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Diagnostics
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Intelligent Imaging in Nuclear Medicine—2nd Edition
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Intelligent Imaging in Nuclear Medicine—2nd Edition

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 January 2025) | Viewed by 6132

Special Issue Editors

Dr. Dilber Uzun Ozsahin

E-Mail Website
Guest Editor
1. Department of Medical Diagnostic Imaging, College of Health Sciences, Sharjah University, Sharjah, United Arab Emirates
2. Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
3. Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Cyprus
Interests: medical imaging; nuclear medicine; decision theory in healthcare; artificial intelligence in healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Ilker Ozsahin

E-Mail Website
Guest Editor
1. Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
2.Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
Interests: medical imaging; radiology; operational research; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Georges El Fakhri

E-Mail Website
Guest Editor
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
Interests: medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) makes use of algorithms that are networked together to function as a neural network. The AI is taught by trial and error, and over time it learns to be very precise and accurate. The application of AI in nuclear medicine varies from easy checkups of diseases in the body to helping to analyze patients’ treatment progress and capturing images more quickly. It can be agreed that the efficiency and accuracy of AI has helped many clinicians to easily identify and diagnose the diseases in their patients’ bodies. The use of AI in diagnosis is more accurate than human experts in some cases. The application of AI in nuclear medicine is increasing in the diagnosis of diseases such as Alzheimer’s disease, Parkinson’s disease, and cardiological disorders. This Special Issue will cover AI applications in nuclear medicine and its application in different areas.

Dr. Dilber Uzun Ozsahin
Dr. Ilker Ozsahin
Prof. Dr. Georges El Fakhri
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

  • nuclear medicine
  • artificial intelligence
  • Alzheimer’s disease
  • Parkinson’s disease
  • cardiological disorders

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Related Special Issue

Published Papers (2 papers)

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Research

11 pages, 1195 KiB  
Article
Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying
by Michalis F. Georgiou, Efrosyni Sfakianaki, Monica N. Diaz-Kanelidis and Baha Moshiree
Diagnostics 2024, 14(12), 1240; https://doi.org/10.3390/diagnostics14121240 - 13 Jun 2024
Cited by 1 | Viewed by 1360
Abstract
Purpose: The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of [...] Read more.
Purpose: The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis. Methods: An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study. The model was trained and tested on a cohort of 1002 patients who underwent GES using a 70/30 stratified split ratio for training vs. testing. The ML software automated the generation of optimal predictive models by employing a combination of data preprocessing, appropriate feature selection, and predictive modeling analysis algorithms. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the predictive modeling performance. Several models were developed using different combinations of imaging time points as input features and methodologies to achieve optimal output. By using GE values at time points 0.5 h, 1 h, 1.5 h, 2 h, and 2.5 h as input predictors of the 4 h outcome, the analysis produced an AUC of 90.7% and a balanced accuracy (BA) of 80.0% on the test set. This performance was comparable to the training set results (AUC = 91.5%, BA = 84.7%) within the 95% confidence interval (CI), demonstrating a robust predictive capability. Through feature selection, it was discovered that the 2.5 h GE value alone was statistically significant enough to predict the 4 h outcome independently, with a slightly increased test set performance (AUC = 92.4%, BA = 83.3%), thus emphasizing its dominance as the primary predictor for delayed GE. ROC analysis was also performed for single time imaging points at 1 h and 2 h to assess their independent predictiveness of the 4 h outcome. Furthermore, the ML model was tested for its ability to predict “flipping” cases with normal GE at 1 h and 2 h that became abnormal with delayed GE at 4 h. Conclusions: An AI/ML model was designed and trained for predicting delayed GE using a limited number of imaging time points in a 4 h GES clinical protocol. This study demonstrates the feasibility of employing ML for GES optimization in the detection of delayed GE and potentially shortening the protocol’s time length without compromising diagnostic power. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine—2nd Edition)
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14 pages, 261 KiB  
Article
Advanced Computational Methods for Radiation Dose Optimization in CT
by Shreekripa Rao, Krishna Sharan, Srinidhi Gururajarao Chandraguthi, Rechal Nisha Dsouza, Leena R. David, Sneha Ravichandran, Mubarak Taiwo Mustapha, Dilip Shettigar, Berna Uzun, Rajagopal Kadavigere, Suresh Sukumar and Dilber Uzun Ozsahin
Diagnostics 2024, 14(9), 921; https://doi.org/10.3390/diagnostics14090921 - 29 Apr 2024
Cited by 2 | Viewed by 4127
Abstract
Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept [...] Read more.
Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution’s DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine—2nd Edition)
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