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Oliveira W, Albuquerque Santos M, Burgardt CAP, Anjos Pontual ML, Zanchettin C. Estimation of human age using machine learning on panoramic radiographs for Brazilian patients. Sci Rep 2024; 14:19689. [PMID: 39181957 DOI: 10.1038/s41598-024-70621-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024] Open
Abstract
This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.
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Affiliation(s)
- Willian Oliveira
- Universidade Federal de Pernambuco, Centro de Informática - CIn, Recife, 50740-560, Brazil
| | - Mariana Albuquerque Santos
- Universidade Federal de Pernambuco, Centro de Ciências da Saúde, Departamento de Clínica e Odontologia Preventiva, Recife, 50670-901, Brazil
| | | | - Maria Luiza Anjos Pontual
- Universidade Federal de Pernambuco, Centro de Ciências da Saúde, Departamento de Clínica e Odontologia Preventiva, Recife, 50670-901, Brazil
| | - Cleber Zanchettin
- Universidade Federal de Pernambuco, Centro de Informática - CIn, Recife, 50740-560, Brazil.
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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] [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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Wesp P, Schachtner BM, Jeblick K, Topalis J, Weber M, Fischer F, Penning R, Ricke J, Ingrisch M, Sabel BO. Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning. Int J Legal Med 2024; 138:1497-1507. [PMID: 38286953 PMCID: PMC11164764 DOI: 10.1007/s00414-024-03167-6] [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: 07/13/2023] [Accepted: 01/16/2024] [Indexed: 01/31/2024]
Abstract
BACKGROUND Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age assessment based on clavicle ossification in computed tomography (CT). METHODS Thoracic CT scans were retrospectively collected from the picture archiving and communication system. Individuals aged 15.0 to 30.0 years examined in routine clinical practice were included. All scans were automatically cropped around the medial clavicular epiphyseal cartilages. A deep learning model was trained to predict a person's chronological age based on these scans. Performance was evaluated using mean absolute error (MAE). Model performance was compared to an optimistic human reader performance estimate for an established reference study method. RESULTS The deep learning model was trained on 4,400 scans of 1,935 patients (training set: mean age = 24.2 years ± 4.0, 1132 female) and evaluated on 300 scans of 300 patients with a balanced age and sex distribution (test set: mean age = 22.5 years ± 4.4, 150 female). Model MAE was 1.65 years, and the highest absolute error was 6.40 years for females and 7.32 years for males. However, performance could be attributed to norm-variants or pathologic disorders. Human reader estimate MAE was 1.84 years and the highest absolute error was 3.40 years for females and 3.78 years for males. CONCLUSIONS We present a deep learning approach for continuous age predictions using CT volumes highlighting the medial clavicular epiphyseal cartilage with performance comparable to the human reader estimate.
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Affiliation(s)
- Philipp Wesp
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
- Munich Center for Machine Learning (MCML), Geschwister-Scholl-Platz 1, 80539, Munich, Germany.
| | - Balthasar Maria Schachtner
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Katharina Jeblick
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Max-Lebsche-Platz 31, 81377, Munich, Germany
| | - Johanna Topalis
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Marvin Weber
- Institute of Informatics, LMU Munich, Oettingenstraße 67, 80538, Munich, Germany
| | - Florian Fischer
- Institute of Forensic Medicine, LMU Munich, Nußbaumstraße 26, 80336, Munich, Germany
| | - Randolph Penning
- Institute of Forensic Medicine, LMU Munich, Nußbaumstraße 26, 80336, Munich, Germany
| | - Jens Ricke
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Michael Ingrisch
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Munich Center for Machine Learning (MCML), Geschwister-Scholl-Platz 1, 80539, Munich, Germany
| | - Bastian Oliver Sabel
- Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
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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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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [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: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Tournois L, Trousset V, Hatsch D, Delabarde T, Ludes B, Lefèvre T. Artificial intelligence in the practice of forensic medicine: a scoping review. Int J Legal Med 2024; 138:1023-1037. [PMID: 38087052 PMCID: PMC11003914 DOI: 10.1007/s00414-023-03140-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 11/21/2023] [Indexed: 04/11/2024]
Abstract
Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help estimate the biological age of migrants or human remains. However, the uses of AI applications by forensic pathologists or physicians and their levels of integration in medicolegal practices are not well described yet. Therefore, a scoping review was conducted on PubMed, ScienceDirect, and Scopus databases. This review included articles that mention any AI application used by forensic pathologists or physicians in practice or any AI model applied in one expertise field of the forensic pathologist or physician. Articles in other languages than English or French or dealing mainly with complementary analyses handled by experts who are not forensic pathologists or physicians or with AI to analyze data for research purposes in forensic medicine were excluded from this review. All the relevant information was retrieved in each article from a grid analysis derived and adapted from the TRIPOD checklist. This review included 35 articles and revealed that AI applications are developed in thanatology and in clinical forensic medicine. However, those applications seem to mainly remain in research and development stages. Indeed, the use of AI applications by forensic pathologists or physicians is not actual due to issues discussed in this article. Finally, the integration of AI in daily medicolegal practice involves not only forensic pathologists or physicians but also legal professionals.
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Affiliation(s)
- Laurent Tournois
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France.
