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Yu H, Cao Z, Pang G, Wu F, Zhu H, Zhu F. A Deep-learning System for Diagnosing Ectopic Eruption. J Dent 2024:105399. [PMID: 39424256 DOI: 10.1016/j.jdent.2024.105399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 10/01/2024] [Accepted: 10/06/2024] [Indexed: 10/21/2024] Open
Abstract
OBJECTIVES To construct a diagnostic model for mixed dentition using a multistage deep-learning network to predict potential ectopic eruption in permanent teeth by integrating dentition segmentation into the process of automatic classification of dental development stages. METHODS A database was established by reviewing 1576 anonymous panoramic radiographs of children aged 6-12 years, collected at the Stomatology Hospital, xxxxxx. These radiographs were categorised as normal or ectopic eruption, with expert diagnoses serving as a benchmark for training and evaluating artificial intelligence (AI) models. Furthermore, tooth boundaries and dental development stages were manually annotated by three pediatric dentistry experts. The dataset was split into training, validation, and test sets at an 8:1:1 ratio. RESULTS The diagnostic performance of the deep-learning model was rigorously evaluated. The model demonstrated accuracy in tooth segmentation, with Intersection over Union, precision, sensitivity, and F1 scores of 0.959, 0.993, 0.966, and 0.979, respectively. Furthermore, its ability to identify tooth ectopic eruptions on panoramic radiographs, when compared to evaluations by three dentists. Based on McNemar's test, the model's specificity and accuracy in identifying ectopic tooth eruptions on the test dataset surpassed that of Dentist 1 (P < 0.05), while no significant difference was observed compared to the other two dentists. Besides, the deep learning model also showed its potential in classifying dental development stages, as tested against three different standards. CONCLUSIONS The adaptability of the AI-enabled model in this study was demonstrated across multiple scenarios, with clinical validation highlighting its efficacy in diagnosing ectopic eruptions using a multistage deep-learning approach. CLINICAL SIGNIFICANCE Our findings provide new insights and technical support for the prevention and treatment of abnormal tooth eruption, laying the groundwork for predictive models for other prevalent pediatric dentistry conditions.
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Affiliation(s)
- Haojie Yu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zheng Cao
- School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China
| | - Gaozhi Pang
- College of Computer Science and Technology, Zhejiang University of Technology.Hangzhou, Zhejiang, 310023, China
| | - Fuli Wu
- College of Computer Science and Technology, Zhejiang University of Technology.Hangzhou, Zhejiang, 310023, China
| | - Haihua Zhu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Fudong Zhu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China.
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Kurt A, Günaçar DN, Şılbır FY, Yeşil Z, Bayrakdar İŞ, Çelik Ö, Bilgir E, Orhan K. Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm. BMC Oral Health 2024; 24:1034. [PMID: 39227802 PMCID: PMC11370008 DOI: 10.1186/s12903-024-04786-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024] Open
Abstract
BACKGROUND This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.
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Affiliation(s)
- Ayça Kurt
- Faculty of Dentistry, Department of Pediatric Dentistry, Recep Tayyip Erdogan University, Rize, Turkey.
