An Determine The Age Range Based On Machine-Learning Methods From Skeletal Angles Of The Face (Glabella And Maxilla Angle And Length And Width Of Piriformis) In A CT Scan

Abstract

Background : One of the main steps in identifying a person in forensic medicine is determining age from skeletal remains, including the skull. This study aimed to investigate the possibility of predicting age from facial angles (glabella, piriformis, and maxillary angle and measuring peripheral length and width) with artificial intelligence in CT scans.

Methods: The study method is cross-sectional, using a questionnaire and simple random sampling.CT scan samples that can be accurately measured are selected. For exclusion criteria, gender uncertainty and the possibility of measurement based on the quality of the CT scan, the researchers examined the angles of the face (angle of the glabella and maxilla and length and width of the piriformis) for 100 men and 100 female. The mean, standard deviation of the age was 39.16 ± 2.22 years for men and 47.84 ± 2.46 years for women. The samples were classified based on age differences, and then the data were analyzed using machine learning algorithms to determine the age group.

Results: After determining the exact amount of measurement, the data were evaluated by machine learning algorithms to determine the age group. Accordingly, in the age group classification based on the World Health Organization (with an age difference of 10 years)(years±5) with 100% accuracy and in the second classification (with an age difference of 5 years)(years±2.5) with 88% accuracy and 79% precision of the age group was predicted.

Conclusion: The obtained data show the importance of new artificial intelligence methods, including machine learning, in providing new methods for determining age groups (age±2.5) through skull angles with high accuracy in cases where even cranial remains are found in identification in forensic medicine.

Keywords: Identificatio glabella angle maxillary angle piriformis length piriformis width Machine learning Artificial intelligence age estimation How to Cite

Mohtarami, S. A., & Hejaz, S. A. (2022). An Determine The Age Range Based On Machine-Learning Methods From Skeletal Angles Of The Face (Glabella And Maxilla Angle And Length And Width Of Piriformis) In A CT Scan. International Journal of Medical Toxicology and Forensic Medicine, 12(4), 38605. https://doi.org/10.32598/ijmtfm.v12i4.38605

References

1.Knight B, Saukko P. Knight's forensic pathology,4th Edition,2016
2. DiMaio D , Vincent B.DiMaio's Forensic Pathology.2nd Edition,2001
3.Byard R, Corey T, Henderson C, James JP. Encyclopedia of forensic and legal medicine. First edition. London, UK: Academic press; 2005, P 96, 98, 103, 1105, 1271
4.Akhlaghi Mitra,Salavati Mahdi. Mandibulo–canine Index Value For Sex Identification. Tehran University Medical Journal(TUMJ) . MARCH 2008; 65)12(;66 -71.
5. White Sc, Pharoah MJ. Oral radiology: principles and interpretation. 6th ed. St. Louis: Mosby Elsevier; 2008. pp. 209-211.
6.Shaw RB Jr, Katzel EB, Koltz PF, Kahn DM, Girotto JA, Langstein HN. Aging of the mandible and its aesthetic implication. Plast Reconstr Surg 2010; 125(1): 332-42.
7.Kahan DM, Shaw RB Jr. Aging of the bony orbit: A three-dimentional computed tomography. Aesthet Surg J. 2008;28(3): 258-64.
8.Mendelson BC, Hartley W, Scott M, McNab A, Granzow JW Brayan C. Age-related change of the orbit and midcheek and implication for facial rejuvenation. Aesthetic Plast Surg. 2007; 31(5): 419-23.
9.Richard MJ, Morris C, Deen BF, Gray L, Woodward JA. Analysis of the anatomic changes of the facial skeleton using computer-assisted tomography. Opthal Plast Reconstr Surg. 2009; 25(5): 382-6.
10.Mendelson B, Wong CH. Changing in the fasial skeleton with aging: implication and clinical applications in facial rejuvenation. Aesth Plast Surg 2012; 36(4): 753-60.
11.Coleman SR, Grover R. The anatomy of the aging face: volume lossand changes in 3-dimentional topography.Aesthet Surg J 2006; 26(1S): S4-6.
12.Peter L. Williams - Gray's Anatomy: 39th (eigth) Edition.2005
13.Furuta M. Measurment of orbital volume by computed tomography: especially on the growth of the orbit. JPJ Ophtalmol 2001; 45(6): 600-6.
14.M. Orfila lenbch der gerichtlichen mediyin, Christian Ernst Kollmann Leipzig, 1948, pp: 197
15.K. Langer, Das wachstum des menschlichen skeletts wmitt bezug out den risen denksch. Kaiserl. Akad wiss (Math- nat klasse) 31 1871, PP: 72 – 76.
16.C Toldt, Die nochen in gerichtsarztlicher Bezehungin von maschka, (O.V.), Hand buch der gerichtlichen medizin, Laupp'sche Buchhandlung, Tubingen, 1882 p 483
17.P Topinard, Elements d' Anthropologie general, paris, 1885, P: 730
18.J, Beddoe, On the stature of the olders races of England as estimated from the long bones, JRAI 17 (1887/1888) 202 – 207
20.Soo Jin Kim, So Jung Kim.Analysis of Age-Related Changes in Asian Facial Skeletons Using 3D Vector Mathematics on Picture Archiving and Communication System Computed Tomography.Yonsei Med J 2015 Sep;56(5):1395-1400
21.Robert B. Shaw, Jr.Facial Bone Density : Effects of Aging and Impact on Facial Rejuvenation.Aesthetic Surgery Journal 2012 32(7): 937 -943
22.Boris Paskhover,David Durand.Patterns of Change in Facial Skeletal Aging.JAMA Facial Plast Surg. 2017;19(5):413-417.
23. David Buziashvili,Jacob L. Long-term Patterns of Age-Related Facial Bone Loss in Black Individuals. JAMA Facial Plastic Surgery. 2019;4(4):301-311
24.Gaël Varoquaux , Veronika Cheplygina.Machine learning for medical imaging: methodological failures and recommendations for the future.npj Digital Medicine.2022 April;5(48)
25.Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Corrigendum: dermatologist-level classification of skin cancer with deep neural networks. Nature. (2017) 546:686.
26.Li Y, Shan B, Li B, Liu X, Pu Y.Literature review on the applications of machine learning and blockchain technology in smart healthcare industry: A bibliometric analysis.Journal of Healthcare Engineering. 2021;Article 739219
27.Brnabic A, Hess LM.Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making .BMC Medical Informatics and Decision Making. 2021,21(1): 54-65
28.Sabetia M ,Boostanib R ,Moradic E ,HosseinShakoor M .Machine learning-based identification of craniosynostosis in newborns.Machine Learning with Applications.2022 June 8(15)

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