Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms

Abstract

The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated using the following five machine learning models: a linear discriminant analysis, logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. In the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for classifying young ages (10–19 years) ranged from 0.85 to 0.88 for five different machine learning models. The AUC values of the older age group (50–69 years) ranged from 0.82 to 0.88, and those of adults (20–49 years) were approximately 0.73. In the six age-group classification, the best scores were also found in age groups 1 (10–19 years) and 6 (60–69 years), with mean AUCs ranging from 0.85 to 0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp Area was important for discriminating young ages (10–49 years), and L-Crown, U-Crown, L-Implant, U-Implant, and Periodontitis were used as predictors for discriminating older ages (50–69 years). We established acceptable linear and nonlinear machine learning models for a dental age group estimation using multiple maxillary and mandibular radiomorphometric parameters. Since certain radiomorphological characteristics of young and the elderly were linearly related to age, young and old groups could be easily distinguished from other age groups with automated machine learning models.

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

“…AI models developed for application in pedodontics have mainly focused on: dental plaque on primary teeth ( n = 1) [ 21 ], ECC ( n = 6) [ 25 , 26 , 27 , 28 , 29 , 30 ], fissure sealant categorization ( n = 1) [ 31 ], mesiodens and supernumerary tooth identification ( n = 6) [ 3 , 10 , 22 , 23 , 24 , 41 ], chronological age assessment ( n = 4) [ 32 , 33 , 37 , 38 ], identification of deciduous and young permanent teeth ( n = 3) [ 34 , 35 , 39 ], children’s oral Health ( n = 2) [ 7 , 20 ], and ectopic eruption ( n = 2) [ 39 , 40 ] ( Figure 3 ).…”

“…Lee, Y.H. et al [ 38 ] conducted an interesting study that used 18 radiomorphometric parameters extracted from panoramic radiographs (PRs) and focused primarily on developing ML algorithms. They observed that ML algorithms are more efficient at estimating age compared to traditional estimation.…”

“…Articles without complete texts, narrative reviews, scoping reviews, letters to the editor, opinion letters, case reports, brief communications, conference proceedings, and non-English language articles (84 articles) were all omitted ( Figure 1 ). Finally, only 25 papers met the qualifying requirements [ 3 , 7 , 10 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Risk factors for hyperglycemia in COVID‐19 patients treated with remdesivir

“…95% CI 0.640-0.671), and random forest (0.60; 95% CI 0.583-0.611) indicated acceptable model performance. 11 Detailed information regarding the parameters used for training the models is provided in Table S1.…”

Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks

“…Galibourg [ 14 ] and Tao [ 15 ] applied machine learning to the existing scoring method for age estimation; however, this method still had a large error owing to the subjective judgment of the observer. Accordingly, attempts have recently been made to estimate age without human intervention using convolutional neural networks (CNNs) [ 18 , 19 , 20 ]. CNNs have been used to diagnose diseases such as breast cancer [ 21 ], skin cancer [ 22 ], diabetic retinopathy [ 23 ], dental caries [ 24 ], and periodontal disease [ 25 ], as well as age estimations.…”