| | - Dilara Nil Günaçar
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Recep Tayyip Erdogan University, Rize, Turkey
| | - Fatma Yanık Şılbır
- Faculty of Dentistry, Department of Pediatric Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
| | - Zeynep Yeşil
- Faculty of Dentistry, Department of Prosthetic Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
- Faculty of Dentistry, Prosthetic Dentistry, Ataturk University, Erzurum, Türkiye
| | - İbrahim Şevki Bayrakdar
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Elif Bilgir
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Kaan Orhan
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Ankara University, Ankara, Turkey
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Bakhsh HH, Al-Shehri NA, Shahwan A, Altuwairqi R, Mojaleed FJ, Alwaalan G, Asaad S. A Comparison of Two Methods of Dental Age Estimation in a Population of Saudi Children and Adolescents. Diagnostics (Basel) 2024; 14:1935. [PMID: 39272720 PMCID: PMC11394459 DOI: 10.3390/diagnostics14171935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/23/2024] [Accepted: 08/28/2024] [Indexed: 09/15/2024] Open
Abstract
This study aimed to compare and evaluate the accuracy of the Demirjian (DE) and the London Atlas (LAE) dental age estimation methods in a Saudi population sample. This retrospective cross-sectional study used digital radiographs from electronic health records in three different dental institutes. In total, 357 male and 354 female (ages 5-15 years) digital orthopantomograms were selected for age estimation. The mean difference between the chronological age (CA) and age estimation method among males and females was 0.03 ± 0.34 and 0.00 ± 0.34, respectively, for LAE and 0.55 ± 0.84 and 0.76 ± 0.51, respectively, for DE. The mean difference between the LAE and DE methods among males and females was 0.52 ± 0.89 and -0.76 ± 0.57, respectively. No statistically significant difference between CA and LAE was found in either males (p = 0.079) or females (p = 0.872). A statistically significant difference was found between CA and DE in both genders (p < 0.001). A statistically significant difference was found between the LAE and DE groups (p < 0.001) in both genders. An overestimation of dental age was observed with DE compared with that in CA. LAE showed higher accuracy than CA, with no clinically significant difference. Although the difference between the LAE and DE methods was insignificant, the LAE method proved to be more accurate.
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Affiliation(s)
- Heba H Bakhsh
- Department of Preventive Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Nada A Al-Shehri
- Department of Preventive Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | | | - Rabab Altuwairqi
- Department of Orthodontics, King Saud Medical City, Riyadh 12746, Saudi Arabia
| | - Faten J Mojaleed
- Riyadh Second Health Cluster, Ministry of Health, Riyadh 13324, Saudi Arabia
| | | | - Shahad Asaad
- Pediatric Resident-Ministry of Health, Riyadh 12211, Saudi Arabia
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Oliveira W, Albuquerque Santos M, Burgardt CAP, Anjos Pontual ML, Zanchettin C. Estimation of human age using machine learning on panoramic radiographs for Brazilian patients. Sci Rep 2024; 14:19689. [PMID: 39181957 PMCID: PMC11344797 DOI: 10.1038/s41598-024-70621-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024] Open
Abstract
This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.
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Affiliation(s)
- Willian Oliveira
- Universidade Federal de Pernambuco, Centro de Informática - CIn, Recife, 50740-560, Brazil
| | - Mariana Albuquerque Santos
- Universidade Federal de Pernambuco, Centro de Ciências da Saúde, Departamento de Clínica e Odontologia Preventiva, Recife, 50670-901, Brazil
| | | | - Maria Luiza Anjos Pontual
- Universidade Federal de Pernambuco, Centro de Ciências da Saúde, Departamento de Clínica e Odontologia Preventiva, Recife, 50670-901, Brazil
| | - Cleber Zanchettin
- Universidade Federal de Pernambuco, Centro de Informática - CIn, Recife, 50740-560, Brazil.
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Niño-Sandoval TC, Doria-Martinez AM, Escobar RAV, Sánchez EL, Rojas IB, Álvarez LCV, Mc Cann DSF, Támara-Patiño LM. Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review. Int J Legal Med 2024; 138:1459-1496. [PMID: 38400923 DOI: 10.1007/s00414-024-03162-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/08/2024] [Indexed: 02/26/2024]
Abstract
The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.
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Affiliation(s)
- Tania Camila Niño-Sandoval
- Research center of the Institute National of Legal Medicine and Forensic Sciences, Research Institute, Faculty of Medicine, University of Antioquia, Medellin, Colombia
| | | | | | | | - Isabella Bermón Rojas
- Electronic Engineering Faculty, Department of Electronics and Telecommunications, University of Antioquia, Medellin, Colombia
| | - Laura Cristina Vargas Álvarez
- Electronic Engineering Faculty, Department of Electronics and Telecommunications, University of Antioquia, Medellin, Colombia
| | - David Stephen Fernandez Mc Cann
- Electronic Engineering Faculty, Department of Electronics and Telecommunications, University of Antioquia, Medellin, Colombia
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Khanagar SB, Albalawi F, Alshehri A, Awawdeh M, Iyer K, Alsomaie B, Aldhebaib A, Singh OG, Alfadley A. Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review. Diagnostics (Basel) 2024; 14:1079. [PMID: 38893606 PMCID: PMC11172066 DOI: 10.3390/diagnostics14111079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.
