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Kadi H, Kawczynski M, Bendjama S, Flores JZ, Leong-Hoi A, de Lastic H, Balbierer J, Mabileau C, Radoux JP, Grollemund B, Jaegle J, Guebert C, Bisch B, Bloch-Zupan A. i-Dent: A virtual assistant to diagnose rare genetic dental diseases. Comput Biol Med 2024; 180:108927. [PMID: 39096608 DOI: 10.1016/j.compbiomed.2024.108927] [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: 02/06/2024] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 08/05/2024]
Abstract
Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare diseases including orodental developmental anomalies such as missing teeth. However, if left untreated, their symptoms can become significant and disabling for the patient. Early detection and rapid management are therefore essential in this context. The i-Dent project aims to supply a pre-diagnostic tool to detect rare diseases with tooth agenesis of varying severity and pattern. To identify missing teeth, image segmentation models (Mask R-CNN, U-Net) have been trained for the automatic detection of teeth on patients' panoramic dental X-rays. Teeth segmentation enables the identification of teeth which are present or missing within the mouth. Furthermore, a dental age assessment is conducted to verify whether the absence of teeth is an anomaly or a characteristic of the patient's age. Due to the small size of our dataset, we developed a new dental age assessment technique based on the tooth eruption rate. Information about missing teeth is then used by a final algorithm based on the agenesis probabilities to propose a pre-diagnosis of a rare disease. The results obtained in detecting three types of genes (PAX9, WNT10A and EDA) by our system are very promising, providing a pre-diagnosis with an average accuracy of 72 %.
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Affiliation(s)
- Hocine Kadi
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Marzena Kawczynski
- Reference Center for Rare Oral and Dental Diseases, Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Centre de Référence des Maladies Rares Orales et Dentaires, CRMR O-Rares, Filière Santé Maladies Rares TETE COU, European Reference Network ERN CRANIO, 1 Place de l'Hôpital, 67000, Strasbourg, France.
| | - Sara Bendjama
- Reference Center for Rare Oral and Dental Diseases, Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Centre de Référence des Maladies Rares Orales et Dentaires, CRMR O-Rares, Filière Santé Maladies Rares TETE COU, European Reference Network ERN CRANIO, 1 Place de l'Hôpital, 67000, Strasbourg, France.
| | - Jesus Zegarra Flores
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Audrey Leong-Hoi
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Hugues de Lastic
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Julien Balbierer
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Claire Mabileau
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Jean Pierre Radoux
- Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Bruno Grollemund
- Reference Center for Rare Oral and Dental Diseases, Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Centre de Référence des Maladies Rares Orales et Dentaires, CRMR O-Rares, Filière Santé Maladies Rares TETE COU, European Reference Network ERN CRANIO, 1 Place de l'Hôpital, 67000, Strasbourg, France.
| | - Jean Jaegle
- e-media, Bâtiment Gauss - Parc d'innovation, 950 Boulevard Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Christophe Guebert
- e-media, Bâtiment Gauss - Parc d'innovation, 950 Boulevard Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Bertrand Bisch
- e-media, Bâtiment Gauss - Parc d'innovation, 950 Boulevard Sébastien Brant, 67400, Illkirch-Graffenstaden, France.
| | - Agnès Bloch-Zupan
- Reference Center for Rare Oral and Dental Diseases, Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Centre de Référence des Maladies Rares Orales et Dentaires, CRMR O-Rares, Filière Santé Maladies Rares TETE COU, European Reference Network ERN CRANIO, 1 Place de l'Hôpital, 67000, Strasbourg, France; Université de Strasbourg, Faculté de Chirurgie Dentaire, 8 Rue St Elisabeth, 67000, Strasbourg, France; Université de Strasbourg, Institut d'études Avancées (USIAS), Strasbourg, France; Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM, U1258, CNRS - UMR7104, BP 10142, 1 Rue Laurent Fries, 67404, Illkirch-Graffenstaden, France.
