open access

Vol 82, No 3 (2023)
Original article
Submitted: 2022-03-24
Accepted: 2022-05-11
Published online: 2022-05-20
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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

D. Senol1, F. Bodur1, Y. Secgın2, R. S. Bakıcı2, N. E. Sahin2, S. Toy2, S. Öner3, Z. Oner4
·
Pubmed: 35607870
·
Folia Morphol 2023;82(3):704-711.
Affiliations
  1. Department of Anatomy, Faculty of Medicine, Düzce University, Düzce, Turkey
  2. Department of Anatomy, Faculty of Medicine, Karabük University, Karabük, Turkey
  3. Department of Radiology, Faculty of Medicine, İzmir Bakırçay University, İzmir, Turkey
  4. Department of Anatomy, Faculty of Medicine, İzmir Bakırçay University, İzmir, Turkey

open access

Vol 82, No 3 (2023)
ORIGINAL ARTICLES
Submitted: 2022-03-24
Accepted: 2022-05-11
Published online: 2022-05-20

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.

Get Citation

Keywords

decision tree, machine learning algorithms, metatarsus, phalanx, radiography, sex prediction, X-ray

About this article
Title

Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms

Journal

Folia Morphologica

Issue

Vol 82, No 3 (2023)

Article type

Original article

Pages

704-711

Published online

2022-05-20

Page views

1277

Article views/downloads

847

DOI

10.5603/FM.a2022.0052

Pubmed

35607870

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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