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Affiliation(s)
- Sanjeev B. Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Farraj Albalawi
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Aram Alshehri
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Mohammed Awawdeh
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Kiran Iyer
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Barrak Alsomaie
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Ali Aldhebaib
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Oinam Gokulchandra Singh
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Abdulmohsen Alfadley
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
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Sivri MB, Taheri S, Kırzıoğlu Ercan RG, Yağcı Ü, Golrizkhatami Z. Dental age estimation: A comparative study of convolutional neural network and Demirjian's method. J Forensic Leg Med 2024; 103:102679. [PMID: 38537363 DOI: 10.1016/j.jflm.2024.102679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 05/14/2024]
Abstract
The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian's method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4-17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian's method's grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian's approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian's age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.
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Affiliation(s)
- Mustan Barış Sivri
- Bahçeşehir University, Faculty of Dentistry, Department of Oral and Maxillofacial Surgery, Türkiye.
| | - Shahram Taheri
- Antalya Bilim University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Türkiye.
| | | | - Ünsun Yağcı
- Private Practice Dentist, Department of Prosthodontics, Antalya, Türkiye.
| | - Zahra Golrizkhatami
- Eastern Mediterranean University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Türkiye.
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Dai X, Liu A, Liu J, Zhan M, Liu Y, Ke W, Shi L, Huang X, Chen H, Deng Z, Fan F. Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:611-619. [PMID: 38343227 PMCID: PMC11031552 DOI: 10.1007/s10278-023-00956-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 04/20/2024]
Abstract
Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson's criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson's criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.
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Affiliation(s)
- Xinhua Dai
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Anjie Liu
- University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Junhong Liu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Mengjun Zhan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yuanyuan Liu
- Department of Oral Radiology, College of Stomatology, Sichuan University, West China, Chengdu, 610041, People's Republic of China
| | - Wenchi Ke
- College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China
| | - Lei Shi
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Xinyu Huang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Hu Chen
- College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China
| | - Zhenhua Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Fei Fan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
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Dogan OB, Boyacioglu H, Goksuluk D. Machine learning assessment of dental age classification based on cone-beam CT images: a different approach. Dentomaxillofac Radiol 2024; 53:67-73. [PMID: 38214945 PMCID: PMC11003658 DOI: 10.1093/dmfr/twad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/14/2023] [Accepted: 11/12/2023] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES 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. METHODS 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. RESULTS The models' performances were found to be low. The models' highest accuracy and confidence intervals were found to belong to the RF algorithm. CONCLUSIONS 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.
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Affiliation(s)
- Ozlem B Dogan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hacettepe University, Sihhiye, Ankara 06230, Turkey
| | - Hatice Boyacioglu
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hacettepe University, Sihhiye, Ankara 06230, Turkey
| | - Dincer Goksuluk
- Department of Biostatistics, Faculty of Medicine, Erciyes University, Kayseri 38039, Turkey
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Kahm SH, Kim JY, Yoo S, Bae SM, Kang JE, Lee SH. Application of entire dental panorama image data in artificial intelligence model for age estimation. BMC Oral Health 2023; 23:1007. [PMID: 38102578 PMCID: PMC10724903 DOI: 10.1186/s12903-023-03745-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models. METHODS Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed. RESULTS For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively. CONCLUSIONS The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.
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Affiliation(s)
- Se Hoon Kahm
- Department of Dentistry, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021, Tongil-ro, Eunpyeong-gu, Seoul, 03312, Republic of Korea
| | - Ji-Youn Kim
- Division of Oral & Maxillofacial Surgery, Department of Dentistry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-daero, Paldal-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Seok Yoo
- Unidocs Inc, 272 Digital-ro, Guro-gu, Seoul, Republic of Korea
| | - Soo-Mi Bae
- Department of Artificial Intelligence, Graduate School, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
| | - Ji-Eun Kang
- JINHAKapply Corp, 34 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Sang Hwa Lee
- Department of Dentistry, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021, Tongil-ro, Eunpyeong-gu, Seoul, 03312, Republic of Korea.