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Warrier V, Shedge R, Garg PK, Dixit SG, Krishan K, Kanchan T. Machine learning and regression analysis for age estimation from the iliac crest based on computed tomographic explorations in an Indian population. MEDICINE, SCIENCE, AND THE LAW 2024; 64:204-216. [PMID: 37670580 DOI: 10.1177/00258024231198917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Age estimation constitutes an integral parameter of identification. In children, sub-adults, and young adults, accurate age estimation is vital on various aspects of civil, criminal, and immigration law. The iliac crest presents as a suitable age marker within these age cohorts, and the modified Risser method constitutes a relatively novel and unexplored method for iliac crest age estimation. The present study attempted to ascertain the applicability of this modified method for age estimation in the Indian population, an aspect previously unexplored, through computed tomographic examination of the iliac crest. Computed tomography scans of consenting individuals undergoing routine examinations of the pelvis/ abdomen for various clinically indicated reasons were collected and scored using the modified Risser stages. Computed tomographic examinations of the iliac crest indicate that the recalibrated method accurately depicts the temporal progression of ossification and fusion changes. Different regression and machine learning models were subsequently derived and/or trained to evaluate the accuracy and precision associated with the method. Amongst the ten regression models derived herein, compound regression exhibited the lowest inaccuracy (4.78 years) and root mean squared error values (5.46 years). Machine learning yielded further reduced error rates, with decision tree regression achieving inaccuracy and root mean squared error values of 1.88 years and 2.28 years, respectively. A comparative evaluation of error computations obtained from regression analysis and machine learning illustrates the statistical superiority of machine learning for forensic age estimation. Error computations obtained with machine learning suggest that the modified Risser method is capable of permitting reliable age estimation within criminal and civil proceedings.
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Affiliation(s)
- Varsha Warrier
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
| | - Rutwik Shedge
- School of Forensic Sciences, National Forensic Sciences University, Tripura, India
| | - Pawan Kumar Garg
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India
| | - Shilpi Gupta Dixit
- Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, India
| | - Kewal Krishan
- Department of Anthropology, (UGC Centre of Advanced Study), Panjab University, Chandigarh, India
| | - Tanuj Kanchan
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
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3
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Park SJ, Yang S, Kim JM, Kang JH, Kim JE, Huh KH, Lee SS, Yi WJ, Heo MS. Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population. Int J Legal Med 2024; 138:1741-1757. [PMID: 38467754 PMCID: PMC11164743 DOI: 10.1007/s00414-024-03204-4] [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: 10/26/2023] [Accepted: 02/27/2024] [Indexed: 03/13/2024]
Abstract
Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.
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Affiliation(s)
- Se-Jin Park
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 03080, South Korea
| | - Jun-Min Kim
- Department of Electronics and Information Engineering, Hansung University, Seoul, 03080, South Korea
| | - Ju-Hee Kang
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 03080, South Korea.
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
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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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Ong SH, Kim H, Song JS, Shin TJ, Hyun HK, Jang KT, Kim YJ. Fully automated deep learning approach to dental development assessment in panoramic radiographs. BMC Oral Health 2024; 24:426. [PMID: 38582843 PMCID: PMC10998373 DOI: 10.1186/s12903-024-04160-6] [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: 11/24/2023] [Accepted: 03/18/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning. METHODS The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. RESULTS The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method. CONCLUSIONS These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.
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Affiliation(s)
- Seung-Hwan Ong
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Hyuntae Kim
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Ji-Soo Song
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Teo Jeon Shin
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Hong-Keun Hyun
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Ki-Taeg Jang
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Young-Jae Kim
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
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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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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Kalyakulina A, Yusipov I, Moskalev A, Franceschi C, Ivanchenko M. eXplainable Artificial Intelligence (XAI) in aging clock models. Ageing Res Rev 2024; 93:102144. [PMID: 38030090 DOI: 10.1016/j.arr.2023.102144] [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: 09/26/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
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Affiliation(s)
- Alena Kalyakulina
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Igor Yusipov
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Claudio Franceschi
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Mikhail Ivanchenko
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
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Bağ İ, Bilgir E, Bayrakdar İŞ, Baydar O, Atak FM, Çelik Ö, Orhan K. An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population. BMC Oral Health 2023; 23:764. [PMID: 37848870 PMCID: PMC10583406 DOI: 10.1186/s12903-023-03532-8] [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: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. METHODS A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. RESULTS A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). CONCLUSIONS The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.
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Affiliation(s)
- İrem Bağ
- Department of Pediatric Dentistry, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Oğuzhan Baydar
- Dentomaxillofacial Radiology Specialist, Faculty of Dentistry, Ege University, İzmir, Turkey
| | - Fatih Mehmet Atak
- Department of Computer Engineering, The Faculty of Engineering, Boğaziçi University, İstanbul, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review. Diagnostics (Basel) 2023; 13:2512. [PMID: 37568875 PMCID: PMC10416832 DOI: 10.3390/diagnostics13152512] [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: 07/11/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.