- BioSilicium, Riom, France.
| | - Victor Trousset
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
| | | | - Tania Delabarde
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Bertrand Ludes
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Thomas Lefèvre
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
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Büyükçakır B, Bertels J, Claes P, Vandermeulen D, de Tobel J, Thevissen PW. OPG-based dental age estimation using a data-technical exploration of deep learning techniques. J Forensic Sci 2024; 69:919-931. [PMID: 38291770 DOI: 10.1111/1556-4029.15473] [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: 11/07/2023] [Revised: 01/07/2024] [Accepted: 01/16/2024] [Indexed: 02/01/2024]
Abstract
Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes.
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Affiliation(s)
- Barkın Büyükçakır
- ESAT, Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Jeroen Bertels
- ESAT, Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Peter Claes
- ESAT, Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Dirk Vandermeulen
- ESAT, Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Jannick de Tobel
- Department of Diagnostic Sciences and Radiology, Ghent University, Ghent, Belgium
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Ibraheem WI. Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Diagnostics (Basel) 2024; 14:806. [PMID: 38667452 PMCID: PMC11049199 DOI: 10.3390/diagnostics14080806] [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: 02/24/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.
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Affiliation(s)
- Wael I Ibraheem
- Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
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Sivri MB, Taheri S, Kırzıoğlu Ercan RG, Yağcı Ü, Golrizkhatami Z. Dental age estimation: A comparative study of convolutional neural network and Demirjian's method. J Forensic Leg Med 2024; 103:102679. [PMID: 38537363 DOI: 10.1016/j.jflm.2024.102679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 05/14/2024]
Abstract
The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian's method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4-17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian's method's grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian's approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian's age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.
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Affiliation(s)
- Mustan Barış Sivri
- Bahçeşehir University, Faculty of Dentistry, Department of Oral and Maxillofacial Surgery, Türkiye.
| | - Shahram Taheri
- Antalya Bilim University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Türkiye.
| | | | - Ünsun Yağcı
- Private Practice Dentist, Department of Prosthodontics, Antalya, Türkiye.
| | - Zahra Golrizkhatami
- Eastern Mediterranean University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Türkiye.
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11
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Dai X, Liu A, Liu J, Zhan M, Liu Y, Ke W, Shi L, Huang X, Chen H, Deng Z, Fan F. Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:611-619. [PMID: 38343227 PMCID: PMC11031552 DOI: 10.1007/s10278-023-00956-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 04/20/2024]
Abstract
Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson's criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson's criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.
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Affiliation(s)
- Xinhua Dai
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Anjie Liu
- University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Junhong Liu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Mengjun Zhan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yuanyuan Liu
- Department of Oral Radiology, College of Stomatology, Sichuan University, West China, Chengdu, 610041, People's Republic of China
| | - Wenchi Ke
- College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China
| | - Lei Shi
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Xinyu Huang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Hu Chen
- College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China
| | - Zhenhua Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Fei Fan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
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12
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Shi Y, Ye Z, Guo J, Tang Y, Dong W, Dai J, Miao Y, You M. Deep learning methods for fully automated dental age estimation on orthopantomograms. Clin Oral Investig 2024; 28:198. [PMID: 38448657 DOI: 10.1007/s00784-024-05598-2] [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: 11/22/2023] [Accepted: 02/29/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVES This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment. METHODS A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset. RESULTS The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72 years with a full dentition (excluding the third molars) as its input. CONCLUSIONS The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population. CLINICAL RELEVANCE The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.
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Affiliation(s)
- Yuchao Shi
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China
| | - Zelin Ye
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China
| | - Jixiang Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Yueting Tang
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China
| | - Wenxuan Dong
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Jiaqi Dai
- Department of Oral Radiology, Sichuan Hospital of Stomatology, Chengdu, 610015, China
| | - Yu Miao
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China
| | - Meng You
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China.
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13
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Lee T, Shin W, Lee JH, Lee S, Yeom HG, Yun JP. Resolving the non-uniformity in the feature space of age estimation: A deep learning model based on feature clusters of panoramic images. Comput Med Imaging Graph 2024; 112:102329. [PMID: 38271869 DOI: 10.1016/j.compmedimag.2024.102329] [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: 07/13/2023] [Revised: 11/04/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024]
Abstract
Age estimation is important in forensics, and numerous techniques have been investigated to estimate age based on various parts of the body. Among them, dental tissue is considered reliable for estimating age as it is less influenced by external factors. The advancement in deep learning has led to the development of automatic estimation of age using dental panoramic images. Typically, most of the medical datasets used for model learning are non-uniform in the feature space. This causes the model to be highly influenced by dense feature areas, resulting in adequate estimations; however, relatively poor estimations are observed in other areas. An effective solution to address this issue can be pre-dividing the data by age feature and training each regressor to estimate the age for individual features. In this study, we divide the data based on feature clusters obtained from unsupervised learning. The developed model comprises a classification head and multi-regression head, wherein the former predicts the cluster to which the data belong and the latter estimates the age within the predicted cluster. The visualization results show that the model can focus on a clinically meaningful area in each cluster for estimating age. The proposed model outperforms the models without feature clusters by focusing on the differences within the area. The performance improvement is particularly noticeable in the growth and aging periods. Furthermore, the model can adequately estimate the age even for samples with a high probability of classification error as they are located at the border of two feature clusters.
