Deep learning in sex estimation from knee radiographs – A proof-of-concept study utilizing the Terry Anatomical Collection.
• There is lack of studies exploring the knee in artificial intelligence-based sex estimation. • This study tested deep learning in sex estimation from radiographs of reconstructed cadaver knee joints. • Of the explored algorithms, an MhNet-based model reached the highest overall testing accuracy of...
| Publicado en: | Legal Medicine Vol. 61 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162209571&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162209571 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13446223 KIS jtl: Legal Medicine issn: 13446223 maglogo: N pubinfo: dt: Mar2023 vid: 61 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 162209571 162209571 162209571 10.1016/j.legalmed.2023.102211 162209571 ppct: 1 formats: tig: atl: Deep learning in sex estimation from knee radiographs – A proof-of-concept study utilizing the Terry Anatomical Collection. aug: au: Oura, Petteri Junno, Juho-Antti Hunt, David Lehenkari, Petri Tuukkanen, Juha Maijanen, Heli affil: Department of Forensic Medicine, Faculty of Medicine, University of Helsinki, Helsinki, Finland sug: subj: Deep Learning Sex Determination Methods Knee Joint Radiography Algorithms Human Cadaver Software Skeleton Middle Age Aged Aged, 80 and Over Male Female Neural Networks (Computer) Descriptive Statistics Knee Joint Anatomy and Histology Artificial Intelligence Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: • There is lack of studies exploring the knee in artificial intelligence-based sex estimation. • This study tested deep learning in sex estimation from radiographs of reconstructed cadaver knee joints. • Of the explored algorithms, an MhNet-based model reached the highest overall testing accuracy of 90.3%. • These findings encourage further research on artificial intelligence-based sex estimation from the knee joint. Although knee measurements yield high classification rates in metric sex estimation, there is a paucity of studies exploring the knee in artificial intelligence-based sexing. This proof-of-concept study aimed to develop deep learning algorithms for sex estimation from radiographs of reconstructed cadaver knee joints belonging to the Terry Anatomical Collection. A total of 199 knee radiographs were obtained from 100 skeletons (46 male and 54 female cadavers; mean age at death 64.2 years, range 50–102 years) whose tibiofemoral joints were reconstructed in standard anatomical position. The AIDeveloper software was used to train, validate, and test neural network architectures in sex estimation based on image classification. Of the explored algorithms, an MhNet-based model reached the highest overall testing accuracy of 90.3%. The model was able to classify all females (100.0%) and most males (78.6%) correctly. These preliminary findings encourage further research on artificial intelligence-based methods in sex estimation from the knee joint. Combining radiographic data with automated and externally validated algorithms may establish valuable tools to be utilized in forensic anthropology. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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