Determining the Age Range Based on Machine-Learning Methods From Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis) in CT Scan
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 12 No. 4 (2022),
,
Page 38605
https://doi.org/10.32598/ijmtfm.v12i4.38605
Abstract
Background: One of the main steps in identifying a person in forensic medicine is determining the age of 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 a CT scan.
Methods: The cross-sectional study method is simple random sampling using a questionnaire. Accurately measurable CT scan samples are selected. For exclusion criteria, gender uncertainty, and the possibility of measurement based on CT scan quality, the researchers examined the facial angles (angle of the glabella and maxilla and length and width of the piriformis) for 100 men and 100 women. The mean±SD 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 (WHO) (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 to determine age groups (age±2.5) through skull angles with high accuracy in cases where even cranial remains are found in identification in forensic medicine.
- Identification
- Age estimation
- Glabella angle
- Maxillary angle
- Piriformis length
- Piriformis width
- Machine learning
- Artificial intelligence
How to Cite
References
CRC Press; 2016. [DOI:10.1201/b13266] [PMCID]
DiMaio V, DiMaio D. Forensic Pathology. London: CRC Press; 2001. [Link]
Payne-James J. Encyclopedia of forensic and legal medicine. New York: Elsevier Academic Press; 2005. [Link]
Akhlaghi M, Salavati M. [Mandibulo-canine index value for sex identification (Persian)]. Tehran University Medical Journal. 2008; 65(12):66 -71. [Link]
White Sc, Pharoah MJ. Oral radiology: Principles and interpretation. Maryland Heights: Mosby/Elsevier; 2008. [Link]
Shaw RB Jr, Katzel EB, Koltz PF, Kahn DM, Girotto JA, Langstein HN. Aging of the mandible and its aesthetic implication. Plastic and Reconstructive Surgery. 2010; 125(1):332-42. [DOI:10.1097/PRS.0b013e3181c2a685] [PMID]
Kahan DM, Shaw RB Jr. Aging of the bony orbit: A three-dimentional computed tomography. Aesthetic Surgery Journal. 2008; 28(3):258-64. [DOI:10.1016/j.asj.2008.02.007] [PMID]
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 Plastic Surgery volume. 2007; 31(5):419-23. [DOI:10.1007/s00266-006-0120-x] [PMID]
Richard MJ, Morris C, Deen BF, Gray L, Woodward JA. Analysis of the anatomic changes of the facial skeleton using computer-assisted tomography. Ophthalmic Plastic & Reconstructive Surgery. 2009; 25(5):382-6. [DOI:10.1097/IOP.0b013e3181b2f766] [PMID]
Mendelson B, Wong CH. Changing in the fasial skeleton with aging: Implication and clinical applications in facial rejuvenation. Aesthetic Plastic Surgery. 2012; 36(4):753-60. [DOI:10.1007/s00266-012-9904-3] [PMID] [PMCID]
Coleman SR, Grover R. The anatomy of the aging face: Volume loss and changes in 3-dimentional topography. Aesthetic Surgery Journal. 2006; 26(1S):S4-6. [DOI:10.1016/j.asj.2005.09.012] [PMID]
Varoquaux G, Cheplygina V. Machine learning for medical imaging: Methodological failures and recommendations for the future. NPJ Digital Medicine. 2022; 5(1):48. [DOI:10.1038/s41746-022-00592-y] [PMID] [PMCID]
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(7660):686. [DOI:10.1038/nature22985] [PMID]
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; 2021:9739219. [DOI:10.1155/2021/9739219] [PMID] [PMCID]
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. [DOI:10.1186/s12911-021-01403-2] [PMID] [PMCID]
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Sciense (NY). 2015; 349(6245):255–60. [DOI:10.1126/science.aaa8415] [PMID]
Sabeti M, Boostani B, Moradic E, Shakoor M. Machine learning-based identification of craniosynostosis in newborns. Machine Learning with Applications. 2022; 8:100292. [DOI:10.1016/j.mlwa.2022.100292]
Kim SJ, Kim SJ, Park JS, Byun SW, Bae JH. Analysis of age-related changes in Asian facial skeletons using 3d vector mathematics on picture archiving and communication system computed tomography. Yonsei Medical Journal. 2015; 56(5):1395-400. [DOI:10.3349/ymj.2015.56.5.1395] [PMID] [PMCID]
Sue M, Oda T, Sasaki Y, Ogura I. Age-related changes in the pulp chamber of maxillary and mandibular molars on cone-beam computed tomography images. Oral Radiology. 2018; 34(3):219-23. [DOI: 10.1007/s11282-017-0300-1] PMID]
Beddoe J. On the stature of the olders races of England as estimated from the long bones. The Journal of the Anthropological Institute of Great Britain and Ireland. 1888; 17:201-9. [DOI:10.2307/2841929]
Shaw RB Jr, Katzel EB, Koltz PF, Kahn DM, Puzas EJ, Langstein HN. Facial bone density: Effects of aging and impact on facial rejuvenation. Aesthetic Surgery Journal. 2012; 32(7):937-42. [DOI:10.1177/1090820X12462865] [PMID]
Paskhover B, Durand D, Kamen E, Gordon NA. Patterns of change in facial skeletal aging. JAMA Facial Plastic Surgery. 2017; 19(5):413-7.[DOI:10.1001/jamafacial.2017.0743] [PMID] [PMCID]
Buziashvili D, Tower JI, Sangal NR, Shah AM, Paskhover B. Long-term patterns of age-related facial bone loss in black individuals. JAMA Facial Plastic Surgery. 2019; 21(4):292-7. [DOI:10.1001/jamafacial.2019.0028] [PMID] [PMCID]
Gray H, Williams PL, Bannisterv LH. Gray’s Anatomy: The anatomical basis of medicine and surgery. Churchill Livingstone: London; 1995. [Link]
Furuta M. Measurment of orbital volume by computed tomography: Especially on the growth of the orbit. Japanese Journal of Ophthalmology. 2001; 45(6):600-6. [DOI:10.1016/S0021-5155(01)00419-1]
Vapnik VN. An overview of statistical learning theory. IEEE Transactions on Neural Networks. 1999; 10(5):988-99. [DOI: 10.1109/72.788640]
Johnson M, Schuster M, Le QV, Krikun M, Wu Y, Chen Z, et al. Google’s multilingual neural machine translation system: Enabling zero-shot translation [Internet] 2016 [Updated 2016 August]. Available from: [Link]
Topinard P. [Elements d’ Anthropologie general (French)]. Paris: A Delahaye et É. Lecrosnier; 1885. [Link]
- Abstract Viewed: 416 times
- pdf Downloaded: 211 times