Submit to Diagnostics Review for Diagnostics Propose a Special Issue

Journal Menu

Journal Browser

Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 2021) | Viewed by 29981

Share This Special Issue

Special Issue Editors

Dr. Barbara Palumbo

E-Mail Website
Guest Editor

Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Italy
Interests: nuclear medicine; image-based diagnostics; artificial intelligence; PET/CT; SPECT; SPECT/CT; radiomics; oncology; neurodegenerative disorders
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Angela Spanu

E-Mail Website
Guest Editor

Nuclear Medicine Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, Italy
Interests: nuclear medicine; image-based diagnostics; SPECT; SPECT/CT; PET/CT; molecular breast imaging; oncology (breast cancer, lung cancer, thyroid cancer, neuroendocrine tumors, and prostate cancer); radiomics; neurodegenerative disorders; radiometabolic therapy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Luca Brunese

E-Mail Website
Guest Editor

Department of Medicine and Health Sciences “Vincenzo Tiberio”, Università degli Studi del Molise, Campobasso, Italy
Interests: artificial intelligence; image-based diagnostics; CT; MRI; radiology; radiomics; oncology
Special Issues, Collections and Topics in MDPI journals

Dr. Francesco Bianconi

E-Mail Website
Guest Editor

Department of Engineering, Università degli Studi di Perugia, Perugia, Italy
Interests: artificial intelligence; computational imaging; computer vision; image processing; medical image analysis; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Diagnostic imaging has experienced major changes in recent years. Radiological and nuclear medicine modalities represent the option of choice to investigate the main oncological and neurological diseases. The improving capabilities of the imaging devices and the increasing availability of storing, sharing and computing facilities have been generating larger and larger amounts of data. Consequently, there has been increasing attention on the development of computational methods for the extraction of objective imaging features (biomarkers) capable of correlating with disease phenotype, clinical outcome and/or response to treatment. The combined use of imaging data, biomarkers and artificial intelligence techniques makes it possible to build powerful predictive models which can assist the physician in the management of patients with a wide range of disorders, particularly oncological and neurological, ultimately leading to personalised treatment and better clinical outcome. However, there are still open challenges before these methods can be translated into clinical practice. Critical to this process, for instance, are standardisation, strong interdisciplinary cooperation, and the availability of centralised repositories of annotated data.

This Special Issue wants to provide a forum to discuss challenges, discoveries and opportunities in the field, with specific focus on the diagnosis of oncological and neurological disorders by radiological and nuclear medicine modalities. We encourage the submission research papers as well as review articles; comparative evaluations and new datasets are also welcome.

Dr. Barbara Palumbo
Prof. Dr. Angela Spanu
Prof. Dr. Luca Brunese
Dr. Francesco Bianconi
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 in diagnostic imaging
Computer-assisted diagnosis and prognostication
Data mining and big data
Deep Learning
Image processing (including acquisition, segmentation and feature extraction)
Radiology
Nuclear Medicine
Imaging modalities (including CT, MRI, PET, PET/CT, PET/MRI, SPECT, SPECT/CT)
Oncological and neurological disorders
Personalised medicine

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review, Other

13 pages, 7115 KiB

Open AccessArticle

Radiomics Analysis of Brain [¹⁸F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis

by Pierpaolo Alongi, Riccardo Laudicella, Francesco Panasiti, Alessandro Stefano, Albert Comelli, Paolo Giaccone, Annachiara Arnone, Fabio Minutoli, Natale Quartuccio, Chiara Cupidi, Gaspare Arnone, Tommaso Piccoli, Luigi Maria Edoardo Grimaldi, Sergio Baldari and Giorgio Russo

Diagnostics 2022, 12(4), 933; https://doi.org/10.3390/diagnostics12040933 - 8 Apr 2022

Cited by 18 | Viewed by 4468

Abstract

Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [¹⁸F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosis of AD, also by comparing the results with following Amyloid-PET and final clinical diagnosis. Methods: From July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clinical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD. Results: A total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (n = 6) and AD (n = 5) were found. Among them, two higher-order features (original_glcm_Idmn and original_glcm_Id), extracted from the limbic enthorinal cortical area (ROI-1) in the FDG-PET/CT images, predicted the positivity of Amyloid-PET/CT scans with maximum values of sensitivity (SS), specificity (SP), precision (PR) and accuracy (AC) of 84.92%, 75.13%, 73.75%, and 79.56%, respectively. Conversely, for the prediction of the clinical-instrumental final diagnosis of AD, the best performance was obtained by two higher-order features (original_glcm_MCC and original_glcm_Maximum Probability) extracted from ROI-2 (frontal cortex) with a SS, SP, PR and AC of 75.16%, 80.50%, 77.68%, and 78.05%, respectively, and by one higher-order feature (original_glcm_Idmn) extracted from ROI-3 (medial Temporal cortex; SS = 80.88%, SP = 76.85%, PR = 75.63%, AC = 78.76%. Conclusions: The results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Figure 1