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Affiliation(s)
- Taehan Lee
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea
| | - WooSang Shin
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea; Electronic Engineering Department, Kyungpook National University, Daegu 41566, South Korea
| | - Jong-Hyeon Lee
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea; Electronic Engineering Department, Kyungpook National University, Daegu 41566, South Korea
| | - Sangmoon Lee
- Electronic Engineering Department, Kyungpook National University, Daegu 41566, South Korea
| | - Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan 54538, South Korea.
| | - Jong Pil Yun
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea; University of Science and Technology, Daegu 42994, South Korea.
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14
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Heinrich A. Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years. Sci Rep 2024; 14:4195. [PMID: 38379027 PMCID: PMC10879188 DOI: 10.1038/s41598-024-54877-1] [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: 10/20/2023] [Accepted: 02/17/2024] [Indexed: 02/22/2024] Open
Abstract
Computer Vision (CV)-based human identification using orthopantomograms (OPGs) has the potential to identify unknown deceased individuals by comparing postmortem OPGs with a comprehensive antemortem CV database. However, the growing size of the CV database leads to longer processing times. This study aims to develop a standardized and reliable Convolutional Neural Network (CNN) for age estimation using OPGs and integrate it into the CV-based human identification process. The CNN was trained on 50,000 OPGs, each labeled with ages ranging from 2 to 89 years. Testing included three postmortem OPGs, 10,779 antemortem OPGs, and an additional set of 70 OPGs within the context of CV-based human identification. Integrating the CNN for age estimation into CV-based human identification process resulted in a substantial reduction of up to 96% in processing time for a CV database containing 105,251 entries. Age estimation accuracy varied between postmortem and antemortem OPGs, with a mean absolute error (MAE) of 2.76 ± 2.67 years and 3.26 ± 3.06 years across all ages, as well as 3.69 ± 3.14 years for an additional 70 OPGs. In conclusion, the incorporation of a CNN for age estimation in the CV-based human identification process significantly reduces processing time while delivering reliable results.
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Affiliation(s)
- Andreas Heinrich
- Department of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
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15
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Kokomoto K, Kariya R, Muranaka A, Okawa R, Nakano K, Nozaki K. Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification. BMC Oral Health 2024; 24:143. [PMID: 38291396 PMCID: PMC10829298 DOI: 10.1186/s12903-024-03928-0] [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: 06/26/2023] [Accepted: 01/23/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. METHODS Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts' dental age calculations. RESULTS Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. CONCLUSION Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy.
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Affiliation(s)
- Kazuma Kokomoto
- Division for Medical Informatics, Osaka University Dental Hospital, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan.
| | - Rina Kariya
- Department of Pediatric Dentistry, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Aya Muranaka
- Department of Pediatric Dentistry, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Rena Okawa
- Department of Pediatric Dentistry, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Kazuhiko Nakano
- Department of Pediatric Dentistry, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Kazunori Nozaki
- Division for Medical Informatics, Osaka University Dental Hospital, 1-8 Yamada-oka, Suita, Osaka, 565-0871, Japan.
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16
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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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17
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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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18
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Yeom HG, Lee BD, Lee W, Lee T, Yun JP. Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population. Sci Rep 2023; 13:21857. [PMID: 38071386 PMCID: PMC10710476 DOI: 10.1038/s41598-023-48960-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
This study suggests a hybrid method based on ResNet50 and vision transformer (ViT) in an age estimation model. To this end, panoramic radiographs are used for learning by considering both local features and global information, which is important in estimating age. Transverse and longitudinal panoramic images of 9663 patients were selected (4774 males and 4889 females with a mean age of 39 years and 3 months). To compare ResNet50, ViT, and the hybrid model, the mean absolute error, mean square error, root mean square error, and coefficient of determination (R2) were used as metrics. The results confirmed that the age estimation model designed using the hybrid method performed better than those using only ResNet50 or ViT. The estimation is highly accurate for young people at an age with distinct growth characteristics. When examining the basis for age estimation in the hybrid model through attention rollout, the proposed model used logical and important factors rather than relying on unclear elements as the basis for age estimation.
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Affiliation(s)
- Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Byung-Do Lee
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Wan Lee
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan, Republic of Korea
| | - Taehan Lee
- AI Research Center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu, 42994, Republic of Korea.
| | - Jong Pil Yun
- AI Research Center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu, 42994, Republic of Korea.
- University of Science and Technology, Daegu, Republic of Korea.
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19
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Timme M, Bender J, Steffens L, Shay D, Schmeling A. Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment-Presentation of a New Non-Staging Method Based on Measurements. BIOLOGY 2023; 12:1403. [PMID: 37998002 PMCID: PMC10669860 DOI: 10.3390/biology12111403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023]
Abstract
The evaluation of third molar eruption in dental panoramic radiographs (DPRs) constitutes an evidence-based approach for forensic age assessment in living individuals. Existing methodologies involve staging morphological radiographic findings and comparing them to reference populations. Conversely, the existing literature presents an alternative method where the distance between third molars and the occlusal plane is measured on dental plaster models. The aim of this study was to adapt this measurement principle for DPRs and to determine correlation between eruption and chronological age. A total of 423 DPRs, encompassing 220 females and 203 males aged 15 to 25 years, were examined, including teeth 38 [FDI] and 48. Two independent examiners conducted the measurements, with one examiner providing dual assessments. Ultimately, a quotient was derived by comparing orthogonal distances from the mesial cementoenamel junctions of the second and third molars to a simplified radiological occlusal plane. This quotient was subsequently correlated with the individual's age. We estimated correlations between age and quotients, as well as inter- and intra-rater reliability. Correlation coefficients (Spearman's rho) between measurements and individuals' ages ranged from 0.555 to 0.597, conditional on sex and tooth. Intra-rater agreement (Krippendorf's alpha) ranged from 0.932 to 0.991, varying according to the tooth and sex. Inter-rater agreement ranged from 0.984 to 0.992, with distinctions drawn for different teeth and sex. Notably, all observer agreement values fell within the "very good" range. In summary, assessing the distance of third molars from a simplified occlusal plane in DPRs emerges as a new and promising method for evaluating eruption status in forensic age assessment. Subsequent reference studies should validate these findings.