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11
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Yeom HG, Lee BD, Lee W, Lee T, Yun JP. Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population. Sci Rep 2023; 13:21857. [PMID: 38071386 PMCID: PMC10710476 DOI: 10.1038/s41598-023-48960-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
This study suggests a hybrid method based on ResNet50 and vision transformer (ViT) in an age estimation model. To this end, panoramic radiographs are used for learning by considering both local features and global information, which is important in estimating age. Transverse and longitudinal panoramic images of 9663 patients were selected (4774 males and 4889 females with a mean age of 39 years and 3 months). To compare ResNet50, ViT, and the hybrid model, the mean absolute error, mean square error, root mean square error, and coefficient of determination (R2) were used as metrics. The results confirmed that the age estimation model designed using the hybrid method performed better than those using only ResNet50 or ViT. The estimation is highly accurate for young people at an age with distinct growth characteristics. When examining the basis for age estimation in the hybrid model through attention rollout, the proposed model used logical and important factors rather than relying on unclear elements as the basis for age estimation.
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Affiliation(s)
- Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Byung-Do Lee
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Wan Lee
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Taehan Lee
- AI Research Center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu, 42994, Republic of Korea.
| | - Jong Pil Yun
- AI Research Center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu, 42994, Republic of Korea.
- University of Science and Technology, Daegu, Republic of Korea.
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12
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Shen S, Zhou Z, Wang J, Fan L, Han J, Tao J. Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar. BMC Oral Health 2023; 23:680. [PMID: 37730591 PMCID: PMC10510268 DOI: 10.1186/s12903-023-03284-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/03/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Having a reliable and feasible method to estimate whether an individual has reached 16 years of age would greatly benefit forensic analysis. The study of age using dental information has matured recently. In addition, machine learning (ML) is gradually being applied for dental age estimation. AIM The purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian3M), measure the development index of the third molar (I3M) using the method by Cameriere, and assess the periodontal ligament development of the second molar (PL2M). This study aimed to predict whether Chinese adolescents have reached the age of criminal responsibility (16 years) by combining the above measurements with ML techniques. SUBJECTS & METHODS A total of 665 Chinese adolescents aged between 12 and 20 years were recruited for this study. The development of the second and third molars was evaluated by taking orthopantomographs. ML algorithms, including random forests (RF), decision trees (DT), support vector machines (SVM), K-nearest neighbours (KNN), Bernoulli Naive Bayes (BNB), and logistic regression (LR), were used for training and testing to determine the dental age. This is the first study to combine ML with an evaluation of periodontal ligament and tooth development to predict whether individuals are over 16 years of age. RESULTS AND CONCLUSIONS The study showed that SVM had the highest Bayesian posterior probability at 0.917 and a Youden index of 0.752. This finding provides an important reference for forensic identification, and the combination of traditional methods and ML is expected to improve the accuracy of age determination for this population, which is of substantial significance for criminal litigation.
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Affiliation(s)
- Shihui Shen
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Center for Stomatology, Shanghai, People's Republic of China
- National Clinical Research Center for Oral Diseases, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China
| | - Zhuojun Zhou
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Center for Stomatology, Shanghai, People's Republic of China
- National Clinical Research Center for Oral Diseases, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China
| | - Jian Wang
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Center for Stomatology, Shanghai, People's Republic of China
- National Clinical Research Center for Oral Diseases, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China
| | - Linfeng Fan
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Center for Stomatology, Shanghai, People's Republic of China
- National Clinical Research Center for Oral Diseases, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junli Han
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- National Center for Stomatology, Shanghai, People's Republic of China.
- National Clinical Research Center for Oral Diseases, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China.
| | - Jiang Tao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- National Center for Stomatology, Shanghai, People's Republic of China.
