دورية أكاديمية
Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs.
| العنوان: | Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs. |
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| المؤلفون: | Ciconelle ACM; Machiron Ltd., São Paulo, Brazil.; Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil., da Silva RLB; Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil.; Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, Republic of Korea., Kim JH; Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil.; Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, Republic of Korea., Rocha BA; Machiron Ltd., São Paulo, Brazil., Dos Santos DG; Machiron Ltd., São Paulo, Brazil., Vianna LGR; Machiron Ltd., São Paulo, Brazil., Gomes Ferreira LG; Machiron Ltd., São Paulo, Brazil., Pereira Dos Santos VH; Machiron Ltd., São Paulo, Brazil.; Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil., Costa JO; Papaiz Associados Diagnosticos Por Imagem S.A., São Paulo, Brazil., Vicente R; Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil. |
| المصدر: | Journal of forensic sciences [J Forensic Sci] 2023 Nov; Vol. 68 (6), pp. 2057-2064. Date of Electronic Publication: 2023 Sep 25. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Blackwell Pub Country of Publication: United States NLM ID: 0375370 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1556-4029 (Electronic) Linking ISSN: 00221198 NLM ISO Abbreviation: J Forensic Sci Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2006- : Malden, MA : Blackwell Pub. Original Publication: [Chicago, Ill.] : Callaghan and Co., 1956- |
| مواضيع طبية MeSH: | Deep Learning*, Humans ; Female ; Male ; Radiography, Panoramic ; Brazil ; Neural Networks, Computer ; Algorithms |
| مستخلص: | The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study involved 207,946 panoramic dental X-rays and their corresponding reports from 15 clinical centers in São Paulo, Brazil. The PRs were acquired with four different devices, and 58% of the patients were female. Data preprocessing included anonymizing the exams, extracting pertinent information from the reports, such as sex, age, type of dentition, and number of missing teeth, and organizing the data into a PostgreSQL database. Two neural network architectures, a standard CNN and a ResNet, were utilized for sex classification, with both undergoing hyperparameter tuning and cross-validation to ensure optimal performance. The CNN model achieved 95.02% accuracy in sex estimation, with image resolution being a significant influencing factor. The ResNet model attained over 86% accuracy in subjects older than 6 years and over 96% in those over 16 years. The algorithm performed better on female images, and the area under the curve (AUC) exceeded 96% for most age groups, except the youngest. Accuracy values were also assessed for different dentition types (deciduous, mixed, and permanent) and missing teeth. This study demonstrates the effectiveness of an AI-driven tool for sex determination using PR and emphasizes the role of image resolution, age, and sex in determining the algorithm's performance. (© 2023 American Academy of Forensic Sciences.) |
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| فهرسة مساهمة: | Keywords: convolutional neural network; deep learning; machine learning; panoramic radiograph; sex estimate accuracy; sex estimation |
| تواريخ الأحداث: | Date Created: 20230925 Date Completed: 20231027 Latest Revision: 20231027 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1111/1556-4029.15376 |
| PMID: | 37746788 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1556-4029 |
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| DOI: | 10.1111/1556-4029.15376 |