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Affiliation(s)
- Esra Sivari
- Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey
| | | | - Erkan Bostanci
- Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
| | | | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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11
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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: 8] [Impact Index Per Article: 8.0] [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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12
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Zhou Y, Jiang F, Cheng F, Li J. Detecting representative characteristics of different genders using intraoral photographs: a deep learning model with interpretation of gradient-weighted class activation mapping. BMC Oral Health 2023; 23:327. [PMID: 37231478 DOI: 10.1186/s12903-023-03033-8] [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: 02/20/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Sexual dimorphism is obvious not only in the overall architecture of human body, but also in intraoral details. Many studies have found a correlation between gender and morphometric features of teeth, such as mesio-distal diameter, buccal-lingual diameter and height. However, it's still difficult to detect gender through the observation of intraoral photographs, with accuracy around 50%. The purpose of this study was to explore the possibility of automatically telling gender from intraoral photographs by deep neural network, and to provide a novel angle for individual oral treatment. METHODS A deep learning model based on R-net was proposed, using the largest dataset (10,000 intraoral images) to support the automatic detection of gender. In order to reverse analyze the classification basis of neural network, Gradient-weighted Class Activation Mapping (Grad-CAM) was used in the second step, exploring anatomical factors associated with gender recognizability. The simulated modification of images based on features suggested was then conducted to verify the importance of characteristics between two genders. Precision (specificity), recall (sensitivity) and receiver operating characteristic (ROC) curves were used to evaluate the performance of our network. Chi-square test was used to evaluate intergroup difference. A value of p < 0.05 was considered statistically significant. RESULTS The deep learning model showed a strong ability to learn features from intraoral images compared with human experts, with an accuracy of 86.5% and 82.5% in uncropped image data group and cropped image data group respectively. Compared with hard tissue exposed in the mouth, gender difference in areas covered by soft tissue was easier to identify, and more significant in mandibular region than in maxillary region. For photographs with simulated removal of lips and basal bone along with overlapping gingiva, mandibular anterior teeth had similar importance for sex determination as maxillary anterior teeth. CONCLUSIONS Deep learning method could detect gender from intraoral photographs with high efficiency and accuracy. With assistance of Grad-CAM, the classification basis of neural network was deciphered, which provided a more precise entry point for individualization of prosthodontic, periodontal and orthodontic treatments.
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Affiliation(s)
- Yimei Zhou
- State Key Laboratory of Oral Diseases, National Center of Stomatology, West China Hospital of Stomatology, National Clinical Research Center for Oral Diseases, Sichuan University, 14#, 3rd Section, Renmin South Road, Chengdu, 610041, China
| | - Fulin Jiang
- Chongqing University Three Gorges Hospital, Chongqing, 404031, China
| | - Fangyuan Cheng
- Chengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China
| | - Juan Li
- State Key Laboratory of Oral Diseases, National Center of Stomatology, West China Hospital of Stomatology, National Clinical Research Center for Oral Diseases, Sichuan University, 14#, 3rd Section, Renmin South Road, Chengdu, 610041, China.
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13
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Bu WQ, Guo YX, Zhang D, Du SY, Han MQ, Wu ZX, Tang Y, Chen T, Guo YC, Meng HT. Automatic sex estimation using deep convolutional neural network based on orthopantomogram images. Forensic Sci Int 2023; 348:111704. [PMID: 37094502 DOI: 10.1016/j.forsciint.2023.111704] [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: 12/29/2022] [Revised: 04/09/2023] [Accepted: 04/19/2023] [Indexed: 04/26/2023]
Abstract
Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.
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Affiliation(s)
- Wen-Qing Bu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu-Xin Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Dong Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China
| | - Shao-Yi Du
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China
| | - Meng-Qi Han
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Zi-Xuan Wu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu Tang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Teng Chen
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, 76 West Yanta Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu-Cheng Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China.
| | - Hao-Tian Meng
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China.