- National Clinical Research Center for Oral Diseases, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China.
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13
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Bafei SEC, Shen C. Biomarkers selection and mathematical modeling in biological age estimation. NPJ AGING 2023; 9:13. [PMID: 37393295 DOI: 10.1038/s41514-023-00110-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/08/2023] [Indexed: 07/03/2023]
Abstract
Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.
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Affiliation(s)
- Solim Essomandan Clémence Bafei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Chong Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
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14
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Vila-Blanco N, Varas-Quintana P, Tomás I, Carreira MJ. A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches. Int J Legal Med 2023; 137:1117-1146. [PMID: 37055627 PMCID: PMC10247592 DOI: 10.1007/s00414-023-02960-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/31/2023] [Indexed: 04/15/2023]
Abstract
Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.
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Affiliation(s)
- Nicolás Vila-Blanco
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Departamento de Electrónica e Computación, Escola Técnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Paulina Varas-Quintana
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical Surgical Specialities, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Inmaculada Tomás
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical Surgical Specialities, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - María J. Carreira
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Departamento de Electrónica e Computación, Escola Técnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
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15
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Kim YR, Choi JH, Ko J, Jung YJ, Kim B, Nam SH, Chang WD. Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks. Healthcare (Basel) 2023; 11:healthcare11081068. [PMID: 37107902 PMCID: PMC10137502 DOI: 10.3390/healthcare11081068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.
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Affiliation(s)
- Yu-Rin Kim
- Department of Dental Hygiene, Silla University, 140 Baegyang-daero 700 Beon-gil, Sasang-gu, Busan 46958, Republic of Korea
| | - Jae-Hyeok Choi
- Department of Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
| | - Jihyeong Ko
- Department of Biomedical Engineering, Chonnam National University, Yeosu 59626, Republic of Korea
| | - Young-Jin Jung
- Department of Biomedical Engineering, Chonnam National University, Yeosu 59626, Republic of Korea
- School of Healthcare and Biomedical Engineering, Chonnam National University, Yeosu 59626, Republic of Korea
| | - Byeongjun Kim
- Department of Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
| | - Seoul-Hee Nam
- Department of Dental Hygiene, Kangwon National University, Samcheok 25913, Republic of Korea
| | - Won-Du Chang
- Department of Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
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16
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Pereira de Sousa D, Diniz Lima E, Souza Paulino JA, dos Anjos Pontual ML, Meira Bento P, Melo DP. Age determination on panoramic radiographs using the Kvaal method with the aid of artificial intelligence. Dentomaxillofac Radiol 2023; 52:20220363. [PMID: 36988148 PMCID: PMC10170175 DOI: 10.1259/dmfr.20220363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/30/2023] Open
Abstract
OBJECTIVES This study aimed to assess and compare age estimation on panoramic radiography using the Kvaal method and machine learning (ML). METHODS AND MATERIALS 554 panoramic radiographs were selected from a Brazilian practice. To estimate age using the Kvaal method, the following measurements were performed on the upper left central incisors and canines: tooth, pulp and root length; root and pulp width at three different levels: at the enamel-cementum junction (ECJ); midpoint between the enamel-cementum junction and; at the mid root level. For ML age estimation, radiomic, semantic and the radiomic-semantic attribute extractions were assessed. Nineteen semantic and 14 radiomic attributes and a single set of 33 semantic-radiomic attributes were extracted. Logistic Regression, Linear Regression, KNN, SVR, Decision Tree Reg, Random Forest Reg, Gradient Boost Reg e XG Boosting Reg were used for ML classification. For the Kvaal method, Mann-Whitney test, Spearman correlation coefficient, Student's t-test and linear regression with its respective coefficient of determination were used to estimate age and to assess data variability. RESULTS Mean absolute error (MAE) and standard error estimate (SEE) were assessed. For the Kvaal method, upper incisors presented higher precision than canines (R²: 0.335, SSE: 7.108). Males presented better MAE and SEE values (5.29,6.96) than females (5.69,7.37). The radiomic-semantic attributes presented superior precision (MAE: 4.77) than the radiomic and semantic (MAE: 5.23) attributes. The XG Boosting Reg classifier performed better than the other six assessed classifiers (MAE: 4.65). ML (MAE: 4.77 presented higher age estimation precision than the Kvaal method (MAE: 5.68). CONCLUSION The use of ML on panoramic radiographs can improve age estimation.
