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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.
Park, Se-Jin; Yang, Su; Kim, Jun-Min; Kang, Ju-Hee; Kim, Jo-Eun; Huh, Kyung-Hoe; Lee, Sam-Sun; Yi, Won-Jin; Heo, Min-Suk.
  • Park SJ; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Yang S; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 03080, South Korea.
  • Kim JM; Department of Electronics and Information Engineering, Hansung University, Seoul, 03080, South Korea.
  • Kang JH; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Kim JE; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Huh KH; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Lee SS; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Yi WJ; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea. wjyi@snu.ac.kr.
  • Heo MS; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 03080, South Korea. wjyi@snu.ac.kr.
Int J Legal Med ; 138(4): 1741-1757, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38467754
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiografía Panorámica / Determinación del Sexo por el Esqueleto / Aprendizaje Profundo Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País como asunto: Asia Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiografía Panorámica / Determinación del Sexo por el Esqueleto / Aprendizaje Profundo Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País como asunto: Asia Idioma: En Año: 2024 Tipo del documento: Article