التفاصيل البيبلوغرافية
| العنوان: |
Computed tomographic age estimation from the iliac crest and ischial tuberosity in an Indian population using supervised machine learning approaches. |
| المؤلفون: |
Warrier V; Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India, 342005., Shedge R; School of Forensic Sciences, National Forensic Sciences University, Tripura, India, 799001., Garg PK; Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India, 342005., Dixit SG; Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, India, 342005., Krishan K; Department of Anthropology (UGC Centre of Advanced Study), Panjab University, Chandigarh, India, 160014., Kanchan T; Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India, 342005. |
| المصدر: |
Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur [Anthropol Anz] 2024 Jun 03; Vol. 81 (3), pp. 301-314. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: E Schweizerbartsche Country of Publication: Germany NLM ID: 0372377 Publication Model: Print Cited Medium: Print ISSN: 0003-5548 (Print) Linking ISSN: 00035548 NLM ISO Abbreviation: Anthropol Anz Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: Stuttgart : E Schweizerbartsche |
| مواضيع طبية MeSH: |
Ilium*/diagnostic imaging , Ilium*/anatomy & histology , Ischium*/diagnostic imaging , Ischium*/anatomy & histology , Age Determination by Skeleton*/methods , Tomography, X-Ray Computed*/methods , Anthropology, Physical*, Humans ; India ; Adult ; Male ; Female ; Young Adult ; Adolescent ; Middle Aged ; Supervised Machine Learning ; Child |
| مستخلص: |
Within the pelvis the iliac crest and ischial tuberosity display delayed ossification and fusion, thus, presenting as reliable maturity indicators. Amongst the different iliac crest and ischial tuberosity age estimation methods, the modified Kreitner-Kellinghaus stages constitute one of the more promising methods. The present study was directed towards establishing the applicability of the modified Kreitner-Kellinghaus method using five supervised machine learning approaches. Clinical CT scans of consenting individuals were collected and scored using the modified Kreitner-Kellinghaus method for the iliac crest and ischial tuberosity, independently. Age was subsequently estimated using different machine learning models. Cumulative scores computed from both markers were additionally employed for age estimation using machine learning. For iliac crest age estimation, Random Forest and Gradient Boosting Regression furnished lowest mean absolute error (2.42 years) and root mean square error (3.06 years). For ischial tuberosity age estimation, Gradient Boosting Regression garnered the lowest computations of mean absolute error (2.60 years) and root mean square error (3.09 years). For cumulative score based age estimation, Support Vector Regression and Gradient Boosting Regression yielded lowest mean absolute error (2.48 years) and root mean square error (3.07 years). Obtained error computations indicate that the iliac crest is a more accurate age marker in comparison to the ischial tuberosity. Additionally, cumulative score-based approaches garnered similar/ marginally more precise results in comparison to the iliac crest with all five models. This marginal improvement is not sufficient to justify employing the relatively more complicated cumulative score-based approach for age estimation. Hence, whenever available, the iliac crest should be preferred over the ischial tuberosity/ cumulative score-based approaches for age estimation. |
| تواريخ الأحداث: |
Date Created: 20231023 Date Completed: 20240515 Latest Revision: 20240515 |
| رمز التحديث: |
20240515 |
| DOI: |
10.1127/anthranz/2023/1723 |
| PMID: |
37869936 |
| قاعدة البيانات: |
MEDLINE |
