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Artificial Intelligence Approaches for Medical Diagnostics in Europe

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5911

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Special Issue Editors

Dr. Evanthia E. Tripoliti

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Guest Editor

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
Interests: artificial intelligence; data mining; machine learning; biomedical signal processing; biomedical image processing; biomedical engineering

Dr. Giuseppe Fico

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Guest Editor

Assistant Professor of Biomedical Engineering, Universidad Politécnica de Madrid, ETSI Telecomunicación, Avenida Complutense, 30, Ciudad Universitaria, 28040 Madrid, Spain
Interests: artificial intelligence and digital health; artificial intelligence and clinical decision support systems; artificial intelligence and health knowledge management; artificial intelligence and health technology assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is considered one of the areas of strategic importance; it has the potential to be a key driver of economic development, in addition to having a wide range of social implications. Governments in Europe and worldwide have identified health as one of the key applications of AI. The European Commission, as part of its AI strategy, has included health as one of the three sectors for which it has provided specific recommendations on investments. More specifically, the EU’s approach centers on fostering excellence in AI, building trustworthy AI, addressing the risks generated using AI in the medical sector, aiming to boost research and industrial capacity and ensure fundamental rights.

AI applications hold a significant potential for enhancing and streamlining existing tasks in medical practice, such as diagnosis, treatment, prevention, progression, and personalized care. The aim of this Special Issue is to bring together all actors involved in the ecosystem of AI approaches related to medical diagnostics, to present state-of-the-art methodologies and ideas that can be adopted in medical operational practice, including but not limited to:

Medical image analysis and diagnostics;
Clinical decision-support systems;
Knowledge management and AI in diagnostics;
Semantics for AI-based applications in diagnostics;
Molecular diagnostics;
Biosensors and biochips in diagnostics;
Multi-scale models in diagnostics;
Explainable and trustworthy AI in diagnostics;
Causal machine learning;
Risk assessment and patient risk stratification with AI;
Diagnostics biomarkers discovery;
AI in electronic health records;
Machine learning/deep learning in medicine;
Lab-on-chip diagnostics;
IoT wearable/implantable devices for diagnostics.

Dr. Evanthia E. Tripoliti
Dr. Giuseppe Fico
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. Diagnostics 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 2600 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
machine learning
deep learning
precision medicine
diagnostics
biosensors
data mining
prevention
big data analysis

Published Papers (4 papers)

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Research

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13 pages, 3666 KiB

Open AccessArticle

Harnessing Machine Learning in Vocal Arts Medicine: A Random Forest Application for “Fach” Classification in Opera

by Zehui Wang, Matthias Müller, Felix Caffier and Philipp P. Caffier

Diagnostics 2023, 13(18), 2870; https://doi.org/10.3390/diagnostics13182870 - 6 Sep 2023

Cited by 1 | Viewed by 964

Abstract

Vocal arts medicine provides care and prevention strategies for professional voice disorders in performing artists. The issue of correct “Fach” determination depending on the presence of a lyric or dramatic voice structure is of crucial importance for opera singers, as chronic overuse often leads to vocal fold damage. To avoid phonomicrosurgery or prevent a premature career end, our aim is to offer singers an improved, objective fach counseling using digital sound analyses and machine learning procedures. For this purpose, a large database of 2004 sound samples from professional opera singers was compiled. Building on this dataset, we employed a classic ensemble learning method, namely the Random Forest algorithm, to construct an efficient fach classifier. This model was trained to learn from features embedded within the sound samples, subsequently enabling voice classification as either lyric or dramatic. As a result, the developed system can decide with an accuracy of about 80% in most examined voice types whether a sound sample has a lyric or dramatic character. To advance diagnostic tools and health in vocal arts medicine and singing voice pedagogy, further machine learning methods will be applied to find the best and most efficient classification method based on artificial intelligence approaches. Full article

(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in Europe)

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18 pages, 3053 KiB

Open AccessArticle

A Comparative Study of Deep Neural Networks for Real-Time Semantic Segmentation during the Transurethral Resection of Bladder Tumors

by Dóra Varnyú and László Szirmay-Kalos

Diagnostics 2022, 12(11), 2849; https://doi.org/10.3390/diagnostics12112849 - 17 Nov 2022

Cited by 4 | Viewed by 1464

Abstract

Bladder cancer is a common and often fatal disease. Papillary bladder tumors are well detectable using cystoscopic imaging, but small or flat lesions are frequently overlooked by urologists. However, detection accuracy can be improved if the images from the cystoscope are segmented in real time by a deep neural network (DNN). In this paper, we compare eight state-of-the-art DNNs for the semantic segmentation of white-light cystoscopy images: U-Net, UNet++, MA-Net, LinkNet, FPN, PAN, DeepLabv3, and DeepLabv3+. The evaluation includes per-image classification accuracy, per-pixel localization accuracy, prediction speed, and model size. Results show that the best F-score for bladder cancer (91%), the best segmentation map precision (92.91%), and the lowest size (7.93 MB) are also achieved by the PAN model, while the highest speed (6.73 ms) is obtained by DeepLabv3+. These results indicate better tumor localization accuracy than reported in previous studies. It can be concluded that deep neural networks may be extremely useful in the real-time diagnosis and therapy of bladder cancer, and among the eight investigated models, PAN shows the most promising results. Full article

(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in Europe)

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13 pages, 2028 KiB

Open AccessArticle

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function

by Jan Eckstein, Negin Moghadasi, Hermann Körperich, Elena Weise Valdés, Vanessa Sciacca, Lech Paluszkiewicz, Wolfgang Burchert and Misagh Piran

Diagnostics 2022, 12(11), 2693; https://doi.org/10.3390/diagnostics12112693 - 4 Nov 2022

Cited by 10 | Viewed by 1703

Abstract

Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics. Full article

(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in Europe)

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Other

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17 pages, 1299 KiB

Open AccessSystematic Review

Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?—Systematic Review

by Marek Malík, Anton Dzian, Martin Števík, Štefánia Vetešková, Abdulla Al Hakim, Maroš Hliboký, Ján Magyar, Michal Kolárik, Marek Bundzel and František Babič

Diagnostics 2023, 13(18), 2995; https://doi.org/10.3390/diagnostics13182995 - 19 Sep 2023

Cited by 2 | Viewed by 1026

Abstract

Background: Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the evaluation of LUS videos by artificial intelligence could improve the implementation of LUS in thoracic surgery. Methods: A literature review of the replacement of the CXR by LUS after thoracic surgery and the evaluation of LUS videos by artificial intelligence after thoracic surgery was conducted in Medline. Results: Here, eight out of 10 reviewed studies evaluating LUS in CXR reduction showed that LUS can reduce CXR without a negative impact on patient outcome after thoracic surgery. No studies on the evaluation of LUS signs by artificial intelligence after thoracic surgery were found. Conclusion: LUS can reduce CXR after thoracic surgery. We presume that artificial intelligence could help increase the LUS accuracy, objectify the LUS findings, shorten the learning curve, and decrease the number of inconclusive results. To confirm this assumption, clinical trials are necessary. This research is funded by the Slovak Research and Development Agency, grant number APVV 20-0232. Full article

(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in Europe)

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