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Diagnostics
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The Impact of Artificial Intelligence in the New Trends of...
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The Impact of Artificial Intelligence in the New Trends of Non-invasive Biomarkers

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 (31 December 2023) | Viewed by 9366

Special Issue Editors

Prof. Dr. Ricardo Teresa Ribeiro

E-Mail Website
Guest Editor
H&TRC- Health & Technology Research Center, ESTeSL- Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisbon, Portugal
Interests: medical imaging; computer vision; deep learning
Prof. Dr. Miguel Brito

E-Mail Website
Guest Editor
H&TRC—Health & Technology Research Center, ESTeSL—Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, 1990-096 Lisbon, Portugal
Interests: human genetics; genetic epidemiology; nutritional epidemiology; pharmacogenetics; molecular diagnostics

Special Issue Information

Dear Colleagues,

A new era for noninvasive biomarkers has begun due to an unprecedented acceleration in recent advances in high performance computing, the availability of large annotated data sets required for training, and new frameworks for implementing deep learning (DL) networks. It has become apparent that medical images can serve as mineable data that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as the texture of radiomic analysis.

DL and radiomics-based techniques have enabled non-invasive characterization of tumor extent and functional metabolic activity and play a central role in screening, diagnostic work-up and surveillance of oncology patients.

In this Special Issue, the current use and future potential of noninvasive biomarkers based on DL- or radiomics-based approaches from medical imaging analysis will be discussed in different clinical diagnostic areas.

Prof. Dr. Ricardo Teresa Ribeiro
Prof. Dr. Miguel Brito
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

  • Radiomics
  • Deep Learning
  • Artificial Intelligence
  • Medical Imaging
  • Noninvasive Biomarkers
  • Screening
  • Precision medicine
  • Neuroimaging
  • Breast and thyroid
  • Gastroenterology and hepatology

Published Papers (3 papers)

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Research

11 pages, 1238 KiB  
Article
Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
by Danijela Tasić, Katarina Đorđević, Slobodanka Galović, Draško Furundžić, Zorica Dimitrijević and Sonja Radenković
Diagnostics 2022, 12(12), 3131; https://doi.org/10.3390/diagnostics12123131 - 12 Dec 2022
Viewed by 1367
Abstract
Markers used in everyday clinical practice cannot distinguish between the permanent impairment of renal function. Sodium and potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. The aim of this study was to investigate [...] Read more.
Markers used in everyday clinical practice cannot distinguish between the permanent impairment of renal function. Sodium and potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. The aim of this study was to investigate volemia and electrolyte status from a clinical cardiorenal viewpoint under consideration of renal function utilizing artificial intelligence. In this paper, an analysis of five variables: B-type natriuretic peptide, sodium, potassium, ejection fraction, EPI creatinine-cystatin C, was performed using an algorithm based on the adaptive neuro fuzzy inference system. B-type natriuretic peptide had the greatest influence on the ejection fraction. It has been shown that values of both Na+ and K+ lead to deterioration of the condition and vital endangerment of patients. To identify the risk of occurrence, the model identifies a prognostic biomarker by random regression from the total data set. The predictions obtained from this model can help optimize preventative strategies and intensive monitoring for patients identified as at risk for electrolyte disturbance and hypervolemia. This approach may be superior to the traditional diagnostic approach due to its contribution to more accurate and rapid diagnostic interpretation and better planning of further patient treatment Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence in the New Trends of Non-invasive Biomarkers)
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10 pages, 1419 KiB  
Article
A Robust, Fully Automatic Detection Method and Calculation Technique of Midline Shift in Intracranial Hemorrhage and Its Clinical Application
by Jiun-Lin Yan, Yao-Lian Chen, Moa-Yu Chen, Bo-An Chen, Jiung-Xian Chang, Ching-Chung Kao, Meng-Chi Hsieh, Yi-Ting Peng, Kuan-Chieh Huang and Pin-Yuan Chen
Diagnostics 2022, 12(3), 693; https://doi.org/10.3390/diagnostics12030693 - 11 Mar 2022
Cited by 8 | Viewed by 3917
Abstract
A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications [...] Read more.
A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications of the fully automatic model. We recruited 300 consecutive non-contrast CT scans consisting of 7269 slices in this study. Six different types of hemorrhage were included. The automatic detection of MLS was based on modified Keypoint R-CNN with keypoint detection followed by training on the ResNet-FPN-50 backbone. The results were further compared with manually drawn outcomes and manually defined keypoint calculations. Clinical parameters, including Glasgow coma scale (GCS), Glasgow outcome scale (GOS), and 30-day mortality, were also analyzed. The mean absolute error for the automatic detection of an MLS was 0.936 mm compared with the ground truth. The interclass correlation was 0.9899 between the automatic method and MLS drawn by different clinicians. There was high sensitivity and specificity in the detection of MLS at 2 mm (91.7%, 80%) and 5 mm (87.5%, 96.7%) and MLSs greater than 10 mm (85.7%, 97.7%). MLS showed a significant association with initial poor GCS and GCS on day 7 and was inversely correlated with poor 30-day GOS (p < 0.001). In conclusion, automatic detection and calculation of MLS can provide an accurate, robust method for MLS measurement that is clinically comparable to the manually drawn method. Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence in the New Trends of Non-invasive Biomarkers)
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13 pages, 1309 KiB  
Article
Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level
by Ziyu Zhu, Du Lei, Kun Qin, Xueling Suo, Wenbin Li, Lingjiang Li, Melissa P. DelBello, John A. Sweeney and Qiyong Gong
Diagnostics 2021, 11(8), 1416; https://doi.org/10.3390/diagnostics11081416 - 05 Aug 2021
Cited by 6 | Viewed by 2598
Abstract
Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a [...] Read more.
Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD. Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence in the New Trends of Non-invasive Biomarkers)
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