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Affiliation(s)
- Douglas Pereira de Sousa
- Department of Dentistry, State
University of Paraiba, Rua Baraúnas, 351, Bairro
Universitário, Campina Grande,
Paraíba, Brasil
| | - Elisa Diniz Lima
- Department of Dentistry, State
University of Paraiba, Rua Baraúnas, 351, Bairro
Universitário, Campina Grande,
Paraíba, Brasil
| | - José Alberto Souza Paulino
- Rua Aprígio Veloso, Federal
University of Campina Grande, RuAprígio Veloso, 882, Bairro
Universitário, Campina Grande,
Paraíba, Brazil
| | - Maria Luiza dos Anjos Pontual
- Department of Oral Diagnosis, Division
of Oral Radiology, Federal University of Pernambuco, Av. Prof. Artur de
Sá, 329-481 - CidadUniversitária,
Recife - PE, Brazil
| | - Patricia Meira Bento
- Department of Dentistry, State
University of Paraiba, Rua Baraúnas, 351, Bairro
Universitário, Campina Grande,
Paraíba, Brasil
| | - Daniela Pita Melo
- Department of Dentistry, State
University of Paraiba, Rua Baraúnas, 351, Bairro
Universitário, Campina Grande,
Paraíba, Brasil
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17
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Bui R, Iozzino R, Richert R, Roy P, Boussel L, Tafrount C, Ducret M. Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4620. [PMID: 36901630 PMCID: PMC10002153 DOI: 10.3390/ijerph20054620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/25/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert.
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Affiliation(s)
- Romain Bui
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Régis Iozzino
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Raphaël Richert
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Pascal Roy
- Service de Biostatistique—Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, 69008 Lyon, France
- Équipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, UMR 5558 CNRS, Université Claude Bernard Lyon 1, Université de Lyon, 69100 Villeurbanne, France
| | - Loïc Boussel
- Department of Radiology, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, 69004 Lyon, France
- CREATIS, INSA Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, UMR 5220, U1294, 69100 Villeurbanne, France
| | - Cheraz Tafrount
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Maxime Ducret
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
- Institut de Biologie et Chimie des Protéines, Laboratoire de Biologie Tissulaire et Ingénierie Thérapeutique, UMR 5305 CNRS, Université Claude Bernard Lyon 1, 69367 Lyon, France
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Oueriagli SN, El Asraoui L, Sahel OA, Benameur Y, Doudouh A. Snow Leopard Appearance of Subcutaneous Panniculitis such as T-cell Lymphoma on 18F-FDG PET/CT. Mol Imaging Radionucl Ther 2023; 32:77-79. [PMID: 36820276 PMCID: PMC9950679 DOI: 10.4274/mirt.galenos.2022.63644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Subcutaneous panniculitis such as T-cell lymphoma (SPTCL) is a very rare disorder. Patients usually present with multiple subcutaneous nodules on the extremities without visceral disease. Dissemination to extra-cutaneous sites is unusual. Only a few cases of SPTCL have been reported in the literature describing the findings of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). Here, we represent an interesting and unusual case of diffuse SPTCL with snow Leopard skin appearance on 18F-FDG PET/computed tomography.
