Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hacettepe University, Sihhiye, Ankara 06230, Turkey.
Department of Biostatistics, Faculty of Medicine, Erciyes University, Kayseri 38039, Turkey.
Dentomaxillofac Radiol. 2024 Jan 11;53(1):67-73. doi: 10.1093/dmfr/twad009.
Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults.
CBCT images of 236 Turkish individuals (121 males and 115 females) from 18 to 70 years of age were included. PTRs were calculated for six teeth in each individual, and a total of 1416 PTRs encompassed the study dataset. Support vector machine, classification and regression tree, and random forest (RF) models for dental age classification were employed. The accuracy of these techniques was compared. To facilitate this evaluation process, the available data were partitioned into training and test datasets, maintaining a proportion of 70% for training and 30% for testing across the spectrum of ML algorithms employed. The correct classification performances of the trained models were evaluated.
The models' performances were found to be low. The models' highest accuracy and confidence intervals were found to belong to the RF algorithm.
According to our results, models were found to be low in performance but were considered as a different approach. We suggest examining the different parameters derived from different measuring techniques in the data obtained from CBCT images in order to develop ML algorithms for age classification in forensic situations.
机器学习（ML）算法是人工智能的一部分，可用于创建更精确的算法程序，以估计个体的牙龄或定义年龄分类。本研究旨在使用 ML 算法评估牙髓/牙齿面积比（PTR）在锥形束 CT（CBCT）图像中预测成人牙龄分类的效果。
纳入了 236 名来自土耳其的个体（121 名男性和 115 名女性）的 CBCT 图像，年龄在 18 至 70 岁之间。为每个个体计算了 6 颗牙齿的 PTR，共有 1416 个 PTR 涵盖了研究数据集。使用支持向量机、分类回归树和随机森林（RF）模型进行牙龄分类。比较了这些技术的准确性。为了便于评估过程，将可用数据分为训练数据集和测试数据集，在使用的 ML 算法范围内，训练数据集的比例为 70%，测试数据集的比例为 30%。评估了经过训练的模型的正确分类性能。
发现模型的性能较低。RF 算法的模型表现出最高的准确性和置信区间。
根据我们的结果，发现模型的性能较低，但被认为是一种不同的方法。我们建议检查从 CBCT 图像中获得的数据中不同测量技术得出的不同参数，以便为法医情况下的年龄分类开发 ML 算法。
