摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsR X记者在越南河内的新闻报道，研究表明：“以前基于深度学习的研究主要是在根尖周病变的检测上进行的，分类信息有限，如根尖周指数(PAI)评分系统。本研究采用了两种深度学习模型，即快速R-CNN和YOLOv4.”在使用来自牙弓三个不同区域的根尖周X线片(PR)的PAI评分来检测和分类根尖周病变:前牙,前磨牙和磨牙。记者从河内科技大学的研究中获得一句话:“在2658个PR中,2122个PR用于训练,268个PR用于验证,268个PR用于测试,以有经验的牙医的诊断作为诊断参考,快速的R-CNN和YOLOv4模型具有很高的敏感性、特异性和敏感性。”R-CNN对PAI 3、PAI 4、PAI 5病变的预测率分别为89%、83.01%和91.84%，YOLOv4的相关值分别为68.06%、50.94%和65.31%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Hanoi, Vietnam, by NewsR x journalists, research stated, "Previous deep learning-based studies were mainl y conducted on detecting periapical lesions; limited information in classificati on, such as the periapical index (PAI) scoring system, is available. The study a imed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting an d classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars." The news correspondents obtained a quote from the research from the Hanoi Univer sity of Science and Technology, "Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference d iagnosis. The Faster R-CNN and YOLOv4 models obtained great sensitivity, specifi city, accuracy, and precision for detecting periapical lesions. No clear differe nce in the performance of both models among these three regions was found. The t rue prediction of Faster R-CNN was 89%, 83.01% and 91 .84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresp onding values of YOLOv4 were 68.06%, 50.94%, and 65.31 %."
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