14 pages, 1989 KiB

Open AccessArticle

A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors

by Ayten Kayi Cangir, Kaan Orhan, Yusuf Kahya, Ayse Uğurum Yücemen, İslam Aktürk, Hilal Ozakinci, Aysegul Gursoy Coruh and Serpil Dizbay Sak

Diagnostics 2022, 12(2), 416; https://doi.org/10.3390/diagnostics12020416 - 5 Feb 2022

Cited by 9 | Viewed by 2047

Abstract

Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud platform (Huiying Medical Technology Co., Ltd., Beijing, China) was used for managing the data, clinical data, and subsequent radiomics analysis. Two hand-crafted radiomics models are prepared in this study: the first model includes the data regarding all of the patients to differentiate between the groups; the second model includes 78 PCTs and 38 PHs without signs of fat tissue. The separation of the training and validation datasets was performed randomly using an (8:2) ratio and 620 random seeds. The results revealed that the MLP method (RF) was best for PH (AUC = 0.999) and PCT (AUC = 0.999) for the first model (AUC = 0.836), and PC (AUC = 0.836) in the test set for the second model. Radiomics tumor features derived from CT images are useful to differentiate the carcinoid tumors from hamartomas with high accuracy. Radiomics features may be used to differentiate PHs from PCTs with high levels of accuracy, even without the presence of fat on the CT. Advances in knowledge: CT-based radiomic holds great promise for a more accurate preoperative diagnosis of solitary pulmonary nodules (SPNs). Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Figure 1

12 pages, 3158 KiB

Open AccessArticle

Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices—A Feasibility Study

by Philipp Sager, Lukas Näf, Erwin Vu, Tim Fischer, Paul M. Putora, Felix Ehret, Christoph Fürweger, Christina Schröder, Robert Förster, Daniel R. Zwahlen, Alexander Muacevic and Paul Windisch

Diagnostics 2021, 11(9), 1676; https://doi.org/10.3390/diagnostics11091676 - 14 Sep 2021

Cited by 2 | Viewed by 2219

Abstract

Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then externally validated on contrast-enhanced and non-contrast-enhanced slices from a different institution. Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results: The model achieved an accuracy of 0.928 (95% CI: 0.869–0.987) on contrast-enhanced slices and 0.795 (95% CI: 0.702–0.888) on non-contrast-enhanced slices from the external validation cohorts. The implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the focus of the model was not limited to the contrast-enhancing tumor but to a larger area of the cerebellum and the cerebellopontine angle. Conclusions: Single-slice predictions might constitute a computationally inexpensive alternative to training 2.5/3D-CNNs for certain detection tasks in medical imaging even without the use of segmentations. Head-to-head comparisons between 2D and more sophisticated architectures could help to determine the difference in accuracy, especially for more difficult tasks. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Figure 1

17 pages, 646 KiB

Open AccessArticle

Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study

by Francesco Bianconi, Mario Luca Fravolini, Isabella Palumbo, Giulia Pascoletti, Susanna Nuvoli, Maria Rondini, Angela Spanu and Barbara Palumbo

Diagnostics 2021, 11(7), 1224; https://doi.org/10.3390/diagnostics11071224 - 6 Jul 2021

Cited by 8 | Viewed by 2514

Abstract

Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Figure 1

11 pages, 891 KiB

Open AccessArticle

Adding Value of MRI over CT in Predicting Peritoneal Cancer Index and Completeness of Cytoreduction

by Chia-Ni Lin, Weh-Shih Huang, Tzu-Hao Huang, Chao-Yu Chen, Cheng-Yi Huang, Ting-Yao Wang, Yu-San Liao and Li-Wen Lee

Diagnostics 2021, 11(4), 674; https://doi.org/10.3390/diagnostics11040674 - 8 Apr 2021

Cited by 17 | Viewed by 3176

Abstract

Background: This study aimed to investigate the adding value of MRI over CT for preoperative cytoreductive surgery with hyperthermic intraperitoneal chemotherapies (CRS/HIPEC). Methods: Imaging and intraoperative peritoneal cancer index (PCI) were calculated in 62 patients with peritoneal metastasis. Predictive models for the completeness of cytoreductive score using PCI data were established using decision tree algorithms. Results: In gastric cancer patients, a large discrepancy and poor agreement was appreciated between CT and surgical PCI, and a nonsignificant difference was noted between MRI and surgical PCI. In colon cancer patients, a better agreement and higher correlation with a smaller error was observed in PCI score using MRI than in that using CT. However, the addition of MRI to CT was limited for appendiceal and ovarian cancer patients. For predicting incomplete cytoreduction, CT models yielded inadequate accuracy while MRI models were more accurate with fair discrimination ability. Conclusions: CT was suitable for estimating PCI and surgery outcome in appendiceal and ovarian cancer patients, while further MRI in addition to CT was recommended for colon and gastric cancer patients. However, for classifying patients with peritoneal carcinomatosis into complete and incomplete cytoreduction, MRI was more effective than CT. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Graphical abstract

