Abstract：
BACKGROUND: Preoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation-based deep learning (DL) system using a panoramic radiograph to predict the difficulty level of surgical removal of an impacted LM3.
METHODS: The study included 1367 LM3 images from 784 patients who presented from 2021-2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3.
RESULTS: The ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels.
CONCLUSIONS: The development of a 3-phase computer-aided visualisation-based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.
METHODS: The study included 1367 LM3 images from 784 patients who presented from 2021-2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3.
RESULTS: The ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels.
CONCLUSIONS: The development of a 3-phase computer-aided visualisation-based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.
摘要：
背景：全景X线片中下颌阻生第三磨牙（LM3）的术前评估在手术计划中很重要。这项研究的目的是使用全景X射线照片开发和评估基于计算机辅助可视化的深度学习（DL）系统，以预测手术切除受影响的LM3的难度。
方法：该研究包括2021-2023年到大学牙科医院就诊的784名患者的1367张LM3图像；回顾性收集图像。通过我们新开发的DL系统评估了手术切除受影响的LM3的难度水平，无缝集成了3种不同的DL模型。ResNet101V2处理了用于识别全景射线照片中受影响的LM3的二进制分类，RetinaNet检测到受影响的LM3的精确位置，VisionTransformer执行了多类别图像分类，以评估删除检测到的受影响的LM3的难度。
结果：ResNet101V2模型实现了0.8671的分类精度。RetinaNet模型展示了卓越的检测性能，平均精度为0.9928。此外,VisionTransformer模型在预测移除难度级别方面的平均准确度为0.7899。
结论：基于3阶段计算机辅助可视化的DL系统的开发在使用全景射线照片预测手术去除受影响的LM3的难度方面取得了非常好的性能。
方法：该研究包括2021-2023年到大学牙科医院就诊的784名患者的1367张LM3图像；回顾性收集图像。通过我们新开发的DL系统评估了手术切除受影响的LM3的难度水平，无缝集成了3种不同的DL模型。ResNet101V2处理了用于识别全景射线照片中受影响的LM3的二进制分类，RetinaNet检测到受影响的LM3的精确位置，VisionTransformer执行了多类别图像分类，以评估删除检测到的受影响的LM3的难度。
结果：ResNet101V2模型实现了0.8671的分类精度。RetinaNet模型展示了卓越的检测性能，平均精度为0.9928。此外,VisionTransformer模型在预测移除难度级别方面的平均准确度为0.7899。
结论：基于3阶段计算机辅助可视化的DL系统的开发在使用全景射线照片预测手术去除受影响的LM3的难度方面取得了非常好的性能。
