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This is a situation that makes challenges of dentistry practice and still awaits a solution. Many specialists and general practitioners have not received extensive training on radiographic image evaluation and not competent in detailed implant planning and interpretation of anatomical data.
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There are studies in the literature where this method, which also enables the processing of more complex images such as CBCT images, has used in various diagnostics in dentistry such as tooth numbering, periapical pathosis, and mandibular canal detection. A deep convolutional neural network method (DCNN) is a powerful deep learning application used on medical diagnostic images. Then the development of this system has gained momentum in many fields of medicine and its use also has become widespread in health sectors such as dentistry in recent years. In radiological diagnostic clinics, using the AI has provided to emerge the computer-aided diagnosis (CAD) systems. It imitates human intelligence and improves its these features acquired over time using the deep learning methods. Īrtificial intelligence (AI) is a field of computer science aimed at performing various specific functions that require human intelligence. Nevertheless, the physician's knowledge, skills, and experience in the interpretation of CBCT images also play very great roles in performing detailed implant planning. It is known that CBCT devices are very successful in determining the ideal implant sizes (i.e., length and width) before the operation and in predicting the necessary extra surgical procedures (i.e., guided tissue regeneration, splitting, sinus elevation) in case of insufficient bone in the operation site. It also offers high-quality images at a lower radiation dose and short scanning time. CBCT devices developed for dentomaxillofacial imaging, have more affordable prices and smaller device sizes than CT devices. Cross-sectional tomograms such as computed tomography (CT) and cone-beam computed tomography (CBCT) which offer three-dimensional (3D) information to surgeons are currently used as an alternative to these conventional techniques.
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Panoramic and intraoral radiographs are used still in dental implant practices to provide an overview of the jaws and to create a preliminary idea but these radiographic techniques are insufficient for detailed implant planning. For this purpose, in implant surgery, the various radiographic techniques are used to evaluate alveolar bone features (bone quality, thickness, and height) and anatomical variations in the operation area (such as nasal fossa, mandibular canal, mental foramen and sinuses). Detailed planning before the implant operation increases the success of the treatment due to the facility of placing in the correct position of the implant and eliminating the surgical risks. Conclusionsĭevelopment 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.ĭental implants have been preferred by clinicians for many years in cases of the total, partial and single-tooth edentulism. Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions.
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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). 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). The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. 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. Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. 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. Seventy-five CBCT images were included in this study. 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.