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Affiliation(s)
- Salah Nabih Oueriagli
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco,* Address for Correspondence: Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco Phone: +212662101403 E-mail:
| | - Laila El Asraoui
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
| | - Omar Ait Sahel
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
| | - Yassir Benameur
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
| | - Abderrahim Doudouh
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
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19
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Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults. Sci Rep 2023; 13:726. [PMID: 36639726 PMCID: PMC9839668 DOI: 10.1038/s41598-023-28086-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/12/2023] [Indexed: 01/15/2023] Open
Abstract
Teeth are known to be the most accurate age indicators of human body and are frequently applied in forensic age estimation. We aimed to validate data mining-based dental age estimation, by comparing the accuracy of the estimation and classification performance of 18-year thresholds with conventional methods and with data mining-based age estimation. A total of 2657 panoramic radiographs were collected from Koreans and Japanese populations aged 15 to 23 years. They were subdivided into a training and internal test set of 900 radiographs each from Koreans, and an external test set of 857 radiographs from Japanese. We compared the accuracy and classification performance of the test sets from conventional methods with those from the data mining models. The accuracy of the conventional method with the internal test set was slightly higher than that of the data mining models, with a slight difference (mean absolute error < 0.21 years, root mean square error < 0.24 years). The classification performance of the 18-year threshold was also similar between the conventional method and the data mining models. Thus, conventional methods can be replaced by data mining models in forensic age estimation using second and third molar maturity of Korean juveniles and young adults.
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20
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Shen S, Yuan X, Wang J, Fan L, Zhao J, Tao J. Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods. Front Public Health 2022; 10:1068253. [PMID: 36530730 PMCID: PMC9751184 DOI: 10.3389/fpubh.2022.1068253] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Background Machine learning (ML) algorithms play a key role in estimating dental age. In this study, three ML models were used for dental age estimation, based on different preprocessing methods. Aim The seven mandibular teeth on the digital panorama were measured and evaluated according to the Cameriere and the Demirjian method, respectively. Correlation data were used for decision tree (DT), Bayesian ridge regression (BRR), k-nearest neighbors (KNN) models for dental age estimation. An accuracy comparison was made among different methods. Subjects and methods We analyzed 748 orthopantomographs (392 males and 356 females) from eastern China between the age of 5 and 13 years in this retrospective study. Three models, DT, BRR, and KNN, were used to estimate the dental age. The data in ML is obtained according to the Cameriere method and the Demirjian method. Coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE), the above five metrics were used to evaluate the accuracy of age estimation. Results Our experimental results showed that the prediction accuracy of dental age was affected by ML algorithms. MD, MAD, MSE, RMSE of the dental age predicted by ML were significantly decreased. Among all the methods, the KNN model based on the Cameriere method had the highest accuracy (ME = 0.015, MAE = 0.473, MSE = 0.340, RMSE = 0.583, R2 = 0.94). Conclusion The results show that the prediction accuracy of dental age is influenced by ML algorithms and preprocessing method. The KNN model based on the Cameriere method was able to infer dental age more accurately in a clinical setting.
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Affiliation(s)
- Shihui Shen
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xiaoyan Yuan
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China
| | - Jian Wang
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China
| | - Linfeng Fan
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China,Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junjun Zhao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China,Junjun Zhao
| | - Jiang Tao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China,*Correspondence: Jiang Tao
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21
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Efficacy of machine learning assisted dental age assessment in local population. Leg Med (Tokyo) 2022; 59:102148. [PMID: 36223694 DOI: 10.1016/j.legalmed.2022.102148] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 09/14/2022] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Although the dental age assessment is commonly applied in forensic and maturity evaluation, the long-standing dilemma from population differences has limited its application. OBJECTIVES This study aimed to verify the efficacy of the machine learning (ML) to build up the dental age standard of a local population. METHODS We retrospectively studied 2052 panoramic films retrieved from healthy Taiwanese children aged 2.6-17.7 years with comparable sizes in each age-group. The recently reported Han population-based standard (H method) served as the control condition. To develop and validate ML models, random divisions of the sample in an 80%-20% ratio repeated 20 times. The model performances were compared with the H method, Demirjian's method, and Willems's method. RESULTS The ML-assisted models provided more accurate age prediction than those non-ML-assisted methods. The range of errors was effectively reduced to less than one per year in the ML models. Furthermore, the consistent agreements among the age groups from preschool to adolescence were reported for the first time. The Gaussian process regression was the best ML model; of the non-ML modalities, the H method was the most efficacious, followed by the Demirjian's method and Willems's methods. CONCLUSION The ML-assisted dental age assessment is helpful to provide customized standards to a local population with more accurate estimations in preschool and adolescent age groups than do studied conventional methods. In addition, the earlier complete tooth developments were also observed in present study. To construct more reliable dental maturity models in the future, additional environment-related factors should be taken into account.
