Estimation of Age implementing pulp tooth volume ratio in maxillary canines of an Indian population by means of CBCT scans

Author:

Jadhav Shreya11,Jhaveri Aditi11,Pawar Ajinkya M.11,Bhardwaj Anuj2,Vinay Vineet3,di Blasio Marco4,Cicciù Marco5,Minervini Giuseppe67

Affiliation:

1. Department of Conservative Dentistry and Endodontics, Nair Hospital Dental College, Mumbai, India

2. Department of Conservative Dentistry and Endodontics, College of Dental Sciences & Hospital, Indore, India

3. Department of Public Health Dentistry, Sinhgad Dental College & Hospital, Pune, India

4. University Center of Dentistry, Department of Medicine and Surgery, University of Parma, Parma, Italy

5. Department of Biomedical and Surgical and Biomedical Sciences, Catania University, Catania, Italy

6. Multidisciplinary Department of Medical-Sugical and Odontostomatological Specialties, University of Campania “Luigi Vanvitelli”, Naples, Italy

7. Saveetha Dental College & Hospitals, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai, India

Abstract

BACKGROUND: Forensic odontology is a subfield of forensics that can assist in determining age, gender, and other demographic factors. OBJECTIVE: The focus of this research is to ascertain age by tooth volume ratio of permanent maxillary canines using a CBCT scan. METHODS: In this retrospective investigation, 1000 CBCT images were gathered and focused on only fully developed maxillary canines. For each of the maxillary canines, the researchers measured and calculated the volume of the pulp chamber, root canal, and entire tooth using the ITK Snap programme. The pulp volume to tooth volume ratio was computed. The equation AGE = 59.208 - (583.652 x pulp volume/tooth volume) was then used to compute age. The samples were divided into 5 age groups; 13–20, 21–30, 31–40, 41–50, and 51–60. The age was calculated for samples in each group and it was compared to the actual age. RESULTS: Between individuals’ actual age and computed age, the study discovered a statistically significant positive association. While groups 3, 4, and 5 demonstrated positive but weaker correlations (r= 0.854, r= 0.658, and r= 0.612, respectively), groups 1 and 2 displayed statistically significant strong positive correlations (r= 0.987 and r= 0.923, respectively). The average total discrepancy between the age that was computed and the actual age was 2.45 1.344. CONCLUSION: The study found a statistically significant positive correlation between computed age and actual age, with the younger age groups 1 (13–20) and 2 (21–30) showing the strongest relationships. This underlines its potential success in court, particularly when dealing with young criminals.

Publisher

IOS Press

Subject

Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics

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