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14
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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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15
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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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16
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Kengkard P, Choovuthayakorn J, Mahakkanukrauh C, Chitapanarux N, Intasuwan P, Malatong Y, Sinthubua A, Palee P, Lampang SN, Mahakkanukrauh P. Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study. Anat Cell Biol 2023; 56:86-93. [PMID: 36655305 PMCID: PMC9989796 DOI: 10.5115/acb.22.205] [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: 10/13/2022] [Revised: 11/26/2022] [Accepted: 12/05/2022] [Indexed: 01/20/2023] Open
Abstract
Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.
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Affiliation(s)
| | | | | | | | - Pittayarat Intasuwan
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Yanumart Malatong
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Apichat Sinthubua
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.,Excellence in Osteology Research and Training Center, Chiang Mai University, Chiang Mai, Thailand
| | - Patison Palee
- College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand
| | - Sakarat Na Lampang
- Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand
| | - Pasuk Mahakkanukrauh
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.,Excellence in Osteology Research and Training Center, Chiang Mai University, Chiang Mai, Thailand
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17
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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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18
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Lo M, Mariconti E, Nakhaeizadeh S, Morgan RM. Preparing computed tomography images for machine learning in forensic and virtual anthropology. Forensic Sci Int Synerg 2023; 6:100319. [PMID: 36852172 PMCID: PMC9958428 DOI: 10.1016/j.fsisyn.2023.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Affiliation(s)
- Martin Lo
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,UCL Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,Corresponding author. UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK.
| | - Enrico Mariconti
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK
| | - Sherry Nakhaeizadeh
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,UCL Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK
| | - Ruth M. Morgan
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,UCL Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK
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19
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Semi-supervised automatic dental age and sex estimation using a hybrid transformer model. Int J Legal Med 2023; 137:721-731. [PMID: 36717384 DOI: 10.1007/s00414-023-02956-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/24/2023] [Indexed: 02/01/2023]
Abstract
Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.
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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: 0] [Impact Index Per Article: 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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21
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Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth. Sci Rep 2023; 13:704. [PMID: 36639691 PMCID: PMC9839705 DOI: 10.1038/s41598-023-27950-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar and canine tooth images, and a parallel VAE structure was further constructed to extract common features shared by the two types of teeth more effectively. The encoder of the VAE was combined with a regression model to estimate the age. To determine which parts of the tooth images were more or less important when estimating age, a method of visualizing the obtained regression coefficient using the decoder of the VAE was developed. The developed age estimation model was trained using data from 910 individuals aged 10-79. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Furthermore, this method of visualizing the influence of particular parts of tooth images on the accuracy of age estimation using a decoder is expected to provide novel insights for future research on explainable artificial intelligence.
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Klingberg G, Benchimol D, Berlin H, Bring J, Gornitzki C, Odeberg J, Tranæus S, Twetman S, Wernersson E, Östlund P, Domeij H. How old are you? A systematic review investigating the relationship between age and mandibular third molar maturity. PLoS One 2023; 18:e0285252. [PMID: 37200251 DOI: 10.1371/journal.pone.0285252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 04/18/2023] [Indexed: 05/20/2023] Open
Abstract
INTRODUCTION AND OBJECTIVE Radiographic evaluation of the maturity of mandibular third molars is a common method used for age estimation of adolescents and young adults. The aim of this systematic review was to examine the scientific base for the relationship between a fully matured mandibular third molar based on Demirjian's method and chronological age, in order to assess whether an individual is above or below the age of 18 years. METHODS The literature search was conducted in six databases until February 2022 for studies reporting data evaluating the tooth maturity using Demirjian´s method (specifically stage H) within populations ranging from 8 to 30 years (chronological age). Two reviewers screened the titles and abstracts identified through the search strategy independently. All studies of potential relevance according to the inclusion criteria were obtained in full text, after which they were assessed for inclusion by two independent reviewers. Any disagreement was resolved by a discussion. Two reviewers independently evaluated the risk of bias using the assessment tool QUADAS-2 and extracted the data from the studies with low or moderate risk of bias. Logistic regression was used to estimate the relationship between chronological age and proportion of subjects with a fully matured mandibular third molar (Demirjian´s tooth stage H). RESULTS A total of 15 studies with low or moderate risk of bias were included in the review. The studies were conducted in 13 countries and the chronological age of the investigated participants ranged from 3 to 27 years and the number of participants ranged between 208 and 5,769. Ten of the studies presented the results as mean age per Demirjian´s tooth stage H, but only five studies showed the distribution of developmental stages according to validated age. The proportion of subjects with a mandibular tooth in Demirjian´s tooth stage H at 18 years ranged from 0% to 22% among males and 0 to 16% in females. Since the studies were too heterogenous to perform a meta-analysis or a meaningful narrative review, we decided to refrain from a GRADE assessment. CONCLUSION The identified literature does not provide scientific evidence for the relationship between Demirjian´s stage H of a mandibular third molar and chronologic age in order to assess if an individual is under or above the age of 18 years.
