背景:获得口腔医疗保健在全球范围内并不统一,特别是在资源有限的农村地区,这限制了自动诊断和先进的远程牙科应用的潜力。通过照相通信使用数字龋齿检测和进展监测,受到在此类设置中难以标准化的多个变量的影响。这项研究的目的是开发一种新颖且具有成本效益的虚拟计算机视觉AI系统,以合理的临床准确性从非标准化照片中预测牙洞。
方法:从233个去识别的牙齿标本中获得了一组1703个增强图像。使用消费者智能手机获取图像,没有任何标准化的设备应用。这项研究利用了最先进的合奏建模,测试时间增加,和迁移学习过程。“你只看一次”算法(YOLO)衍生物,v5s,v5m,v5l,和v5x,进行了独立评估,增强了最佳结果的集合,以及使用ResNet50、ResNet101、VGG16、AlexNet学习的传输,和DenseNet。使用精确度评估结果,召回,和平均平均精度(MAP)。
结果:YOLO模型集合的平均精度(mAP)为0.732,准确率为0.789,召回率为0.701。当转移到VGG16时,最终模型的诊断准确率为86.96%,准确率为0.89,召回率为0.88。这超过了所有其他从免费的非标准化智能手机照片中进行物体检测的基本方法。
结论:虚拟计算机视觉AI系统,混合模型集合,测试时间增加,转移了深度学习过程,开发用于从具有合理临床准确性的非标准化照片中预测牙洞。这种模式可以改善农村地区在资源有限的情况下获得口腔保健的机会,并有可能帮助自动诊断和先进的远程牙科应用。
Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy.
A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The \"you only look once\" algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP).
The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs.
A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications.