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Diagnostics
Special Issues
Artificial Intelligence and Augmented Reality in Diagnostic Radiology
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Artificial Intelligence and Augmented Reality in Diagnostic Radiology

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 (30 November 2022) | Viewed by 1706

Special Issue Editor

Prof. Dr. Alessandro Bartoloni

E-Mail Website
Guest Editor
Istituto Nazionale di Fisica Nucleare - INFN, Rome, Italy
Interests: clinical decision- support systems; augmented reality; space radiation; space radiobiology

Special Issue Information

Dear Colleagues,

Diagnostics in radiology are now essential in many clinical fields to ensure effective and reliable precision medicine. Advanced techniques are available from state-of-the-art computer science and data analysis methodologies to create useful artificial intelligence software tools.

Another step forward is now arising thanks to new hardware and sensor technology that offer augmented reality tools to assist real-time operations. 

This Special Issue aims to summarize the research activities in these two aspects of science and technology in the diagnostic radiology field, as well as in radiotherapy treatment rooms and nuclear medicine clinic protocol implementations.

This Special Issue accepts original research and review papers relating (but not limited) to the following topics:

  • Artificial Intelligence systems and machine learning applications in diagnostic radiology.
  • Augmented and virtual reality sensors and devices for use in the clinical radiology operational environment.
  • Integrated systems for automatic or robotic support in radiology, radiotherapy, and nuclear medicine clinics. 

Prof. Dr. Alessandro Bartoloni
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

  • diagnostic radiology
  • radiotherapy
  • nuclear medicine
  • artificial intelligence and machine learning
  • augmented and virtual reality
  • clinical robotic
  • clinical decision-support systems
  • health informatics
  • radiation detection
  • medical imaging

Published Papers (1 paper)

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Research

12 pages, 1625 KiB  
Article
Computer-Aided Breast Surgery Framework Using a Markerless Augmented Reality Method
by Seungwoo Khang, Taeyong Park, Junwoo Lee, Kyung Won Kim, Hyunjoo Song and Jeongjin Lee
Diagnostics 2022, 12(12), 3123; https://doi.org/10.3390/diagnostics12123123 - 11 Dec 2022
Cited by 2 | Viewed by 1312
Abstract
This study proposes a markerless Augmented Reality (AR) surgical framework for breast lesion removal using a depth sensor and 3D breast Computed Tomography (CT) images. A patient mesh in the real coordinate system is acquired through a patient 3D scan using a depth [...] Read more.
This study proposes a markerless Augmented Reality (AR) surgical framework for breast lesion removal using a depth sensor and 3D breast Computed Tomography (CT) images. A patient mesh in the real coordinate system is acquired through a patient 3D scan using a depth sensor for registration. The patient mesh on the virtual coordinate system is obtained by contrast-based skin segmentation in 3D mesh generated from breast CT scans. Then, the nipple area is detected based on the gradient in the segmented skin area. The region of interest (ROI) is set based on the detection result to select the vertices in the virtual coordinate system. The mesh on the real and virtual coordinate systems is first aligned by matching the center of mass, and the Iterative Closest Point (ICP) method is applied to perform more precise registration. Experimental results of 20 patients’ data showed 98.35 ± 0.71% skin segmentation accuracy in terms of Dice Similarity Coefficient (DSC) value, 2.79 ± 1.54 mm nipple detection error, and 4.69 ± 1.95 mm registration error. Experiments using phantom and patient data also confirmed high accuracy in AR visualization. The proposed method in this study showed that the 3D AR visualization of medical data on the patient’s body is possible by using a single depth sensor without having to use markers. Full article
(This article belongs to the Special Issue Artificial Intelligence and Augmented Reality in Diagnostic Radiology)
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