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A web application for sex and stature estimation from radiographic proximal femur for a Thai population
Institution:1. PhD Candidate in Forensic Osteology and Odontology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;2. Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai 50200, Thailand;3. Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai 50200, Thailand;4. College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;5. Department of Forensic Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;6. Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand;7. Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;8. Excellence Center in Osteology Research and Training Center (ORTC), Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;1. Instituto de Investigación en Genética Molecular, Universidad de Guadalajara (CUCiénega-UdeG), Av. Universidad 1115, Paso Blanco, Ocotlán, Jalisco CP 47810, Mexico;2. Laboratorio DNA Profile, Delgadillo Araujo #305, Col. El Rosario, Ocotlán, Jalisco CP 47849, Mexico;1. Guilloz Imaging Department, Central Hospital, CHRU Nancy, F-54000, France;2. Forensic Institute, Brabois Adultes Hospital, CHRU Nancy, F-54000, France;3. Lorraine University, Inserm, IADI, F-54000 Nancy, France;1. Division of Legal Medicine, Department of Social Medicine, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake-cho, Miyazaki 889-1692, Japan;2. Radiology Division, Faculty of Medicine, University of Miyazaki Hospital, Miyazaki, Japan;3. Medical Student, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake-cho, Miyazaki 889-1692, Japan;4. Department of Medical Sciences, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake-cho, Miyazaki 889-1692, Japan;5. Frontier Science Research Center, University of Miyazaki, 5200 Kihara, Kiyotake-cho, Miyazaki 889-1692, Japan;1. Emergency County Hospital \"Constantin Opris\", Baia Mare, Department of Forensic Medicine, Street George Co?buc 31, Baia Mare, Maramures, 430031, Romania;2. ?Iuliu Ha?ieganu? University of Medicine and Pharmacy, Department of Internal Medicine, Street Victor Babe? 8, Cluj-Napoca, Cluj, 400347, Romania;3. University of Medicine and Pharmacy \"Carol Davila\" Bucharest Departament of Foresic Medicine, Street Bulevardul Eroii Sanitari 8, Bucharest, 050474, Romania
Abstract:In both forensic and archaeological domains, the discovery of incomplete human remains is a frequent occurrence. Nevertheless, the estimation of biological profiles from such remains presents a challenge due to the absence of crucial skeletal elements, such as the skull and pelvis. This study aimed to assess the utility of the proximal femur in the forensic identification process by creating a web application for osteometric analysis of the proximal femur. The aim was to determine the sex and stature of an individual from radiographs of the left anteroposterior femur. To accomplish this, an automated method was developed for acquiring linear measurements from radiographic images of the proximal femur using Python tools. The application of Hough techniques and Canny edge detection was utilized to generate linear femoral dimensions from radiographs. A total of 354 left femora were radiographed and measured by the algorithm. The sex classification model employed in this study was the Naïve Bayes algorithm (accuracy = 91.2 %). Results indicated that Gaussian process regression (GPR) was the most effective method for estimating stature (mean error = 4.68 cm, SD = 3.93 cm). The proposed web application holds the potential to serve as a valuable asset in the realm of forensic investigations in Thailand, particularly in the estimation of biological profiles from fragmentary skeletal remains.
Keywords:Forensic anthropology  Proximal femur  Web application  Sex estimation  Stature estimation  FNAL"}  {"#name":"keyword"  "$":{"id":"k0035"}  "$$":[{"#name":"text"  "_":"femoral neck axis length  FND"}  {"#name":"keyword"  "$":{"id":"k0045"}  "$$":[{"#name":"text"  "_":"femoral neck diameter  FHD"}  {"#name":"keyword"  "$":{"id":"k0055"}  "$$":[{"#name":"text"  "_":"femoral head diameter  PF"}  {"#name":"keyword"  "$":{"id":"k0065"}  "$$":[{"#name":"text"  "_":"proximal femur length  ML"}  {"#name":"keyword"  "$":{"id":"k0075"}  "$$":[{"#name":"text"  "_":"machine learning  SVM"}  {"#name":"keyword"  "$":{"id":"k0085"}  "$$":[{"#name":"text"  "_":"support vector machine  KNN"}  {"#name":"keyword"  "$":{"id":"k0095"}  "$$":[{"#name":"text"  "_":"k-nearest neighbor  ANN"}  {"#name":"keyword"  "$":{"id":"k0105"}  "$$":[{"#name":"text"  "_":"artificial neural network  GPR"}  {"#name":"keyword"  "$":{"id":"k0115"}  "$$":[{"#name":"text"  "_":"Gaussian process regression
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