Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.2
Journals
J. Imaging
Special Issues
Clinical and Pathological Imaging in the Era of Artificial Intelligence:...
share announcement

Special Issue "Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 506

Special Issue Editors

Dr. Gerardo Cazzato

E-Mail Website
Guest Editor
Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
Interests: dermatopathology; dermatology; skin diseases; skin tumors; soft tissue pathology; gynecopathology; neuropathology
Special Issues, Collections and Topics in MDPI journals
Dr. Francesca Arezzo
E-Mail Website
Guest Editor
Gynecologic Oncology Unit, Department of Precision and Regenerative Medicine-Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
Interests: gynecologic oncology; gynecological malignancy; gynecological ultrasound; artificial intelligence in gynecology; radiomics in gynecological imaging

Special Issue Information

Dear Colleagues,

Clinical imaging has always been one of the primary modalities of patient study, depending on the most diverse pathologies that may come to the attention of the clinical physician. On the other hand, pathology has also benefitted from this investment in innovation, with the development of new instrumentation such as digital scanners and algorithms that can co-advise the pathologist in routine diagnostics. In this Special Issue, we aim to focus our attention on the new artificial intelligence (AI) methods that have developed precisely from imaging and that are beginning to be validated as a medical aid not only at the patient's bedside but also at a distance (telemedicine).

Dr. Gerardo Cazzato
Dr. Francesca Arezzo
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1600 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
  • clinical imaging
  • pathology
  • ginecopathology
  • dermatopathology

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 5789 KiB  
Article
SCOLIONET: An Automated Scoliosis Cobb Angle Quantification Using Enhanced X-ray Images and Deep Learning Models
by
Renato R. Maaliw III
J. Imaging 2023, 9(12), 265; https://doi.org/10.3390/jimaging9120265 - 30 Nov 2023
Viewed by 312
Abstract
The advancement of medical prognoses hinges on the delivery of timely and reliable assessments. Conventional methods of assessments and diagnosis, often reliant on human expertise, lead to inconsistencies due to professionals’ subjectivity, knowledge, and experience. To address these problems head-on, we harnessed artificial [...] Read more.
The advancement of medical prognoses hinges on the delivery of timely and reliable assessments. Conventional methods of assessments and diagnosis, often reliant on human expertise, lead to inconsistencies due to professionals’ subjectivity, knowledge, and experience. To address these problems head-on, we harnessed artificial intelligence’s power to introduce a transformative solution. We leveraged convolutional neural networks to engineer our SCOLIONET architecture, which can accurately identify Cobb angle measurements. Empirical testing on our pipeline demonstrated a mean segmentation accuracy of 97.50% (Sorensen–Dice coefficient) and 96.30% (Intersection over Union), indicating the model’s proficiency in outlining vertebrae. The level of quantification accuracy was attributed to the state-of-the-art design of the atrous spatial pyramid pooling to better segment images. We also compared physician’s manual evaluations against our machine driven measurements to validate our approach’s practicality and reliability further. The results were remarkable, with a p-value (t-test) of 0.1713 and an average acceptable deviation of 2.86 degrees, suggesting insignificant difference between the two methods. Our work holds the premise of enabling medical practitioners to expedite scoliosis examination swiftly and consistently in improving and advancing the quality of patient care. Full article
(This article belongs to the Special Issue Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop