Determination of sex from the patella in a contemporary Spanish population

Peckmann, Tanya R.; Meek, Susan; Dilkie, Natasha; Rozendaal, Andrew S.

doi:10.1016/j.jflm.2016.09.007

Cited by 29 publications

(23 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This revealed that the patella is dimorphic based on gender. Thus, discriminant function equations obtained with the variables could be used in gender determination (1). Similar findings were reported for South African white (20), South African black (22), Thai (23), Japanese (24), South Italian (25), and Medieval German (4) communities.…”

Section: Discussionsupporting

confidence: 71%

“…In forensic anthropology, it is essential to accurately define the measurement parameters. Thus, researchers using these parameters could understand the definitions and reproduce the measurements (1,5). Previous studies showed that different populations have different bone characteristics, and these characteristics may affect the process of identifying sex (17)(18)(19) .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Gender by Using Decision Tree, a Machine Learning Algorithm, With Patellar Measurements Obtained From MDCT Images

2021

View full text Add to dashboard Cite

The present study aimed to analyze whether gender could be determined with the decision tree (DT) method, a machine learning algorithm, based on patellar multidetector computed tomography (MDCT) image measurements. Material and Methods:The study was conducted on 219 male and 131 female MDCT images. The patellar anteroposterior (Ap), craniocaudal (Cc), transverse (Trv) length and volume (Vol), adjusted on the orthogonal plane by the radiologist, were calculated. In patellar length measurements, initially linear discriminant outliers were detected to clear the data for gender prediction. Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), F1-Score (F1) and Matthew's Correlation Coefficient (Mcc) criteria were taken as the performance criteria for DT. Results: It was determined that male Ap, Trv, Cc, and Vol values were higher when compared to the female values and there was a significant difference between these values based on gender (p Ap, Trv, Cc, Vol = 0.000). Using the above-mentioned measurements, it was calculated that the prediction rate for male individuals was 98.2% and for female individuals, it was 98.4%. Conclusion: DT analysis based on patella morphometry provided a simple, adequate and highly accurate approach for gender estimation. Furthermore, it was determined that it would provide an advantage for researchers in gender prediction using only branching and cut-off values on the tree structure without the need to use a computer.

show abstract

Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Estimation of Gender by Using Decision Tree, a Machine Learning Algorithm, With Patellar Measurements Obtained From MDCT Images

2021

View full text Add to dashboard Cite

show abstract

“…One study considered the diseased patella, while another used CT scans or cadaveric specimens. Both ignored the precise measurement of the patellar cartilage in the healthy population 25,26 . These studies are neither reliable nor powerful to meet the normal patellar morphology, native patellar thickness, and to reproduce the normal knee joint biomechanics since they ignored the measurement of the patella's chondral structure.…”

Section: Discussionmentioning

confidence: 99%

The Mathematical Relationship Among Normal Patellar Dimensions, to Find the Pre-Diseased Patellar Thickness in Different Populations

Kerdari¹,

Rastegar²,

Mustafa³

et al. 2020

Preprint

View full text Add to dashboard Cite

Whether resurfacing or not resurfacing the patella during total knee arthroplasty (TKA) still is a challenge to orthopedic surgeons. A significant reason for this controversy is the far from perfect outcomes of both techniques, resulting from inadequate knowledge of normal patellar dimensions in a diseased one. The primary purpose of the current study is to find the pre-diseased patellar dimensions and the ethnic differences in patellar dimensions. We measured the patella's dimensions on 927 normal young adult knee MRIs from seven different ethnicities. Besides comparing the dimensions between sexes, ages, and sides, we analyzed the differences among ethnic groups. The average thickness was 25.12±2.33 mm; the average width was 44.57±4.32 mm, the average articular surface length was 32.69±3.75 mm, with significant gender, age, and ethnic differences. There were also significant differences in dimensions among ethnic groups, except for between the Indians and Far Eastern Asians and between the Arabs and North Africans. We could also find a robust mathematical relationship between the patella's width, length, and thickness. The ethnic differences in patellar dimensions found in this study can help optimize surgical technique and implant designs for patellar resurfacing. The mathematical equation will help the surgeons find the normal, pre-diseased patella thickness to prevent over-or under-stuffing during the patellar resurfacing.

show abstract

“…In literature there are a large number of various parameters and measurement methods described to characterize the patellar morphology [5][6][7]1,8,9]. However, the majority rely on the manual measurement of subjectively defined landmarks in medical image data or cadaveric bones using callipers.…”

Section: Introductionmentioning

confidence: 99%

“…In case of normality the parameters were described according to mean and standard deviation. Genders were treated separately, since previous showed statistical significant differences [9,6].…”

Section: Introductionmentioning

confidence: 99%

Automatic analysis of morphological parameters of the patella based on three-dimensional (3D) surface data

Asseln¹,

Radermacher²

EPiC Series in Health Sciences

View full text Add to dashboard Cite

Morphological knowledge about the patella is fundamental to understand normal and pathological knee joint mechanics and guide the design of patellar prosthesis. In literature there are a large number of parameters and measurement methods described to characterize the patellar morphology. However, the majority rely on manual measurements, sample sizes are often small and the number of morphological parameters is limited. The goal of this study was to identify morphological parameters of the patella, develop a standardized and fully automatic workflow for their extraction, and to provide accurate statistical numbers for a large number of patients.An anonymized dataset of 412 knee geometries from consecutively scheduled total knee arthroplasty patients (gender: 248 female, 164 male) were available. A total number of 8 morphological parameters were identified and later used for the calculation of 3 additional aspect ratios. A fully automatic workflow was developed for parameter extraction based on the three-dimensional surface data. The workflow was applied to all datasets and the results were stored in a database for statistical analysis.The workflow could process all 412 patellar geometries fully automatic without any algorithmic adjustment or user interaction. The processing time was in the range of 30 s per case. The results of the statistical analysis were in very good agreement with the literature. The numbers clearly demonstrated gender-specific differences, however, it is still unknown whether these are sexual dimorphism or can be eliminated by a scaling. Extensive statistical analysis on gender-specific differences and their normalization is part of future work.

show abstract

Determination of sex from the patella in a contemporary Spanish population

Cited by 29 publications

References 15 publications

Estimation of Gender by Using Decision Tree, a Machine Learning Algorithm, With Patellar Measurements Obtained From MDCT Images

Estimation of Gender by Using Decision Tree, a Machine Learning Algorithm, With Patellar Measurements Obtained From MDCT Images

The Mathematical Relationship Among Normal Patellar Dimensions, to Find the Pre-Diseased Patellar Thickness in Different Populations

Automatic analysis of morphological parameters of the patella based on three-dimensional (3D) surface data

Contact Info

Product

Resources

About