P. Oliveira

Appl. Sci. 2022, 12(4), 1959; https://0-doi-org.brum.beds.ac.uk/10.3390/app12041959 - 13 Feb 2022

Cited by 12 | Viewed by 4000

Abstract

Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 ± 0.2140 and 0.8750 ± 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model. Full article

(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Medicine and Health)

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16 pages, 3680 KiB

Open AccessArticle

An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare

by Sumayah Al-Rabiaah, Manar Hosny and Sarab AlMuhaideb

Appl. Sci. 2022, 12(3), 1403; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031403 - 28 Jan 2022

Cited by 9 | Viewed by 2021

Abstract

Recently, drones, have been utilized in many real-life applications including healthcare services. For example, providing medical supplies, blood samples, and vaccines to people in remote areas or during emergencies. In this study, the maximum coverage facility location problem with drones (MCFLPD) was studied. The problem is the application of drones in the context of the facility location and routing. It involves selecting the locations of drone launching centers, which maximizes patient service coverage within certain drone range constraints. In this study, a heuristic named the maximum coverage greedy randomized heuristic (MCGRH) is developed. The idea of the algorithm is to first choose some facilities to open at random from among those that can handle the most weight of the patient demands. After that, patients are assigned to the closest opened facility with the capacity to serve them. Finally, drones are assigned to patients based on the least amount of battery consumed between the patient and the facility. Extensive testing of MCGRH indicated that it ranks efficiently alongside other methods in the literature that tried to solve the MCFLPD. It was able to achieve a high coverage of patients (more than 80% on average) within a very fast processing time (less than 1 s on average). Full article

(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Medicine and Health)

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12 pages, 1594 KiB

Open AccessArticle

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

by Joana Morgado, Tania Pereira, Francisco Silva, Cláudia Freitas, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António J. Madureira, Isabel Ramos, Venceslau Hespanhol, José Luis Costa, António Cunha and Hélder P. Oliveira

Appl. Sci. 2021, 11(7), 3273; https://0-doi-org.brum.beds.ac.uk/10.3390/app11073273 - 06 Apr 2021

Cited by 22 | Viewed by 4056

Abstract

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies. Full article

(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Medicine and Health)

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