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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 Hand searches were also performed, and unpublished manuscripts were searched in specialized journals.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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Singh S, Singha B, Kumar S. Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review. THE JOURNAL OF FORENSIC ODONTO-STOMATOLOGY 2024; 42:30-37. [PMID: 38742570 PMCID: PMC11154095 DOI: 10.5281/zenodo.11088513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
In the past few years, there has been an enormous increase in the application of artificial intelligence and its adoption in multiple fields, including healthcare. Forensic medicine and forensic odontology have tremendous scope for development using AI. In cases of severe burns, complete loss of tissue, complete or partial loss of bony structure, decayed bodies, mass disaster victim identification, etc., there is a need for prompt identification of the bony remains. The mandible, is the strongest bone of the facial region, is highly resistant to undue mechanical, chemical or physical impacts and has been widely used in many studies to determine age and sexual dimorphism. Radiographic estimation of the jaw bone for age and sex is more workable since it is simple and can be applied equally to both dead and living cases to aid in the identification process. Hence, this systematic review is focused on various AI tools for age and sex determination in maxillofacial radiographs. The data was obtained through searching for the articles across various search engines, published from January 2013 to March 2023. QUADAS 2 was used for qualitative synthesis, followed by a Cochrane diagnostic test accuracy review for the risk of bias analysis of the included studies. The results of the studies are highly optimistic. The accuracy and precision obtained are comparable to those of a human examiner. These models, when designed with the right kind of data, can be of tremendous use in medico legal scenarios and disaster victim identification.
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Affiliation(s)
- S Singh
- Department of Forensic medicine & Toxicology, Rajendra Institute of Medical Science, Ranchi, India
| | - B Singha
- Department of Forensic medicine & Toxicology, Rajendra Institute of Medical Science, Ranchi, India
| | - S Kumar
- Department of Forensic medicine & Toxicology, Rajendra Institute of Medical Science, Ranchi, India
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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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Wang J, Dou J, Han J, Li G, Tao J. A population-based study to assess two convolutional neural networks for dental age estimation. BMC Oral Health 2023; 23:109. [PMID: 36803132 PMCID: PMC9938587 DOI: 10.1186/s12903-023-02817-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
BACKGROUND Dental age (DA) estimation using two convolutional neural networks (CNNs), VGG16 and ResNet101, remains unexplored. In this study, we aimed to investigate the possibility of using artificial intelligence-based methods in an eastern Chinese population. METHODS A total of 9586 orthopantomograms (OPGs) (4054 boys and 5532 girls) of the Chinese Han population aged from 6 to 20 years were collected. DAs were automatically calculated using the two CNN model strategies. Accuracy, recall, precision, and F1 score of the models were used to evaluate VGG16 and ResNet101 for age estimation. An age threshold was also employed to evaluate the two CNN models. RESULTS The VGG16 network outperformed the ResNet101 network in terms of prediction performance. However, the model effect of VGG16 was less favorable than that in other age ranges in the 15-17 age group. The VGG16 network model prediction results for the younger age groups were acceptable. In the 6-to 8-year-old group, the accuracy of the VGG16 model can reach up to 93.63%, which was higher than the 88.73% accuracy of the ResNet101 network. The age threshold also implies that VGG16 has a smaller age-difference error. CONCLUSIONS This study demonstrated that VGG16 performed better when dealing with DA estimation via OPGs than the ResNet101 network on a wholescale. CNNs such as VGG16 hold great promise for future use in clinical practice and forensic sciences.
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Affiliation(s)
- Jian Wang
- grid.16821.3c0000 0004 0368 8293Department of General Dentistry, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, 200011 China ,grid.16821.3c0000 0004 0368 8293National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, 200011 China
| | - Jiawei Dou
- grid.16821.3c0000 0004 0368 8293School of Software, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jiaxuan Han
- grid.16821.3c0000 0004 0368 8293Department of General Dentistry, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, 200011 China ,grid.16821.3c0000 0004 0368 8293National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, 200011 China
| | - Guoqiang Li
- School of Software, Shanghai Jiao Tong University, Shanghai, 200240, 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, Shanghai, 200011, China. .,National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, 200011, China.
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