22 pages, 2707 KiB

Open AccessArticle

On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis

by Antonella Santone, Maria Paola Belfiore, Francesco Mercaldo, Giulia Varriano and Luca Brunese

Diagnostics 2021, 11(2), 293; https://doi.org/10.3390/diagnostics11020293 - 12 Feb 2021

Cited by 10 | Viewed by 2464

Abstract

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Figure 1

Review

Jump to: Research, Other

19 pages, 322 KiB

Open AccessReview

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

by Jeffrey Liu, Bino Varghese, Farzaneh Taravat, Liesl S. Eibschutz and Ali Gholamrezanezhad

Diagnostics 2022, 12(6), 1351; https://doi.org/10.3390/diagnostics12061351 - 30 May 2022

Cited by 7 | Viewed by 2453

Abstract

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

22 pages, 386 KiB

Open AccessReview

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

by Alfonso Reginelli, Valerio Nardone, Giuliana Giacobbe, Maria Paola Belfiore, Roberta Grassi, Ferdinando Schettino, Mariateresa Del Canto, Roberto Grassi and Salvatore Cappabianca

Diagnostics 2021, 11(10), 1796; https://doi.org/10.3390/diagnostics11101796 - 29 Sep 2021

Cited by 33 | Viewed by 4382

Abstract

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

Other

Jump to: Research, Review

2 pages, 173 KiB

Open AccessReply

Reply to Perrella et al. Coming Back to the Basics. Comment on “Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors. Diagnostics 2022, 12, 416”

by Ayten Kayi Cangir, Kaan Orhan and Aysegul Gursoy Coruh

Diagnostics 2023, 13(23), 3490; https://doi.org/10.3390/diagnostics13233490 - 21 Nov 2023

Viewed by 648

Abstract

We thank to Dr. Perrella and and his fellow authors for your kind letter and thoughtful comments [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

2 pages, 186 KiB

Open AccessComment

Coming Back to the Basics. Comment on Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors. Diagnostics 2022, 12, 416

by Armando Perrella, Giulio Bagnacci, Nunzia Di Meglio, Vito Di Martino, Cristiana Bellan, Luca Luzzi, Maria Antonietta Mazzei and Luca Volterrani

Diagnostics 2023, 13(23), 3489; https://doi.org/10.3390/diagnostics13233489 - 21 Nov 2023

Cited by 1 | Viewed by 723

Abstract

We read with great interest the article by Cangir et al., “A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors”, published on 5 February 2022 [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

13 pages, 1395 KiB

Open AccessSystematic Review

Diagnostic Performance of the Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson’s Disease: An Updated Systematic Review and Meta-Analysis

by Seongken Kim, Chong Hyun Suh, Woo Hyun Shim and Sang Joon Kim

Diagnostics 2022, 12(1), 12; https://doi.org/10.3390/diagnostics12010012 - 22 Dec 2021

Cited by 9 | Viewed by 3135

Abstract

Progressive supranuclear palsy (PSP) and Parkinson’s disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and EMBASE was performed to identify original articles evaluating the diagnostic performance of the MRPI in differentiating PSP from PD published up to 20 February 2021. The pooled sensitivity, specificity, and 95% CI were calculated using the bivariate random-effects model. The area under the curve (AUC) was calculated using a hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression was performed to explain the effects of heterogeneity. A total of 14 original articles involving 484 PSP patients and 1243 PD patients were included. In all studies, T1-weighted images were used to calculate the MRPI. Among the 14 studies, nine studies used 3D T1-weighted images. The pooled sensitivity and specificity for the diagnostic performance of the MRPI in differentiating PSP from PD were 96% (95% CI, 87–99%) and 98% (95% CI, 91–100%), respectively. The area under the HSROC curve was 0.99 (95% CI, 0.98–1.00). Heterogeneity was present (sensitivity: I² = 97.29%; specificity: I² = 98.82%). Meta-regression showed the association of the magnet field strength with heterogeneity. Studies using 3 T MRI showed significantly higher sensitivity (100%) and specificity (100%) than those of studies using 1.5 T MRI (sensitivity of 98% and specificity of 97%) (p < 0.01). Thus, the MRPI could accurately differentiate PSP from PD and support the implementation of appropriate management strategies for patients with PSP. Full article

(This article belongs to the Special Issue Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders)

► Show Figures

Journal Menu

Journal Browser

Artificial Intelligence in Image-Based Diagnostics of Oncological and Neurological Disorders

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI