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 Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review

“…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.…”

“…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.…”

“…The major weaknesses for most of the selected and analyzed studies were the variation of indexes used for result presentation [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ], the absence of clear exclusion criteria [ 37 , 38 , 39 , 42 , 43 ], and poor explanation of the reference test [ 37 , 39 , 42 , 43 ]. These weaknesses mainly affect the studies’ duplication process that is essential according to the standards for reporting of diagnostic accuracy studies (STARD) guidelines [ 44 ].…”

Three-Dimensional Virtual Anatomy as a New Approach for Medical Student’s Learning

“…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 ].…”

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

“…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 ].…”