Y M Sillmann, J L G C Monteiro, P Eber, A M P Baggio, Z S Peacock, F P S Guastaldi
{"title":"Empowering surgeons: will artificial intelligence change oral and maxillofacial surgery?","authors":"Y M Sillmann, J L G C Monteiro, P Eber, A M P Baggio, Z S Peacock, F P S Guastaldi","doi":"10.1016/j.ijom.2024.09.004","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial Intelligence (AI) can enhance the precision and efficiency of diagnostics and treatments in oral and maxillofacial surgery (OMS), leveraging advanced computational technologies to mimic intelligent human behaviors. The study aimed to examine the current state of AI in the OMS literature and highlight the urgent need for further research to optimize AI integration in clinical practice and enhance patient outcomes. A scoping review of journals related to OMS focused on OMS-related applications. PubMed was searched using terms \"artificial intelligence\", \"convolutional networks\", \"neural networks\", \"machine learning\", \"deep learning\", and \"automation\". Ninety articles were analyzed and classified into the following subcategories: pathology, orthognathic surgery, facial trauma, temporomandibular joint disorders, dentoalveolar surgery, dental implants, craniofacial deformities, reconstructive surgery, aesthetic surgery, and complications. There was a significant increase in AI-related studies published after 2019, 95.6% of the total reviewed. This surge in research reflects growing interest in AI and its potential in OMS. Among the studies, the primary uses of AI in OMS were in pathology (e.g., lesion detection, lymph node metastasis detection) and orthognathic surgery (e.g., surgical planning through facial bone segmentation). The studies predominantly employed convolutional neural networks (CNNs) and artificial neural networks (ANNs) for classification tasks, potentially improving clinical outcomes.</p>","PeriodicalId":94053,"journal":{"name":"International journal of oral and maxillofacial surgery","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of oral and maxillofacial surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.ijom.2024.09.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial Intelligence (AI) can enhance the precision and efficiency of diagnostics and treatments in oral and maxillofacial surgery (OMS), leveraging advanced computational technologies to mimic intelligent human behaviors. The study aimed to examine the current state of AI in the OMS literature and highlight the urgent need for further research to optimize AI integration in clinical practice and enhance patient outcomes. A scoping review of journals related to OMS focused on OMS-related applications. PubMed was searched using terms "artificial intelligence", "convolutional networks", "neural networks", "machine learning", "deep learning", and "automation". Ninety articles were analyzed and classified into the following subcategories: pathology, orthognathic surgery, facial trauma, temporomandibular joint disorders, dentoalveolar surgery, dental implants, craniofacial deformities, reconstructive surgery, aesthetic surgery, and complications. There was a significant increase in AI-related studies published after 2019, 95.6% of the total reviewed. This surge in research reflects growing interest in AI and its potential in OMS. Among the studies, the primary uses of AI in OMS were in pathology (e.g., lesion detection, lymph node metastasis detection) and orthognathic surgery (e.g., surgical planning through facial bone segmentation). The studies predominantly employed convolutional neural networks (CNNs) and artificial neural networks (ANNs) for classification tasks, potentially improving clinical outcomes.