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Diagnostics
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Advances in Biomedical and Dental Diagnostics Using Artificial Intelligence
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Advances in Biomedical and Dental Diagnostics Using Artificial Intelligence

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 May 2023) | Viewed by 9069

Special Issue Editor

Dr. Vahid Rakhshan

E-Mail Website
Guest Editor
Department of Cognitive Neuroscience, Institute for Cognitive Science Studies, Tehran, Iran
Interests: medical imaging; cognitive neuroscience; artificial intelligence; EEG; radiology; computer programming; dentistry

Special Issue Information

Dear Colleagues,

Biomedical and dental diagnosis requires the recognition and judgment of patterns in various signals (such as images, sounds, or voltage fluctuations) and comparing them with libraries of normal and pathological patterns in order to categorize the observed signals as healthy or not, that is various forms and degrees of diseases and disorders. Sometimes, this is quite time-consuming or tiresome. For instance, evaluating a lateral cephalogram manually would take a lot of precious time. Also, a busy and tired clinician is more prone to make inaccurate and hasty diagnoses. Computerized methods may help in this regard. For example, artificial intelligence applications in several fields (such as computer vision and image processing as well as any other signal processing methods) may assist the clinician by performing a great deal of the pre-diagnostic tasks as well as increasing the speed of diagnosis itself, or improving diagnostic accuracy.

Image processing adopts methods used to manipulate or improve digital images, in order to make them more practical for the task at hand. Computer vision uses various sets of algorithms and methods to allow the computer to “understand” the visual signal. These may count as forms of computer-assisted diagnostics. However, diagnostics are not limited to visual input and may be applied to many other signals such as EEG, ECG, or sound.

This special issue aims at publishing high-quality original or review articles on any technologies that can improve disease diagnosis in any dental or biomedical fields including, but not limited to dental or medical radiography or microscopy, brain mapping and neurological or psychiatric diagnosis via MRI, fMRI, EEG, or ERP diagnostics, as well as any other potential applications for dental or biomedical diagnostics.

Dr. Vahid Rakhshan
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

  • diagnostics
  • artificial intelligence
  • machine learning
  • deep learning
  • image processing
  • signal processing
  • imaging
  • brain mapping
  • radiology
  • dentistry
  • medicine

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Published Papers (2 papers)

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Research

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12 pages, 970 KiB  
Article
Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records
by Jay S. Patel, Krishna Kumar, Ahad Zai, Daniel Shin, Lisa Willis and Thankam P. Thyvalikakath
Diagnostics 2023, 13(6), 1028; https://doi.org/10.3390/diagnostics13061028 - 8 Mar 2023
Cited by 6 | Viewed by 2636
Abstract
Objective: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). Methods: [...] Read more.
Objective: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. Results: Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients’ disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods. Full article
(This article belongs to the Special Issue Advances in Biomedical and Dental Diagnostics Using Artificial Intelligence)
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Review

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30 pages, 1955 KiB  
Review
A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health
by Imran Shafi, Anum Fatima, Hammad Afzal, Isabel de la Torre Díez, Vivian Lipari, Jose Breñosa and Imran Ashraf
Diagnostics 2023, 13(13), 2196; https://doi.org/10.3390/diagnostics13132196 - 28 Jun 2023
Cited by 11 | Viewed by 5995
Abstract
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process [...] Read more.
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues. Full article
(This article belongs to the Special Issue Advances in Biomedical and Dental Diagnostics Using Artificial Intelligence)
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