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22
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Wu TJ, Tsai CL, Gao QZ, Chen YP, Kuo CF, Huang YH. The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay. J Pers Med 2022; 12:1158. [PMID: 35887655 PMCID: PMC9322373 DOI: 10.3390/jpm12071158] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. METHODS The panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample t-test. RESULTS The AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys. CONCLUSION The AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders.
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Affiliation(s)
- Te-Ju Wu
- Department of Craniofacial Orthodontics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833253, Taiwan;
| | - Chia-Ling Tsai
- Department of Pedodontics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833253, Taiwan;
| | - Quan-Ze Gao
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333423, Taiwan; (Q.-Z.G.); (Y.-P.C.)
| | - Yueh-Peng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333423, Taiwan; (Q.-Z.G.); (Y.-P.C.)
| | - Chang-Fu Kuo
- Division of Rheumatology, Allergy and Immunology, Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 333423, Taiwan;
| | - Ying-Hua Huang
- Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833253, Taiwan
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Han M, Du S, Ge Y, Zhang D, Chi Y, Long H, Yang J, Yang Y, Xin J, Chen T, Zheng N, Guo YC. With or without human interference for precise age estimation based on machine learning? Int J Legal Med 2022; 136:821-831. [DOI: 10.1007/s00414-022-02796-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/08/2022] [Indexed: 11/29/2022]
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Shen S, Liu Z, Wang J, Fan L, Ji F, Tao J. Machine learning assisted Cameriere method for dental age estimation. BMC Oral Health 2021; 21:641. [PMID: 34911516 PMCID: PMC8672533 DOI: 10.1186/s12903-021-01996-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/24/2021] [Indexed: 11/23/2022] Open
Abstract
Background Recently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful. Aim The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation. Subjects and methods This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5–13 years. Data were randomly divided into training and test datasets in an 80–20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Results The research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (− 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively. Conclusions Compared to the Cameriere formula, ML methods based on the Cameriere’s maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula. Supplementary Information The online version contains supplementary material available at 10.1186/s12903-021-01996-0.
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Affiliation(s)
- Shihui Shen
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China
| | - Zihao Liu
- Department of Nuclear Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Jian Wang
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China
| | - Linfeng Fan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China
| | - Fang Ji
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China.
| | - Jiang Tao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China.
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25
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Zaborowicz K, Biedziak B, Olszewska A, Zaborowicz M. Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods. SENSORS (BASEL, SWITZERLAND) 2021; 21:6008. [PMID: 34577221 PMCID: PMC8473021 DOI: 10.3390/s21186008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/13/2022]
Abstract
The analog methods used in the clinical assessment of the patient's chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.
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Affiliation(s)
- Katarzyna Zaborowicz
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Barbara Biedziak
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Aneta Olszewska
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Maciej Zaborowicz
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznań, Poland
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Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images. Int J Legal Med 2021; 135:1589-1597. [PMID: 33661340 DOI: 10.1007/s00414-021-02542-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 02/11/2021] [Indexed: 02/06/2023]
Abstract
Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learning techniques have been applied for age estimation recently but lacking performance comparison between manual and machine learning methods based on a large sample of dental orthopantomograms (OPGs). In total, we collected 10,257 orthopantomograms for the study. We derived logistic regression linear models for each legal age threshold (14, 16, and 18 years old) for manual method and developed the end-to-end convolutional neural network (CNN) which classified the dental age directly to compare with the manual method. Both methods are based on left mandibular eight permanent teeth or the third molar separately. Our results show that compared with the manual methods (92.5%, 91.3%, and 91.8% for age thresholds of 14, 16, and 18, respectively), the end-to-end CNN models perform better (95.9%, 95.4%, and 92.3% for age thresholds of 14, 16, and 18, respectively). This work proves that CNN models can surpass humans in age classification, and the features extracted by machines may be different from that defined by human.
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