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Affiliation(s)
- Gunilla Klingberg
- Faculty of Odontology, Department of Pediatric Dentistry, Malmö University, Malmö, Sweden
| | - Daniel Benchimol
- Department of Dental Medicine, Division of Oral Diagnostics and Rehabilitation, Karolinska Institutet, Huddinge, Sweden
| | - Henrik Berlin
- Faculty of Odontology, Department of Pediatric Dentistry, Malmö University, Malmö, Sweden
| | | | - Carl Gornitzki
- Swedish Agency for Health Technology Assessment and Assessment of Social Services, Stockholm, Sweden
| | - Jenny Odeberg
- Swedish Agency for Health Technology Assessment and Assessment of Social Services, Stockholm, Sweden
| | - Sofia Tranæus
- Swedish Agency for Health Technology Assessment and Assessment of Social Services, Stockholm, Sweden
- Faculty of Odontology, Health Technology Assessment-Odontology, Malmö University, Malmö, Sweden
| | - Svante Twetman
- Faculty of Health and Medical Sciences, Department of Odontology, University of Copenhagen, Copenhagen, Denmark
| | - Emma Wernersson
- Swedish Agency for Health Technology Assessment and Assessment of Social Services, Stockholm, Sweden
| | - Pernilla Östlund
- Swedish Agency for Health Technology Assessment and Assessment of Social Services, Stockholm, Sweden
- Faculty of Odontology, Health Technology Assessment-Odontology, Malmö University, Malmö, Sweden
| | - Helena Domeij
- Swedish Agency for Health Technology Assessment and Assessment of Social Services, Stockholm, Sweden
- Faculty of Odontology, Health Technology Assessment-Odontology, Malmö University, Malmö, Sweden
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Bonfanti-Gris M, Garcia-Cañas A, Alonso-Calvo R, Salido Rodriguez-Manzaneque MP, Pradies Ramiro G. Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs. J Dent 2022; 126:104301. [PMID: 36150430 DOI: 10.1016/j.jdent.2022.104301] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES To evaluate the diagnostic reliability of a web-based Artificial Intelligence program on the detection and classification of dental structures and treatments present on panoramic radiographs. METHODS A total of 300 orthopantomographies (OPG) were randomly selected for this study. First, the images were visually evaluated by two calibrated operators with radiodiagnosis experience that, after consensus, established the "ground truth". Operators' findings on the radiographs were collected and classified as follows: metal restorations (MR), resin-based restorations (RR), endodontic treatment (ET), Crowns (C) and Implants (I). The orthopantomographies were then anonymously uploaded and automatically analyzed by the web-based software (Denti.Ai). Results were then stored, and a statistical analysis was performed by comparing them with the ground truth in terms of Sensitivity (S), Specificity (E), Positive Predictive Value (PPV) Negative Predictive Value (NPV) and its later representation in the area under (AUC) the Receiver Operating Characteristic (ROC) Curve. RESULTS Diagnostic metrics obtained for each study variable were as follows: (MR) S=85.48%, E=87.50%, PPV=82.8%, NPV=42.51%, AUC=0.869; (PR) S=41.11%, E=93.30%, PPV=90.24%, NPV=87.50%, AUC=0.672; (ET) S=91.9%, E=100%, PPV=100%, NPV=94.62%, AUC=0.960; (C) S=89.53%, E=95.79%, PPV=89.53%, NPV=95.79%, AUC=0.927; (I) S, E, PPV, NPV=100%, AUC=1.000. CONCLUSIONS Findings suggest that the web-based Artificial intelligence software provides a good performance on the detection of implants, crowns, metal fillings and endodontic treatments, not being so accurate on the classification of dental structures or resin-based restorations. CLINICAL SIGNIFICANCE General diagnostic and treatment decisions using orthopantomographies can be improved by using web-based artificial intelligence tools, avoiding subjectivity and lapses from the clinician.