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Affiliation(s)
- Maximilian Timme
- Institute of Legal Medicine, University Hospital Münster, Röntgenstraße 23, 48149 Münster, Germany; (J.B.); (L.S.); (D.S.); (A.S.)
| | - Jostin Bender
- Institute of Legal Medicine, University Hospital Münster, Röntgenstraße 23, 48149 Münster, Germany; (J.B.); (L.S.); (D.S.); (A.S.)
| | - Laurin Steffens
- Institute of Legal Medicine, University Hospital Münster, Röntgenstraße 23, 48149 Münster, Germany; (J.B.); (L.S.); (D.S.); (A.S.)
| | - Denys Shay
- Institute of Legal Medicine, University Hospital Münster, Röntgenstraße 23, 48149 Münster, Germany; (J.B.); (L.S.); (D.S.); (A.S.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Andreas Schmeling
- Institute of Legal Medicine, University Hospital Münster, Röntgenstraße 23, 48149 Münster, Germany; (J.B.); (L.S.); (D.S.); (A.S.)
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Scendoni R, Tomassini L, Cingolani M, Perali A, Pilati S, Fedeli P. Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers. Healthcare (Basel) 2023; 11:1979. [PMID: 37510420 PMCID: PMC10378994 DOI: 10.3390/healthcare11141979] [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: 06/13/2023] [Revised: 07/01/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) span multiple disciplines, including the medico-legal sciences, also with reference to the concept of disease and disability. In this context, the International Classification of Diseases, Injuries, and Causes of Death (ICD) is a standard for the classification of diseases and related problems developed by the World Health Organization (WHO), and it represents a valid tool for statistical and epidemiological studies. Indeed, the International Classification of Functioning, Disability, and Health (ICF) is outlined as a classification that aims to describe the state of health of people in relation to their existential spheres (social, family, work). This paper lays the foundations for proposing an operating model for the use of AI in the assessment of impairments with the aim of making the information system as homogeneous as possible, starting from the main coding systems of the reference pathologies and functional damages. Providing a scientific basis for the understanding and study of health, as well as establishing a common language for the assessment of disability in its various meanings through AI systems, will allow for the improvement and standardization of communication between the various expert users.
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Affiliation(s)
- Roberto Scendoni
- Department of Law, Institute of Legal Medicine, University of Macerata, 62100 Macerata, Italy
| | - Luca Tomassini
- International School of Advanced Studies, University of Camerino, 62032 Camerino, Italy
| | - Mariano Cingolani
- Department of Law, Institute of Legal Medicine, University of Macerata, 62100 Macerata, Italy
| | - Andrea Perali
- Physics Unit, School of Pharmacy, University of Camerino, 62032 Camerino, Italy
| | - Sebastiano Pilati
- Physics Division, School of Science and Technology, University of Camerino, 62032 Camerino, Italy
| | - Piergiorgio Fedeli
- School of Law, Legal Medicine, University of Camerino, 62032 Camerino, Italy
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21
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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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22
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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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23
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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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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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25
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Bui R, Iozzino R, Richert R, Roy P, Boussel L, Tafrount C, Ducret M. Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4620. [PMID: 36901630 PMCID: PMC10002153 DOI: 10.3390/ijerph20054620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/25/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert.
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Affiliation(s)
- Romain Bui
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Régis Iozzino
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Raphaël Richert
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Pascal Roy
- Service de Biostatistique—Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, 69008 Lyon, France
- Équipe Biostatistique-Santé, Laboratoire de Biométrie et Biologie Évolutive, UMR 5558 CNRS, Université Claude Bernard Lyon 1, Université de Lyon, 69100 Villeurbanne, France
| | - Loïc Boussel
- Department of Radiology, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, 69004 Lyon, France
- CREATIS, INSA Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, UMR 5220, U1294, 69100 Villeurbanne, France
| | - Cheraz Tafrount
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
| | - Maxime Ducret
- Pôle d’Odontologie, Hospices Civils de Lyon, 69008 Lyon, France
- Faculté d’Odontologie, Université Claude Bernard Lyon 1, Université de Lyon, 69372 Lyon, France
- Institut de Biologie et Chimie des Protéines, Laboratoire de Biologie Tissulaire et Ingénierie Thérapeutique, UMR 5305 CNRS, Université Claude Bernard Lyon 1, 69367 Lyon, France
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Galante N, Cotroneo R, Furci D, Lodetti G, Casali MB. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. Int J Legal Med 2023; 137:445-458. [PMID: 36507961 DOI: 10.1007/s00414-022-02928-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.
