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Medicina
Special Issues
Imaging in Personalized Medicine: Detection, Diagnosis, and Therapy of Disease
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Imaging in Personalized Medicine: Detection, Diagnosis, and Therapy of Disease

A special issue of Medicina (ISSN 1648-9144).

Deadline for manuscript submissions: closed (1 September 2021) | Viewed by 4338

Special Issue Editors

Prof. Dr. Anna Capasso

E-Mail Website1 Website2
Guest Editor
Department of Pharmacy, University of Salerno, Salerno, Italy
Interests: addiction; neuropharmacology; neuroscience; neurology; psychiatry
Special Issues, Collections and Topics in MDPI journals
Dr. Graziella Di Grezia

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Co-Guest Editor
Radiology Department “G Criscuoli Hospital” Sant’Angelo dei Lombardi (Av) - IT
Interests: imaging; breast imaging; ultrasonography; CT; MRI; mammography; artificial intelligence; deep learning
Dr. Gianluca Gatta

E-Mail Website
Co-Guest Editor
Precision Medicine Department, University of Campania "Luigi Vanvitelli", 80127 Naples, Italy
Interests: diagnostic senology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue, we would like to enhance the role of detection and characterization, but also of post-therapy follow-up in the era of personalized medicine. Deep neural networks can help experts in diagnosis and decision-making.

Artificial Intelligence, thanks to deep learning, has revolutionized the fields of medicine with predictive analysis and computer-aided diagnosis and characterization.

The analysis and integration of data allows a personalized therapy with better results, as shown by imaging in follow-up procedures.

We encourage multidisciplinary original research contributions or review to enhance personalized patient management strategies. Pictorial essays or letters to the editor focused on these topics are also welcome.

Prof. Anna Capasso
Dr. Graziella Di Grezia
Dr. Gianluca Gatta
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. Medicina 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 1800 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

  • imaging
  • diagnosis
  • detection
  • therapy
  • pharmacology
  • radiomic
  • artificial intelligence
  • deep learning

Published Papers (1 paper)

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Research

10 pages, 1090 KiB  
Article
Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis
by Norio Yamamoto, Shintaro Sukegawa, Kazutaka Yamashita, Masaki Manabe, Keisuke Nakano, Kiyofumi Takabatake, Hotaka Kawai, Toshifumi Ozaki, Keisuke Kawasaki, Hitoshi Nagatsuka, Yoshihiko Furuki and Takashi Yorifuji
Medicina 2021, 57(8), 846; https://doi.org/10.3390/medicina57080846 - 20 Aug 2021
Cited by 14 | Viewed by 3657
Abstract
Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This [...] Read more.
Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification. Full article
(This article belongs to the Special Issue Imaging in Personalized Medicine: Detection, Diagnosis, and Therapy of Disease)
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