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Affiliation(s)
- Monica Bonfanti-Gris
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain
| | - Angel Garcia-Cañas
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain
| | - Raul Alonso-Calvo
- Department of Informatics Systems and Languages, Faculty of Software Engineering, Polytechnic University of Madrid. Campus Montegancedo s/n, Boadilla del Monte. 28660 Madrid, Spain
| | - Maria Paz Salido Rodriguez-Manzaneque
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain.
| | - Guillermo Pradies Ramiro
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain
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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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Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network. Int J Legal Med 2022; 136:797-810. [PMID: 35039894 DOI: 10.1007/s00414-021-02746-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/10/2021] [Indexed: 12/20/2022]
Abstract
In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segmentation networks have been used to automate the location of key pelvic areas and minimize restrictions like doubling images of pelvic organs to increase the accuracy of estimation. This study conducted a retrospective analysis of 2164 pelvis X-ray images of Chinese Han teenagers ranging from 11 to 21 years old. Key areas of the pelvis were detected with a U-Net segmentation network, and the findings were combined with the original X-ray image for regional augmentation. Bone age estimation was conducted with the enhanced and not enhanced pelvis X-ray images by separately using three convolutional neural networks (CNNs). The root mean square errors (RMSE) of the Inception-V3, Inception-ResNet-V2, and VGG19 convolutional neural networks were 0.93 years, 1.12 years, and 1.14 years, respectively, and the mean absolute errors (MAE) of these networks were 0.67 years, 0.77 years, and 0.88 years, respectively. For comparison, a network without segmentation was employed to conduct the estimation, and it was found that the RMSE of the three CNNs above became 1.22 years, 1.25 years, and 1.63 years, respectively, and the MAE became 0.93 years, 0.96 years, and 1.23 years. Bland-Altman plots and attention maps were also generated to provide a visual comparison. The proposed segmentation network can be used to reduce the influence of restrictions like image overlapping of organs and can thus increase the accuracy of pelvic bone age estimation.
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26
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Thurzo A, Kosnáčová HS, Kurilová V, Kosmeľ S, Beňuš R, Moravanský N, Kováč P, Kuracinová KM, Palkovič M, Varga I. Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy. Healthcare (Basel) 2021; 9:1545. [PMID: 34828590 PMCID: PMC8619074 DOI: 10.3390/healthcare9111545] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 12/11/2022] Open
Abstract
Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia;
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
| | - Helena Svobodová Kosnáčová
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia;
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy Sciences, Dúbravská Cesta 9, 84505 Bratislava, Slovakia
| | - Veronika Kurilová
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovičova 3, 81219 Bratislava, Slovakia;
| | - Silvester Kosmeľ
- Deep Learning Engineering Department at Cognexa, Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovičova 2, 84216 Bratislava, Slovakia;
| | - Radoslav Beňuš
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
- Department of Anthropology, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská dolina Ilkovičova 6, 84215 Bratislava, Slovakia
| | - Norbert Moravanský
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
- Institute of Forensic Medicine, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia
| | - Peter Kováč
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
- Department of Criminal Law and Criminology, Faculty of Law Trnava University, Kollárova 10, 91701 Trnava, Slovakia
| | - Kristína Mikuš Kuracinová
- Institute of Pathological Anatomy, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia; (K.M.K.); (M.P.)
| | - Michal Palkovič
- Institute of Pathological Anatomy, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia; (K.M.K.); (M.P.)
- Forensic Medicine and Pathological Anatomy Department, Health Care Surveillance Authority (HCSA), Sasinkova 4, 81108 Bratislava, Slovakia
| | - Ivan Varga
- Institute of Histology and Embryology, Faculty of Medicine, Comenius University in Bratislava, 81372 Bratislava, Slovakia;
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27
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Monill-González A, Rovira-Calatayud L, d'Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthod Craniofac Res 2021; 24 Suppl 2:6-15. [PMID: 34270881 DOI: 10.1111/ocr.12517] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 06/29/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. MATERIALS AND METHODS This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed. RESULTS The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. CONCLUSION The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.
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Affiliation(s)
- Anna Monill-González
- Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | | | - Nuno Gustavo d'Oliveira
- Department of Odontostomatology - Orthodontics. Coordinator of the Master of Orthodontics, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Josep M Ustrell-Torrent
- Department of Odontostomatology - Orthodontics, Oral Health and Masticatory System Group (IDIBELL), University of Barcelona, Barcelona, Spain
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