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Affiliation(s)
- Nicola Galante
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
| | - Rosy Cotroneo
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Domenico Furci
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Giorgia Lodetti
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Michelangelo Bruno Casali
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
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27
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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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28
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Oueriagli SN, El Asraoui L, Sahel OA, Benameur Y, Doudouh A. Snow Leopard Appearance of Subcutaneous Panniculitis such as T-cell Lymphoma on 18F-FDG PET/CT. Mol Imaging Radionucl Ther 2023; 32:77-79. [PMID: 36820276 PMCID: PMC9950679 DOI: 10.4274/mirt.galenos.2022.63644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Subcutaneous panniculitis such as T-cell lymphoma (SPTCL) is a very rare disorder. Patients usually present with multiple subcutaneous nodules on the extremities without visceral disease. Dissemination to extra-cutaneous sites is unusual. Only a few cases of SPTCL have been reported in the literature describing the findings of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). Here, we represent an interesting and unusual case of diffuse SPTCL with snow Leopard skin appearance on 18F-FDG PET/computed tomography.
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Affiliation(s)
- Salah Nabih Oueriagli
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco,* Address for Correspondence: Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco Phone: +212662101403 E-mail:
| | - Laila El Asraoui
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
| | - Omar Ait Sahel
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
| | - Yassir Benameur
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
| | - Abderrahim Doudouh
- Mohamed V University Souissi, Mohammed V Military Teaching Hospital, Department of Nuclear Medicine, Rabat, Morocco
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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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Arsiwala-Scheppach LT, Chaurasia A, Müller A, Krois J, Schwendicke F. Machine Learning in Dentistry: A Scoping Review. J Clin Med 2023; 12:937. [PMID: 36769585 PMCID: PMC9918184 DOI: 10.3390/jcm12030937] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/06/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
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Affiliation(s)
- Lubaina T. Arsiwala-Scheppach
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
- Department of Oral Medicine and Radiology, King George’s Medical University, Lucknow 226003, India
| | - Anne Müller
- Pharmacovigilance Institute (Pharmakovigilanz- und Beratungszentrum, PVZ) for Embryotoxicology, Institute of Clinical Pharmacology and Toxicology, Charité—Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
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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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Lin Y, Maimaitiyiming N, Sui M, Abuduxiku N, Tao J. Performance of the London Atlas, Willems, and a new quick method for dental age estimation in Chinese Uyghur children. BMC Oral Health 2022; 22:624. [PMID: 36544118 PMCID: PMC9773604 DOI: 10.1186/s12903-022-02652-x] [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: 10/12/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Numerous dental age estimation methods have been devised and practised for decades. Among these, the London Atlas and Willems methods were two of the most frequently adopted, however dependent on atlantes or tables. A new estimation method less reliant on external measurement could be efficient and economical. AIM This study aimed to evaluate the utility and applicability of the dental age estimation methods of London Atlas, Willems, and a new quick method that subtracts the number of developing teeth from the universal root mature age of 16 years in one of the lower quadrants reported in this work among Chinese Uyghur children. METHODS A comparative cross-sectional study was conducted. Subjects enrolled in the study were screened according to preset inclusion and exclusion criteria. The observer then obtained the dental age from the subjects' panoramic radiographs based on the estimated rules of the London Atlas, Willems, and a new quick method. Paired t-test was used to compare the accuracy and precision of the above three estimation methods. Independent-sample t-test was used to find the difference between gender. RESULTS Totally, 831 radiographs entered the analyses of this study. Among the three methods evaluated, the Willems method, in particular, showed a distinct underestimated tendency. The mean error of the dental age predicted by the London Atlas, the Willems method, and the quick method was 0.06 ± 1.13 years, 0.44 ± 1.14 years, and 0.30 ± 0.63 years, respectively. The mean absolute error was 0.86 ± 0.75 years according to the London Atlas, 1.17 ± 0.89 years under the Willems method, and 0.70 ± 0.54 years under our quick method. No significant difference was found between the chronological age and dental age using the London Atlas, generally for the 10 to 15 years group (p > 0.05), but our quick method for the 15-16 years children (p < 0.05) and Willems method (p < 0.001). CONCLUSION The London Atlas outperformed the Willems method with better accuracy and precision among 10-15 years Chinese Uyghur children. Our new quick method may be comparable to the London Atlas for children aged 10-14 and potentially become a more straightforward dental age prediction instrument.
