Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

Orhan, Kaan; Bayrakdar, İ̇brahim Şevki; Ezhov, Matvey; Kravtsov, Alexander; Özyürek, Taha

doi:10.1111/iej.13265

Cited by 181 publications

(162 citation statements)

References 53 publications

(72 reference statements)

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“…Focusing on the binary presence or absence of lesions, DL identified proximal carious lesions from near-infrared transillumination images with an area under the receiver operating characteristic curve of 0.856 (Casalegno et al 2019). Volumetric measurement in CBCT, following DL-based segmentation, was reported to be comparable to the results obtained from manual segmentation of periapical lesions (Orhan et al 2020). However, this research failed to report outcomes such as the volume deviation of lesions and the Intersection over Union metric.…”

Section: Applications Of Ai In Dentistrymentioning

confidence: 63%

Application of Artificial Intelligence in Dentistry

2020

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Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.

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Section: Applications Of Ai In Dentistrymentioning

confidence: 63%

Application of Artificial Intelligence in Dentistry

2020

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“…Orhan et al verified the performance of a deep learning algorithm using CBCT images to detect and volumetrically measure periapical lesions [28]. A detection rate of 92.8% and a significant positive correlation between the automated and manual measurements were reported.…”

Section: Automated Diagnosis Of Dental and Maxillofacial Diseasesmentioning

confidence: 99%

“…Table 1 exhibits the included studies regarding the use of AI for 3D imaging in DMFR. These studies focused on three main applications, including automated diagnosis of dental and maxillofacial diseases [16][17][18][19][20][28][29][30][31][32], localization of anatomical landmarks for orthodontic and orthognathic treatment planning [21,22,[33][34][35], and improvement of image quality [23,36].…”

Section: Current Use Of Ai For 3d Imaging In Dmfrmentioning

confidence: 99%

Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

Hung

Yeung

Tanaka

et al. 2020

IJERPH

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The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

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“…It offers distinct advantages including lower radiation doses, compared to medical CT, and the potential of importing and exporting individualized, DICOM and overlap-free reconstructed data to and from other applications [4][5][6][7] . CBCT can supply high-resolution three-dimensional (3D) images without distortion and superimposition of bone and other dental structures that can be seen in conventional radiography [8][9] .…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Cbct-Based Artificial Intelligence System for Accurate Diagnoses of Dental Diseases

Ezhov¹,

Gusarev²,

Golitsyna³

et al. 2021

Preprint

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Cone-beam computed tomography (CBCT) in dental practice is becoming increasingly popular. However, the correct teeth identification, positioning and diagnosis based on CBCT can be tedious and challenging for the untrained eye. This is due to additional training, specific knowledge and time required for analysis and diagnosis. When compared to conventional dental imaging methods. In this study, we introduce a novel artificial intelligence (AI) system that facilitates analysis and diagnosis. This system is based on deep learning approaches that can localize teeth and define pathologies within three-dimensional CBCT scans. The study showed that the diagnostic performance of AI system image interpretation reaches and sometimes exceeds in comparison to clinician’s expertise. In this randomized cross-over trial we demonstrated a significant improvement of aided diagnostic accuracy for various dental diseases in comparison to a group of radiologists that made unaided decisions. AI can be used for both stand-alone CBCT interpretation and as a decision support system to improve quality of diagnostics and time efficiency.

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Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

Cited by 181 publications

References 53 publications

Application of Artificial Intelligence in Dentistry

Application of Artificial Intelligence in Dentistry

Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

Development and Validation of a Cbct-Based Artificial Intelligence System for Accurate Diagnoses of Dental Diseases

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