Department of Computer Science & Engineering, School of Computing & IT (SoCIT), Taylor's University, Subang Jaya, Malaysia.
Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia.
Front Public Health. 2022 May 30;10:879418. doi: 10.3389/fpubh.2022.879418. eCollection 2022.
Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilize for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- 1 year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers to enhance the accuracy of age estimation using OPGs from 1 year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbors (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimize the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of 1 year old, 6 months, 3 months and 1-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.
口腔颌面全景片（OPG）的年龄估计是一种医学成像技术，医生和病理学家利用它来识别疾病和处理法律事务。例如，用于估计死后时间、检测儿童虐待、毒品走私和识别无名尸体。自动化图像处理模型的最新发展提高了年龄估计的有限精度，达到了大约 +/- 1 年的近似范围。虽然这种估计通常被认为是准确的测量，但在大多数严重的情况下，如凶杀案，年龄估计应该尽可能精确。目前的年龄估计技术高度依赖于手动和耗时的图像处理。年龄估计通常是一个时间敏感的问题，图像处理时间至关重要。基于机器学习的数据处理方法的最新发展已经减少了成像时间的处理；然而，这些技术的准确性仍有待进一步提高。我们提出了一种图像分类器的集成方法，使用 OPG 从 1 岁提高到几个月（1-3-6）来提高年龄估计的准确性。这种混合模型基于卷积神经网络（CNN）和 K 最近邻（KNN）。混合（HCNN-KNN）模型用于研究来自马来西亚各教学机构和私人牙科诊所的 1922 张全景牙科射线照片。为了最大程度地减少模型过拟合的机会，我们使用主成分分析（PCA）算法消除了具有高相关性的特征。为了进一步提高我们混合模型的性能，我们进行了系统的图像预处理。我们应用了一系列分类来训练我们的模型。我们已经成功地证明，结合这些创新方法可以提高分类和分割的性能，从而改善模型的年龄估计结果。我们的研究结果表明，我们的创新模型首次成功地估计了分类研究中 1 岁、6 个月、3 个月和 1 个月大的病例的年龄，准确率分别为 99.98%、99.96%、99.87%和 98.78%。