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Affiliation(s)
- Yueting Lin
- grid.412523.30000 0004 0386 9086Department of General Dentistry, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, No. 500 Qu Xi Road, Shanghai, 200011 China
| | - Nuerbiyamu Maimaitiyiming
- grid.412631.3Department of Cariology and Endodontics, The First Affiliated Hospital of Xinjiang Medical University (The Affiliated Stomatology Hospital of Xinjiang Medical University), Ürümqi, Xinjiang China
| | - Meizhi Sui
- grid.412523.30000 0004 0386 9086Department of General Dentistry, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, No. 500 Qu Xi Road, Shanghai, 200011 China ,Department of Stomatology, Kashgar Prefecture Second People’s Hospital, Kashgar, Xinjiang China
| | - Nuerbiya Abuduxiku
- Department of Stomatology, The First People′s Hospital of Kashgar Prefecture, No. 66 Yingbin Avenue, Kashgar, Xinjiang 844000 China
| | - Jiang Tao
- grid.412523.30000 0004 0386 9086Department of General Dentistry, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, No. 500 Qu Xi Road, Shanghai, 200011 China
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Shen S, Yuan X, Wang J, Fan L, Zhao J, Tao J. Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods. Front Public Health 2022; 10:1068253. [PMID: 36530730 PMCID: PMC9751184 DOI: 10.3389/fpubh.2022.1068253] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Background Machine learning (ML) algorithms play a key role in estimating dental age. In this study, three ML models were used for dental age estimation, based on different preprocessing methods. Aim The seven mandibular teeth on the digital panorama were measured and evaluated according to the Cameriere and the Demirjian method, respectively. Correlation data were used for decision tree (DT), Bayesian ridge regression (BRR), k-nearest neighbors (KNN) models for dental age estimation. An accuracy comparison was made among different methods. Subjects and methods We analyzed 748 orthopantomographs (392 males and 356 females) from eastern China between the age of 5 and 13 years in this retrospective study. Three models, DT, BRR, and KNN, were used to estimate the dental age. The data in ML is obtained according to the Cameriere method and the Demirjian method. Coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE), the above five metrics were used to evaluate the accuracy of age estimation. Results Our experimental results showed that the prediction accuracy of dental age was affected by ML algorithms. MD, MAD, MSE, RMSE of the dental age predicted by ML were significantly decreased. Among all the methods, the KNN model based on the Cameriere method had the highest accuracy (ME = 0.015, MAE = 0.473, MSE = 0.340, RMSE = 0.583, R2 = 0.94). Conclusion The results show that the prediction accuracy of dental age is influenced by ML algorithms and preprocessing method. The KNN model based on the Cameriere method was able to infer dental age more accurately in a clinical setting.
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Affiliation(s)
- Shihui Shen
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xiaoyan Yuan
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China
| | - Jian Wang
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China
| | - Linfeng Fan
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China,Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junjun Zhao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China,Junjun Zhao
| | - Jiang Tao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China,National Center for Stomatology, Shanghai, China,National Clinical Research Center for Oral Diseases, Shanghai, China,Shanghai Key Laboratory of Stomatology, Shanghai, China,Shanghai Research Institute of Stomatology, Shanghai, China,*Correspondence: Jiang Tao
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Pintana P, Upalananda W, Saekho S, Yarach U, Wantanajittikul K. Fully automated method for dental age estimation using the ACF detector and deep learning. EGYPTIAN JOURNAL OF FORENSIC SCIENCES 2022. [DOI: 10.1186/s41935-022-00314-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Abstract
Background
Dental age estimation plays an important role in identifying an unknown person. In forensic science, estimating age with high accuracy depends on the experience of the practitioner. Previous studies proposed classification of tooth development of the mandibular third molar by following Demirjian’s method, which is useful for dental age estimation. Although stage of tooth growth is very helpful in assessing age estimation, it must be performed manually. The drawback of this procedure is its need for skilled observers to carry out the tasks precisely and reproducibly because it is quite detailed. Therefore, this research aimed to apply computer-aid methods for reducing time and subjectivity in dental age estimation by using dental panoramic images based on Demirjian’s method. Dental panoramic images were collected from persons aged 15 to 23 years old. In accordance with Demirjian’s method, this study focused only on stages D to H of tooth development, which were discovered in the 15- to 23-year age range. The aggregate channel features detector was applied automatically to localize and crop only the lower left mandibular third molar in panoramic images. Then, the convolutional neural network model was applied to classify cropped images into D to H stages. Finally, the classified stages were used to estimate dental age.
Results
Experimental results showed that the proposed method in this study can localize the lower left mandibular third molar automatically with 99.5% accuracy, and training in the convolutional neural network model can achieve 83.25% classification accuracy using the transfer learning strategy with the Resnet50 network.
Conclusion
In this work, the aggregate channel features detector and convolutional neural network model were applied to localize a specific tooth in a panoramic image and identify the developmental stages automatically in order to estimate the age of the subjects. The proposed method can be applied in clinical practice as a tool that helps clinicians to reduce the time and subjectivity for dental age estimation.
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Venema J, Peula D, Irurita J, Mesejo P. Employing deep learning for sex estimation of adult individuals using 2D images of the humerus. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07981-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractBiological profile estimation, of which sex estimation is a fundamental first stage, is a really important task in forensic human identification. Although there are a large number of methods that address this problem from different bone structures, mainly using the pelvis and the skull, it has been shown that the humerus presents significant sexual dimorphisms that can be used to estimate sex in their absence. However, these methods are often too subjective or costly, and the development of new methods that avoid these problems is one of the priorities in forensic anthropology research. In this respect, the use of artificial intelligence may allow to automate and reduce the subjectivity of biological profile estimation methods. In fact, artificial intelligence has been successfully applied in sex estimation tasks, but most of the previous work focuses on the analysis of the pelvis and the skull. More importantly, the humerus, which can be useful in some situations due to its resistance, has never been used in the development of an automatic sex estimation method. Therefore, this paper addresses the use of machine learning techniques to the task of image classification, focusing on the use of images of the distal epiphysis of the humerus to classify whether it belongs to a male or female individual. To address this, we have used a set of humerus photographs of 417 adult individuals of Mediterranean origin to validate and compare different approaches, using both deep learning and traditional feature extraction techniques. Our best model obtains an accuracy of 91.03% in test, correctly estimating the sex of 92.68% of the males and 89.19% of the females. These results are superior to the ones obtained by the state of the art and by a human expert, who has achieved an accuracy of 83.33% using a state-of-the-art method on the same data. In addition, the visualization of activation maps allows us to confirm not only that the neural network observes the sexual dimorphisms that have been proposed by the forensic anthropology literature, but also that it has been capable of finding a new region of interest.
