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Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms
, 1, 2, 2, 2, 2, 3, 4
Affiliations- Department of Anatomy, Faculty of Medicine, Düzce University, Düzce, Turkey
- Department of Anatomy, Faculty of Medicine, Karabük University, Karabük, Turkey
- Department of Radiology, Faculty of Medicine, İzmir Bakırçay University, İzmir, Turkey
- Department of Anatomy, Faculty of Medicine, İzmir Bakırçay University, İzmir, Turkey
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Abstract
Background: The aim of this study is to predict sex with machine learning (ML)
algorithms by making morphometric measurements on radiological images of the
first and fifth metatarsal and phalanx bones.
Materials and methods: In this study, radiologic images of 263 individuals (135
female, 128 male) between the ages of 27 and 60 were analysed retrospectively.
The images in digital imaging and communications in medicine (DICOM) format
were transferred to personal workstation Radiant DICOM Viewer programme.
Length and width measurements of the first and fifth metatarsal and foot phalanx
bones were performed on the transferred images. In addition, the ratios of the
total length of the first proximal and distal phalanx and length of the first metatarsal
and total length of fifth proximal, middle, and distal phalanx and maximum
length of fifth metatarsal were calculated.
Results: As a result of machine learning algorithms, highest accuracy, specificity,
sensitivity, and Matthews correlation coefficient values were found as 0.85, 0.86,
0.85, and 0.71, respectively with decision tree algorithm. It was found that accuracy
rates of other algorithms varied between 0.74 and 0.83.
Conclusions: As a result of our study, it was found that sex estimation was made
with high accuracy rate by using machine learning algorithms on X-ray images
of the first and fifth metatarsal and foot phalanx. We think that in cases when
pelvis, cranium and long bones are harmed and examination is difficult, bones
of the first and fifth metatarsal and foot phalanx can be used for sex estimation.
Abstract
Background: The aim of this study is to predict sex with machine learning (ML)
algorithms by making morphometric measurements on radiological images of the
first and fifth metatarsal and phalanx bones.
Materials and methods: In this study, radiologic images of 263 individuals (135
female, 128 male) between the ages of 27 and 60 were analysed retrospectively.
The images in digital imaging and communications in medicine (DICOM) format
were transferred to personal workstation Radiant DICOM Viewer programme.
Length and width measurements of the first and fifth metatarsal and foot phalanx
bones were performed on the transferred images. In addition, the ratios of the
total length of the first proximal and distal phalanx and length of the first metatarsal
and total length of fifth proximal, middle, and distal phalanx and maximum
length of fifth metatarsal were calculated.
Results: As a result of machine learning algorithms, highest accuracy, specificity,
sensitivity, and Matthews correlation coefficient values were found as 0.85, 0.86,
0.85, and 0.71, respectively with decision tree algorithm. It was found that accuracy
rates of other algorithms varied between 0.74 and 0.83.
Conclusions: As a result of our study, it was found that sex estimation was made
with high accuracy rate by using machine learning algorithms on X-ray images
of the first and fifth metatarsal and foot phalanx. We think that in cases when
pelvis, cranium and long bones are harmed and examination is difficult, bones
of the first and fifth metatarsal and foot phalanx can be used for sex estimation.
Keywords
decision tree, machine learning algorithms, metatarsus, phalanx, radiography, sex prediction, X-ray
About this articleTitle
Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms
Journal
Issue
Article type
Original article
Pages
704-711
Published online
2022-05-20
Page views
1296
Article views/downloads
866
DOI
Pubmed
Bibliographic record
Folia Morphol 2023;82(3):704-711.
Keywords
decision tree
machine learning algorithms
metatarsus
phalanx
radiography
sex prediction
X-ray
Authors
D. Senol
F. Bodur
Y. Secgın
R. S. Bakıcı
N. E. Sahin
S. Toy
S. Öner
Z. Oner
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