Virtual Morphometry of the First Lumbar Vertebrae for Estimation of Sex Using Computed Tomography Data in the Turkish Population

Bozdağ, Mustafa; Karaman, Gokçe

doi:10.7759/cureus.16597

Cited by 3 publications

(6 citation statements)

References 22 publications

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“…14 Numerous studies have stated that sexual dimorphism is most correlated with the size of the vertebral body, and lumbar vertebrae offer an accuracy range of 47.0% to 94.5% for sex determination. 3,4,9,[15][16][17][18][19][20][21] The lumbar upper endplate depth and width variables are found to be useful for sex determination by discriminant function analysis. 13 Sex determination studies based on lumbar vertebral morphometry utilize various techniques, including computed tomography (CT), magnetic resonance imaging (MRI), and dry bone.…”

Section: Introductionmentioning

confidence: 99%

“…Morphometric assessment is capable of sex determination from all levels of lumbar vertebrae, with accuracy dependent upon the level of lumbar vertebra used, the parameters, study population, and measurement methods. 3,4,9,[15][16][17][18][19][20][21] Although morphometric methods are effective, they are highly dependent on the observer's experience to assess the parameters, and they can still be subject to measurement errors. 22 In recent years, deep learning (DL) has been increasingly included in various studies because it automatically extracts features from numerous images and then performs image classification.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population

et al. 2022

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Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep learning and morphometric technique to perform sex determination from lumbar vertebrae in a Thai population. A total of 1100 lumbar vertebrae (L1-L5) from 220 Thai individuals (110 males and 110 females) were obtained from the Forensic Osteology Research Center, Faculty of Medicine, Chiang Mai University, Thailand. In addition, two linear measurements of superior and inferior endplates from the digital caliper and image analysis were carried out for morphometric technique. Deep learning applied image classification to the superior and inferior endplates of the lumbar vertebral body. All lumbar vertebrae images are included in the dataset to increase the number of images per class. The accuracy determined the performance of each technique. The results showed the accuracies of 82.7%, 90.0%, and 92.5% for digital caliper, image analysis, and deep learning techniques, respectively. The lumbar vertebrae L1-L5 exhibit sexual dimorphism and can be used in sex estimation. Deep learning is more accurate in determining sex than the morphometric method. In addition, the subjectivity and errors in the measurement are decreased. Finally, this study presented an alternative approach to determining sex from lumbar vertebrae when the more traditionally used skeletal elements are incomplete or absent.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population

et al. 2022

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“…Most previous literature has described conventional morphometric methods to estimate sex from the vertebrae. Classification rates have mainly fluctuated between 85 and 90% [9][10][11][12][13][14][15][16][17][18][19][20], and the accuracy of the current best algorithm also falls within this range. In comparison to a previous Thai study [25] that developed a deep learning algorithm for sex estimation from L1 to L5, the accuracy was somewhat lower (86.4% vs. 92.5%).…”

Section: Discussionmentioning

confidence: 99%

“…These cases in particular may benefit from novel sex estimation methods. The vertebrae display clear sex discrepancy in the adult spine [8] and have proven accurate in conventional morphometric sex estimation (classification rates ~ 85 to ~ 90%, maximum 94.5%) [9][10][11][12][13][14][15][16][17][18][19][20]. However, as conventional osteology methods may be laborious and only able to focus on a limited number of parameters at a time, the application of modern image recognition techniques may increase accuracy and expedite processes.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra—a proof-of-concept study

Oura

Korpinen

Machnicki

et al. 2023

Forensic Sci Med Pathol

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Sex estimation is a key element in the analysis of unknown skeletal remains. The vertebrae display clear sex discrepancy and have proven accurate in conventional morphometric sex estimation. This proof-of-concept study aimed to investigate the possibility to develop a deep learning algorithm for sex estimation even from a single peripheral quantitative computed tomography (pQCT) slice of the fourth lumbar vertebra (L4). The study utilized a total of 117 vertebrae from the Terry Anatomical Collection. There were 58 male and 59 female cadavers, all of the white ethnicity, with the average age at death 49 years and a range of 24 to 77 years. A coronal pQCT scan was taken from the midway of the L4 corpus. Sex estimation was performed in a total of 19 neural network architectures implemented in the AIDeveloper software. Of the explored architectures, a LeNet5-based algorithm reached the highest accuracy of 86.4% in the test set. Sex-specific classification rates were 90.9% among males and 81.8% among females. This preliminary finding advances the field by encouraging and directing future research on artificial intelligence-based methods in sex estimation from individual skeletal traits such as the vertebrae. Combining quickly obtained imaging data with automated deep learning algorithms may establish a valuable pipeline for forensic anthropology and provide aid when combined with traditional methods.

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Anthropometric evaluation of side, sex and age by radiological examination of the normal ankle joint among adult Egyptian population

Nasr El-Din,

Mohammed Ali,

Madani

et al. 2023

AGJSR

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PurposeSex and age estimation is important, particularly when information about the deceased is unavailable. There are limited radiological studies investigating side, sex and age differences in normal ankle morphometric parameters. The authors’ goal was to evaluate different ankle joint morphometric measurements and document variations among Egyptians.Design/methodology/approachA prospective study was conducted throughout 23 months on 203 (100 males and 103 females) adult Egyptians, aged between 20-69 years old, who were referred for a plain x-ray of bilateral normal ankle joints.FindingsAnkle parameters showed no statistical difference between both sides, except for tarsal width (TaW) which was significantly higher on right than left side (26.92 ± 2.66 vs 26.18 ± 2.65 mm). Males showed significantly higher morphometric values except for anteroposterior gap (APG) and talus height (TaH) which were significantly higher in females (2.29 ± 0.80 vs 1.80 ± 0.61 mm and 13.01 ± 1.68 vs 11.87 ± 1.91 mm, respectively). There was significant increase in tibial arc length, APG, distance of level of MTiTh from anterior limit of mortise, distance of level of MTiTh from vertex of mortise, sagittal distance between tibial and talar vertices and sagittal radius of trochlea tali arc in old age group compared to young one. A significant decrease in tibial width, malleolar width, TaW and TaH was noted in old age group compared to young one.Originality/valueAnkle joints of both sides are mostly symmetrical; however, there are significant differences in most morphometric values due to sex and age factors. These findings may be essential during side, sex and age determination.

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Virtual Morphometry of the First Lumbar Vertebrae for Estimation of Sex Using Computed Tomography Data in the Turkish Population

Abstract: Bozdag et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cited by 3 publications

References 22 publications

Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population

Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population

Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra—a proof-of-concept study

Anthropometric evaluation of side, sex and age by radiological examination of the normal ankle joint among adult Egyptian population

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