State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China; Shenzhen Stomatology Hospital (Pingshan), Southern Medical University, 143 Dongzong Road, Pingshan District, Shenzhen 518118, China.
J Dent. 2022 Oct;125:104239. doi: 10.1016/j.jdent.2022.104239. Epub 2022 Jul 18.
Ectopic eruption (EE) of maxillary permanent first molars (PFMs) is among the most frequent ectopic eruption, which leads to premature loss of adjacent primary second molars, impaction of premolars and a decrease in dental arch length. Apart from oral manifestations such asdelayed eruption of PFMs and discoloration of primary second molars, panoramic radiographs can reveal EE of maxillary PFMs as well. Identifying eruption anomalies in radiographs can be strongly experience-dependent, leading us to develop here an automatic model that can aid dentists in this task and allow timelier interventions.
Panoramic X-ray images from 1480 patients aged 4-9 years old were used to train an auto-screening model. Another 100 panoramic images were used to validate and test the model.
The positive and negative predictive values of this auto-screening system were 0.86 and 0.88, respectively, with a specificity of 0.90 and a sensitivity of 0.86. Using the model to aid dentists in detecting EE on the 100 panoramic images led to higher sensitivity and specificity than when three experienced pediatric dentists detected EE manually.
Deep learning-based automatic screening system is useful and promising in the detection EE of maxillary PFMs with relatively high specificity. However, deep learning is not completely accurate in the detection of EE. To minimize the effect of possible false negative diagnosis, regular follow-ups and re-evaluation are required if necessary.
Identification of EE through a semi-automatic screening model can improve the efficacy and accuracy of clinical diagnosis compared to human experts alone. This method may allow earlier detection and timelier intervention and management.
上颌恒磨牙（PFMs）的异位萌出是最常见的异位萌出之一，可导致邻接的乳第二磨牙过早丧失、前磨牙阻生和牙弓长度减少。除了 PFM 萌出延迟和乳第二磨牙变色等口腔表现外，全景片也可以显示上颌 PFMs 的异位萌出。在 X 光片上识别萌出异常强烈依赖于经验，因此我们开发了一种自动模型，可以帮助牙医完成这项任务，并实现更及时的干预。
使用来自 1480 名 4-9 岁患者的全景 X 射线图像来训练自动筛选模型。另外 100 张全景图像用于验证和测试模型。
该自动筛选系统的阳性预测值和阴性预测值分别为 0.86 和 0.88，特异性为 0.90，敏感性为 0.86。使用该模型辅助牙医在 100 张全景图像上检测 EE，比三位经验丰富的儿科牙医手动检测 EE 的敏感性和特异性更高。
基于深度学习的自动筛选系统在检测上颌 PFMs 的 EE 方面具有较高的特异性，具有一定的有效性和应用前景。但是，深度学习在 EE 的检测中并非完全准确。为了最大限度地减少可能的假阴性诊断的影响，如果需要，应进行定期随访和重新评估。
与单纯依靠人类专家相比，通过半自动筛选模型识别 EE 可以提高临床诊断的效果和准确性。这种方法可能可以更早地发现并进行更及时的干预和管理。
