{"title":"尝试使用人工智能分析下颌畸形患者的面部照片","authors":"Takao Kato","doi":"10.1016/j.ajoms.2023.11.008","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Here, the feasibility of orthodontic surgery<span> was determined by obtaining cephalometric and model analyses. To make predictions easier without using these methods, I examined a plan in which AI judged whether orthodontic surgery was possible.</span></p></div><div><h3>Methods</h3><p><span>This study was approved by the bioethics committee of our hospital (No. I459). We included 1766 patients with suspected jaw deformity among those who visited the outpatient clinic of the Department of Oral and Maxillofacial Surgery in Kanazawa Medical University from August 2004 to October 2019. I used </span>medical records<span> to divide patients into those who underwent orthodontic surgery and those who underwent conservative orthodontic treatment<span>. Side profile photographs of the patients were used to train the AI system, and the untrained data were analyzed by AI.</span></span></p></div><div><h3>Results</h3><p>The AI judgments for surgical orthodontic surgery of jaw deformity in patients aged 16 years or younger, aged 17 years or older, and of all ages were 0.630, 0.668, and 0.768 (ROC curves), respectively.</p></div><div><h3>Conclusion</h3><p>The AI judgement of orthodontic surgery for jaw deformities in patients has high accuracy. Further improvements will require a larger number of subjects, the use of subject scope, image processing methods, and a loss function system.</p></div>","PeriodicalId":45034,"journal":{"name":"Journal of Oral and Maxillofacial Surgery Medicine and Pathology","volume":"36 4","pages":"Pages 478-482"},"PeriodicalIF":0.4000,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An attempt to analyze facial photographs of patients with jaw deformity using artificial intelligence\",\"authors\":\"Takao Kato\",\"doi\":\"10.1016/j.ajoms.2023.11.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Here, the feasibility of orthodontic surgery<span> was determined by obtaining cephalometric and model analyses. To make predictions easier without using these methods, I examined a plan in which AI judged whether orthodontic surgery was possible.</span></p></div><div><h3>Methods</h3><p><span>This study was approved by the bioethics committee of our hospital (No. I459). We included 1766 patients with suspected jaw deformity among those who visited the outpatient clinic of the Department of Oral and Maxillofacial Surgery in Kanazawa Medical University from August 2004 to October 2019. I used </span>medical records<span> to divide patients into those who underwent orthodontic surgery and those who underwent conservative orthodontic treatment<span>. Side profile photographs of the patients were used to train the AI system, and the untrained data were analyzed by AI.</span></span></p></div><div><h3>Results</h3><p>The AI judgments for surgical orthodontic surgery of jaw deformity in patients aged 16 years or younger, aged 17 years or older, and of all ages were 0.630, 0.668, and 0.768 (ROC curves), respectively.</p></div><div><h3>Conclusion</h3><p>The AI judgement of orthodontic surgery for jaw deformities in patients has high accuracy. Further improvements will require a larger number of subjects, the use of subject scope, image processing methods, and a loss function system.</p></div>\",\"PeriodicalId\":45034,\"journal\":{\"name\":\"Journal of Oral and Maxillofacial Surgery Medicine and Pathology\",\"volume\":\"36 4\",\"pages\":\"Pages 478-482\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Oral and Maxillofacial Surgery Medicine and Pathology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221255582300251X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Oral and Maxillofacial Surgery Medicine and Pathology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221255582300251X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
An attempt to analyze facial photographs of patients with jaw deformity using artificial intelligence
Purpose
Here, the feasibility of orthodontic surgery was determined by obtaining cephalometric and model analyses. To make predictions easier without using these methods, I examined a plan in which AI judged whether orthodontic surgery was possible.
Methods
This study was approved by the bioethics committee of our hospital (No. I459). We included 1766 patients with suspected jaw deformity among those who visited the outpatient clinic of the Department of Oral and Maxillofacial Surgery in Kanazawa Medical University from August 2004 to October 2019. I used medical records to divide patients into those who underwent orthodontic surgery and those who underwent conservative orthodontic treatment. Side profile photographs of the patients were used to train the AI system, and the untrained data were analyzed by AI.
Results
The AI judgments for surgical orthodontic surgery of jaw deformity in patients aged 16 years or younger, aged 17 years or older, and of all ages were 0.630, 0.668, and 0.768 (ROC curves), respectively.
Conclusion
The AI judgement of orthodontic surgery for jaw deformities in patients has high accuracy. Further improvements will require a larger number of subjects, the use of subject scope, image processing methods, and a loss function system.