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DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs. BMC Bioinformatics 2022; 23:426. [PMID: 36241969 PMCID: PMC9569056 DOI: 10.1186/s12859-022-04935-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability. RESULT Based on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3-11 (children), 12-18 (teens), 19-25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead. CONCLUSIONS We introduced a novel deep learning approach DENSEN to estimate a subject's age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.
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Vila-Blanco N, Varas-Quintana P, Aneiros-Ardao Á, Tomás I, Carreira MJ. XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions. Comput Biol Med 2022; 149:106072. [PMID: 36115299 DOI: 10.1016/j.compbiomed.2022.106072] [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: 04/25/2022] [Revised: 08/05/2022] [Accepted: 08/27/2022] [Indexed: 11/03/2022]
Abstract
Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenarios.
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Affiliation(s)
- Nicolás Vila-Blanco
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), 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, Spain.
| | - Ángela Aneiros-Ardao
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical Surgical Specialities, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Spain.
| | - Inmaculada Tomás
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de 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, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Spain.
| | - María J Carreira
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Spain; Departamento de Electrónica e Computación, Escola Técnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Spain.
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Zhang Z, Liu N, Guo Z, Jiao L, Fenster A, Jin W, Zhang Y, Chen J, Yan C, Gou S. Ageing and degeneration analysis using ageing-related dynamic attention on lateral cephalometric radiographs. NPJ Digit Med 2022; 5:151. [PMID: 36168038 PMCID: PMC9515216 DOI: 10.1038/s41746-022-00681-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/22/2022] [Indexed: 11/25/2022] Open
Abstract
With the increase of the ageing in the world’s population, the ageing and degeneration studies of physiological characteristics in human skin, bones, and muscles become important topics. Research on the ageing of bones, especially the skull, are paid much attention in recent years. In this study, a novel deep learning method representing the ageing-related dynamic attention (ARDA) is proposed. The proposed method can quantitatively display the ageing salience of the bones and their change patterns with age on lateral cephalometric radiographs images (LCR) images containing the craniofacial and cervical spine. An age estimation-based deep learning model based on 14142 LCR images from 4 to 40 years old individuals is trained to extract ageing-related features, and based on these features the ageing salience maps are generated by the Grad-CAM method. All ageing salience maps with the same age are merged as an ARDA map corresponding to that age. Ageing salience maps show that ARDA is mainly concentrated in three regions in LCR images: the teeth, craniofacial, and cervical spine regions. Furthermore, the dynamic distribution of ARDA at different ages and instances in LCR images is quantitatively analyzed. The experimental results on 3014 cases show that ARDA can accurately reflect the development and degeneration patterns in LCR images.
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Affiliation(s)
- Zhiyong Zhang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China.,College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China.,Department of Orthodontics, the Affiliated Stomatological Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China
| | - Ningtao Liu
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China.,Robarts Research Institute, Western University, London, N6A 3K7, ON, Canada
| | - Zhang Guo
- Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Licheng Jiao
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, N6A 3K7, ON, Canada
| | - Wenfan Jin
- Department of Radiology, the Affiliated Stomatological Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China
| | - Yuxiang Zhang
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Jie Chen
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China
| | - Chunxia Yan
- College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China.
| | - Shuiping Gou
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China.
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Zhao H, Wang J, Dai D, Lin S, Chen Z. D-NMS: A dynamic NMS network for general object detection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal 2022; 79:102470. [DOI: 10.1016/j.media.2022.102470] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 03/15/2022] [Accepted: 05/02/2022] [Indexed: 12/11/2022]
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Han M, Du S, Ge Y, Zhang D, Chi Y, Long H, Yang J, Yang Y, Xin J, Chen T, Zheng N, Guo YC. With or without human interference for precise age estimation based on machine learning? Int J Legal Med 2022; 136:821-831. [DOI: 10.1007/s00414-022-02796-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/08/2022] [Indexed: 11/29/2022]
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Shen S, Liu Z, Wang J, Fan L, Ji F, Tao J. Machine learning assisted Cameriere method for dental age estimation. BMC Oral Health 2021; 21:641. [PMID: 34911516 PMCID: PMC8672533 DOI: 10.1186/s12903-021-01996-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/24/2021] [Indexed: 11/23/2022] Open
Abstract
Background Recently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful. Aim The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation. Subjects and methods This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5–13 years. Data were randomly divided into training and test datasets in an 80–20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Results The research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (− 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively. Conclusions Compared to the Cameriere formula, ML methods based on the Cameriere’s maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula. Supplementary Information The online version contains supplementary material available at 10.1186/s12903-021-01996-0.
