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Diagnostics
Special Issues
Evolutions in Musculoskeletal Imaging
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Evolutions in Musculoskeletal Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (10 November 2022) | Viewed by 3282

Special Issue Editor

Dr. Aristeidis H. Zibis

E-Mail Website
Guest Editor
Department of Anatomy, Faculty of Medicine, University of Thessalia, Neofytoy 9 St., 41223 Larissa, Greece
Interests: spine; hip; osteonecrosis; bone marrow edema; musculoskeletal imaging anatomy; knee; shoulder

Special Issue Information

Dear Colleagues, 

Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, short communications, case reports, and interesting images. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles. Diagnostics is going to create a Special Issue on musculoskeletal imaging. This Special Issue will be focused on musculoskeletal imaging anatomy, new imaging techniques of musculoskeletal pathology, what each imaging modality can add to musculoskeletal diagnosis, and which modality is best for evaluation of a specific musculoskeletal pathology.

Dr. Aristeidis H. Zibis
Guest Editor

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

  • musculoskeletal imaging
  • head
  • spine
  • shoulder
  • elbow
  • wrist
  • hand
  • hip
  • knee
  • ankle
  • foot

Published Papers (1 paper)

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Research

10 pages, 1264 KiB  
Article
Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip
by Michail E. Klontzas, Ioannis Stathis, Konstantinos Spanakis, Aristeidis H. Zibis, Kostas Marias and Apostolos H. Karantanas
Diagnostics 2022, 12(8), 1870; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12081870 - 02 Aug 2022
Cited by 3 | Viewed by 2678
Abstract
Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep learning methodology with the use of transfer learning and [...] Read more.
Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep learning methodology with the use of transfer learning and a convolutional neural network (CNN) ensemble, for the accurate differentiation between the two diseases. An augmented dataset of 210 hips with TOH and 210 hips with AVN was used to finetune three ImageNet-trained CNNs (VGG-16, InceptionResNetV2, and InceptionV3). An ensemble decision was reached in a hard-voting manner by selecting the outcome voted by at least two of the CNNs. Inception-ResNet-V2 achieved the highest AUC (97.62%) similar to the model ensemble, followed by InceptionV3 (AUC of 96.82%) and VGG-16 (AUC 96.03%). Precision for the diagnosis of AVN and recall for the detection of TOH were higher in the model ensemble compared to Inception-ResNet-V2. Ensemble performance was significantly higher than that of an MSK radiologist and a fellow (P < 0.001). Deep learning was highly successful in distinguishing TOH from AVN, with a potential to aid treatment decisions and lead to the avoidance of unnecessary surgery. Full article
(This article belongs to the Special Issue Evolutions in Musculoskeletal Imaging)
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