Abstract
Chronological age (CA) estimation is essential in medicine, forensics, and law. Teeth are often used for this due to their reliability. The Demirjian and the Nolla methods are used to estimate dental age (DA). Both methods have strengths and weaknesses; the Demirjian method usually overestimates age, whereas the Nolla method underestimates it. Their accuracy varies among different populations. Our objective is to compare the accuracy of these methods across various age groups and to probe the effect of gender. We searched PubMed, Scopus, the Excerpta Medica database (EMBASE), the Cochrane Library, and the Web of Science for relevant articles until March 2024. We then screened for comparative studies using the Demirjian and the Nolla methods. We used the RevMan 5.4 software package (The Cochrane Collaboration, London, UK) to compare the accuracy of both methods in estimating chronological age in both genders across age groups ranging from five to 16 years. All data were pooled using a random effects model, and relevant forest plots were generated. The accuracy was calculated based on the pooled mean difference between the chronological age and that estimated by each method. Our literature search identified 25 articles for inclusion in the review. The Demirjian method overestimated the age in males by 0.71 years in the six to 6.99 age group and an average overestimation of 0.5 years across all age groups. In females, the overestimation was 0.82 years in the 11-11.99 age group, but the average overestimation was 0.5 years. Overall, the Nolla method underestimated the age of males by 0.28 years and females by 0.25 years. Estimations from both methods were 0.7 years apart on average. In conclusion, while the Demirjian and Nolla methods have unique advantages, using them together can provide a more robust and reliable age estimation. Forensic practitioners can determine the best approach by considering population-specific accuracy, age group and gender, and the case context. Combining both techniques offers cross-verification, comprehensive assessment, bias mitigation, and enhanced reliability.