Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
Artificial Intelligence in Oral Diagnostics
share announcement

Artificial Intelligence in Oral Diagnostics

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 (15 October 2022) | Viewed by 16270

Special Issue Editor

Dr. Joachim Krois

E-Mail Website
Guest Editor
Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Berlin, Germany
Interests: machine learning and neural networks; dental disease; imaging and diagnostics

Special Issue Information

Dear Colleagues,

MDPI is excited to announce the publication of a Special Issue in Diagnostics focusing on the latest scientific advances in Artificial Intelligence in Oral Diagnostics. This peer-reviewed Special Issue is open for manuscripts that leverage advanced analytics, predictive modeling, and other applications of Artificial Intelligence with a special focus on machine learning and deep learning. We welcome new clinical applications and use cases as well as methods and strategies to improve diagnostic accuracies, generalizability, fairness, and explainability within the dental, oral, and craniofacial domain. Further, we invite manuscripts devoted to the development of best practices and benchmark studies to inform the establishment of guidelines, standards, and norms. The issue will be of interest to clinical researchers, data-driven physicians, data scientists, and machine learning engineers.

With the rise of the data-driven research paradigm and the democratization of access to software and algorithms, more and more AI-based use-cases and applications have been reported in the fields of dental, oral, and craniofacial diagnostic research. So far, these approaches have been particular successful with complex data structures such as imagery and text data, where expert-level performance seems to be in reach. Such a (semi-)automatized diagnostic regime would be beneficiary for patients, physicians, and the healthcare system as a whole. However, at the same time, such systems must be scrutinized for validity and generalizability and should adhere to best practices of evidence-based medicine, as by now, the most likely pathway into the daily practice seems to be side by side with the domain expert, where tasks and responsibilities are shared between human and computer intelligence.   

We invite original papers and reviews from the described fields. We in particular emphasize consistent reporting standards and recommend reviewing the paper “Artificial intelligence in dental research: Checklist for authors, reviewers, readers” by Schwendicke et al. 2021 (doi: 10.1016/j.jdent.2021.103610) before submitting your manuscript. All manuscripts submitted for consideration will be subject to the peer review process in a manner identical to the manuscripts submitted to Diagnostics.

Papers should be submitted to https://www.mdpi.com/journal/diagnostics, no later than 30 April  2022 to be given full consideration. Papers after this date will be considered for publication in the regular issue of the journal.

Dr. Joachim Krois
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.

Published Papers (4 papers)

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

Research

Jump to: Review, Other

12 pages, 2045 KiB  
Article
The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability
by Zelun Huang, Haoran Zheng, Junqiang Huang, Yang Yang, Yupeng Wu, Linhu Ge and Liping Wang
Diagnostics 2022, 12(11), 2673; https://doi.org/10.3390/diagnostics12112673 - 3 Nov 2022
Cited by 4 | Viewed by 1717
Abstract
Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT [...] Read more.
Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. Results: The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant’s stability in only 3.76 s, indicating high efficiency. Conclusions: To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Diagnostics)
►▼ Show Figures

Figure 1

15 pages, 990 KiB  
Article
Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification
by Aiham Taleb, Csaba Rohrer, Benjamin Bergner, Guilherme De Leon, Jonas Almeida Rodrigues, Falk Schwendicke, Christoph Lippert and Joachim Krois
Diagnostics 2022, 12(5), 1237; https://doi.org/10.3390/diagnostics12051237 - 16 May 2022
Cited by 10 | Viewed by 3867
Abstract
High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised [...] Read more.
High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ≥45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Diagnostics)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

20 pages, 1022 KiB  
Review
Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
by Sanjeev B. Khanagar, Khalid Alfouzan, Mohammed Awawdeh, Lubna Alkadi, Farraj Albalawi and Abdulmohsen Alfadley
Diagnostics 2022, 12(5), 1083; https://doi.org/10.3390/diagnostics12051083 - 26 Apr 2022
Cited by 21 | Viewed by 5028
Abstract
Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the [...] Read more.
Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Diagnostics)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

18 pages, 618 KiB  
Systematic Review
Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review
by Sanjeev B. Khanagar, Abdulmohsen Alfadley, Khalid Alfouzan, Mohammed Awawdeh, Ali Alaqla and Ahmed Jamleh
Diagnostics 2023, 13(3), 414; https://doi.org/10.3390/diagnostics13030414 - 23 Jan 2023
Cited by 16 | Viewed by 4109
Abstract
Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned [...] Read more.
Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Diagnostics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop