Diagnosis of DMFR Anatomy and Pathologies Using Deep-Learning Artificial Intelligence System
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 October 2023) | Viewed by 6016
Special Issue Editor
Interests: radiology; MRI/CBCT/CT/USG; dentistry; head and neck imaging; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
There have been many remarkable advances in conventional imaging over the past decade. Perhaps the most remarkable is the rapid conversion from film-based to digital radiographic systems through Artificial Intelligence (AI). AI can perform complex behaviors such as problem solving, decision making, and object recognition. Deep learning methods cover a group of AI methods that use multiple simple linked units to perform complex tasks. These algorithms can learn from large numbers of data instead of a set of pre-programmed directions. Convolutional neural networks (CNNs) have become the most popular deep learning model for the field of medical imaging. Radiomics refers to the high-throughput extraction of large numbers of imaging features, thus converting medical images into mineable high-dimensional data; the subsequent quantitative analysis of these data can support decision making. Radiomics aims to predict patient-specific outcomes based on high-throughput analysis and mining of advanced imaging biomarkers by machine learning algorithms. The Scope of the Thematic Issue is to give up-to-date information regarding recent developments such as OPG tooth numbering, OPG periodontal assessment, OPG caries detection, OPG anatomical landmark detection, MDCT segmentation, MDCT anatomical landmark detection, MDCT paranasal sinus pathology detection, CBCT 3D segmentation, MRI landmark detection, MRI rhinosinus pathology detection, MRI salivary gland detection, MRI- anatomical landmark detection, USG salivary gland, USG lymph node detection, USG metastasis detection, USG radiomics, oral cancers and radiomics, oral cancer deep learning, and oral mucosal pathologies and radiomics.
Prof. Dr. Kaan Orhan
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.