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Affiliation(s)
- Shihui Shen
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China
| | - Zihao Liu
- Department of Nuclear Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Jian Wang
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China
| | - Linfeng Fan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China
| | - Fang Ji
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China.
| | - Jiang Tao
- Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, People's Republic of China.
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Vila-Blanco N, Varas-Quintana P, Aneiros-Ardao Á, Tomás I, Carreira MJ. Automated description of the mandible shape by deep learning. Int J Comput Assist Radiol Surg 2021; 16:2215-2224. [PMID: 34449038 PMCID: PMC8616887 DOI: 10.1007/s11548-021-02474-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/04/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery. Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs). METHODS We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model. RESULTS The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectively. CONCLUSION The methodology proposed, including the shape model, can be valuable in any field that requires a quantitative description of the mandible shape and a visual representation of its changes such as clinical practice, surgery management, dental research, or legal medicine.
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Affiliation(s)
- Nicolás Vila-Blanco
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS) and Departamento de Electrónica e Computación, 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.,Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Ángela Aneiros-Ardao
- 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
- 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. .,Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain.
| | - María J Carreira
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS) and Departamento de Electrónica e Computación, 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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Heo MS, Kim JE, Hwang JJ, Han SS, Kim JS, Yi WJ, Park IW. Artificial intelligence in oral and maxillofacial radiology: what is currently possible? Dentomaxillofac Radiol 2021; 50:20200375. [PMID: 33197209 PMCID: PMC7923066 DOI: 10.1259/dmfr.20200375] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 08/28/2020] [Accepted: 08/28/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.
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Affiliation(s)
- Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Republic of Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, Seoul, Republic of Korea
| | - Jae-Joon Hwang
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, Republic of Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, Seoul, Republic of Korea
| | - Jin-Soo Kim
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Chosun University, Gwangju, Republic of Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Republic of Korea
| | - In-Woo Park
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Gangneung-Wonju National University, Gangneung, Republic of Korea
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Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach. Int J Legal Med 2021; 135:665-675. [PMID: 33410925 DOI: 10.1007/s00414-020-02489-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 12/09/2020] [Indexed: 10/22/2022]
Abstract
CONTEXT Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' method). The major drawbacks of these methods are that they are based on population-specific conversion tables and may tend to over- or underestimate dental age in other populations. Machine learning (ML) methods make it possible to create complex data schemas more simply while keeping the same annotation system. The objectives of this study are to compare (1) the capacity of ten machine learning algorithms to predict dental age in children using the seven left permanent mandibular teeth compared to reference methods and (2) the capacity of ten machine learning algorithms to predict dental age from childhood to young adulthood using the seven left permanent mandibular teeth and the four third molars. METHODS Using a large radiological database of 3605 orthopantomograms (1734 females and 1871 males) of healthy French patients aged between 2 and 24 years, seven left permanent mandibular teeth and the 4 third molars were assessed using Demirjian's stages. Dental age estimation was then performed using Demirjian's reference method and various ML regression methods. Two analyses were performed: with the 7 left mandibular teeth without third molars for the under 16 age group and with the third molars for the entire study population. The different methods were compared using mean error, mean absolute error, root mean square error as metrics, and the Bland-Altman graph. RESULTS All ML methods had a mean absolute error (MAE) under 0.811 years. With Demirjian's and Willems' methods, the MAE was 1.107 and 0.927 years, respectively. Except for the Bayesian ridge regression that gives poorer accuracy, there was no statistical difference between all ML tested. CONCLUSION Compared to the two reference methods, all the ML methods based on the maturation stages defined by Demirjian were more accurate in estimating dental age. These results support the use of ML algorithms instead of using standard population tables.
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Khanagar SB, Vishwanathaiah S, Naik S, A Al-Kheraif A, Devang Divakar D, Sarode SC, Bhandi S, Patil S. Application and performance of artificial intelligence technology in forensic odontology - A systematic review. Leg Med (Tokyo) 2020; 48:101826. [PMID: 33341601 DOI: 10.1016/j.legalmed.2020.101826] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 11/19/2020] [Accepted: 12/04/2020] [Indexed: 01/04/2023]
Abstract
Forensic odontology (FO) mainly deals with the identification of the individual through the remains, which mainly includes teeth and jawbones. Artificial intelligence (AI) technology has proven to be a breakthrough in providing reliable information in decision making in forensic sciences. This systematic review aimed to report on the application and performance of AI technology in FO. The data was gathered through searching for the articles in the renowned search engines, which have been published between January 2000 - June 2020. QUADAS-2 was adopted for the risk of bias analysis of the included studies. AI technology has been widely applied in FO for identifying bite-marks, predicting mandibular morphology, gender determination, and age estimation. Most of these AI models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). The results of the studies are promising. Studies have reported that these models display accuracy and precision equivalent to that of the trained examiners. These models can be promising tools when identifying victims of mass disasters and as an additive aid in medico-legal situations.
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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, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Divison of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Sachin Naik
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 1143, Saudi Arabia
| | - Abdulaziz A Al-Kheraif
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 1143, Saudi Arabia
| | - Darshan Devang Divakar
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 1143, Saudi Arabia
| | - Sachin C Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y.Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, Maharashtra, India
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Divison of Operative Dentistry, College of Dentistry, Jazan University, Saudi Arabia
| | - Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan, Saudi Arabia
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