Thesis supervisor: Dániel Palkovics
Location of studies (in Hungarian): SE Abbreviation of location of studies: SE
Description of the research topic:
Recent technological advancements have enabled the application of computer technologies to be utilized during the planning and execution of treatment modalities in various fields of dentistry. Technological achievements, such as digital image processing, computer-aided design (3D modeling), computer-aided manufacturing (additive, subtractive) and various branches of artificial intelligence (AI) are more and more frequently incorporated into diagnostic and treatment processes. In periodontology (1), dentoalveolar surgery, implant dentistry (2), endodontic surgery (3), and maxillo-facial surgery (4), procedures can be planned virtually, and navigated surgeries can be performed with the aid of static or dynamic guide systems. The application of chair-side systems and the increased availability of 3D printers facilitates the point-of-care production of anatomical models, surgical guides, prostheses, and orthodontic splints.
A computer-aided workflow for surgical planning and execution has previously been published by Matsumoto et al. (2015) affiliated with the Mayo Clinic (Rochester, Minnesota, USA). In our previous work certain elements of this workflow were implemented into the digital planning process of surgical procedures in periodontology and implant dentistry. Our current workflow consists of the following steps: (i) digital data acquisition, (ii) digital data processing, (iii) virtual surgical planning, (iv) computer-aided surgical procedure, and (v) three-dimensional postoperative evaluation.
1. Digital data acquisition
Diagnostic approaches in the field of dentistry and oral surgery are well established. Conventional radiographic methods and clinical examination protocols certainly have their limitations. In recent years many disciplines have utilized 3D imaging methods to overcome limitations of conventional methods. The most utilized digital data acquisition modalities in dentistry are: (i) cone-beam computed tomography (CBCT) and intraoral optical scanning (IOS).
2. Digital data processing
The algorithm for digital data processing utilizing CBCT and IOS data was previous described in methods articles and know-how descriptions. The three major steps are: (i) CBCT segmentation, (ii) spatial registration of CBCT and IOS data, (iii) model fusion. Previously manual and semi-automatic methods were used, however with the application of AI-based methods these processes can largely be automated. Incorporation of AI-based methods reduces human labor and errors that derive from manual input.
3. Virtual surgical planning
Different surgical modalities require different aspects to be planned. For example, in periodontal regenerative procedures surgical planning can be done completely virtually, meaning these procedures mostly do not require the use of 3D printed surgical stents or guides, whereas surgical guides are necessary for navigated implant placement (static).
4. Computer-aided surgical procedure
Similarly to the previous step, the process for surgical execution largely depends on the surgical procedure.
5. Three-dimensional postoperative evaluation
Digital technologies have provided new ways to interpret the results of various surgical procedures. 3D modelling does not only allow volumetric analysis, but also visualizes morphological tissue alterations in three-dimensions. Therefore, providing an in-depth view of healing dynamics after surgical procedures.
Our aim is to investigate different aspects of digital planning and computer-aided treatment processes in the field of periodontal and dento-alveolar rehabilitation based on our previous research. There are still some limitations that must be addressed and improved.
Referenciák
1. Cui Z, Fang Y, Mei L, Zhang B, Yu B, Liu J, Jiang C, Sun Y, Ma L, Huang J, Liu Y, Zhao Y, Lian C, Ding Z, Zhu M, Shen D. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun. 2022 Apr 19;13(1):2096. doi: 10.1038/s41467-022-29637-2. PMID: 35440592; PMCID: PMC9018763.
2. Gerhardt MDN, Fontenele RC, Leite AF, Lahoud P, Van Gerven A, Willems H, Smolders A, Beznik T, Jacobs R. Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks. J Dent. 2022 Jul;122:104139. doi: 10.1016/j.jdent.2022.104139. Epub 2022 Apr 21. PMID: 35461974.
3. Mangano FG, Admakin O, Lerner H, Mangano C. Artificial intelligence and augmented reality for guided implant surgery planning: A proof of concept. J Dent. 2023 Jun;133:104485. doi: 10.1016/j.jdent.2023.104485. Epub 2023 Mar 23. PMID: 36965859.
4. Cucchi A, Bettini S, Corinaldesi G. A novel technique for digitalisation and customisation of reinforced polytetrafluoroethylene meshes: Preliminary results of a clinical trial. Int J Oral Implantol (Berl). 2022 May 13;15(2):129-146. PMID: 35546723.
5. Palkovics D, Solyom E, Molnar B, Pinter C, Windisch P. Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures. J Vis Exp. 2021 Aug 5;(174). doi: 10.3
Deadline for application: 2025-02-01
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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