Miragall, Maximilian F. and Knoedler, Samuel and Kauke-Navarro, Martin and Saadoun, Rakan and Grabenhorst, Alex and Grill, Florian D. and Ritschl, Lucas M. and Fichter, Andreas M. and Safi, Ali-Farid and Knoedler, Leonard (2023) Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery. JOURNAL OF CLINICAL MEDICINE, 12 (21): 6843. ISSN 2077-0383
Full text not available from this repository. (Request a copy)Abstract
Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ORTHOGNATHIC SURGERY; DEEP; SEGMENTATION; CYSTS; CLASSIFICATION; CANCER; TUMOR; oral and maxillofacial surgery; oral surgery; maxillofacial surgery; OMFS; artificial intelligence; AI; deep learning; machine learning |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Mund-, Kiefer- und Gesichtschirurgie Medicine > Zentren des Universitätsklinikums Regensburg > Zentrum für Plastische-, Hand- und Wiederherstellungschirurgie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 May 2024 09:16 |
| Last Modified: | 14 May 2024 09:29 |
| URI: | https://pred.uni-regensburg.de/id/eprint/59888 |
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