2021
DOI: 10.1186/s12880-021-00618-z
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A deep learning approach for dental implant planning in cone-beam computed tomography images

Abstract: Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jos… Show more

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Cited by 104 publications
(78 citation statements)
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“…All seven retrospective studies involve a total of 1288 human CBCT scans. Five out of seven studies used convolutional neural network algorithms [ 37 , 38 , 39 , 40 , 41 ], and in the other two studies, one used statistical shape models [ 42 ], and the other one tested a new automated method [ 43 ]. Despite the progress of AI within oral and maxillofacial radiology, the number of published studies testing AI algorithms for IAN/IANC detection on CBCT scans is relevantly low; from 2016 till the 22 of August 2021, only seven studies have been published and identified.…”
Section: Resultsmentioning
confidence: 99%
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“…All seven retrospective studies involve a total of 1288 human CBCT scans. Five out of seven studies used convolutional neural network algorithms [ 37 , 38 , 39 , 40 , 41 ], and in the other two studies, one used statistical shape models [ 42 ], and the other one tested a new automated method [ 43 ]. Despite the progress of AI within oral and maxillofacial radiology, the number of published studies testing AI algorithms for IAN/IANC detection on CBCT scans is relevantly low; from 2016 till the 22 of August 2021, only seven studies have been published and identified.…”
Section: Resultsmentioning
confidence: 99%
“…The U-net-like algorithms implemented by Diagnocat software (Diagnocat Inc, West Sacramento, CA, USA) were tested by Orhan et al [ 37 ] and Bayrakdar et al [ 39 ], respectively tested 85 and 75 CBCT scans as sample size. In each study, one oral and maxillofacial radiologist was involved in performing the reference test.…”
Section: Resultsmentioning
confidence: 99%
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“…Three-dimensional cone-beam computed tomographies of jaws were converted into DICOM format and can be observed on Anatomage, where it is possible to measure the bone height and thickness where implants were necessary. Thus, alveolar bone morphological characteristics and anatomical variations (nasal fossa, mandibular canal, mental foramen, and sinuses) could be carefully detected, which allowed to specialize the surgical implant [ 42 ].…”
Section: Resultsmentioning
confidence: 99%
“…Furthermore, there are possible applications in implantology. AI-based treatment planning in CAD/CAM implant dentistry could be of great interest in order to simplify virtual 3D treatment planning, and, consecutively, robotic insertion of dental implants using AI applications [ 30 ].…”
Section: Discussionmentioning
confidence: 99%