Sexual Dimorphism in Canine Teeth for Forensic Sex Determination: A Population-Specific Study from Northern Ghana

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Despite its importance in medicolegal investigations, comprehensive data on dental sexual dimorphism remain limited in West African populations. Objective : This study evaluated the forensic utility of maxillary and mandibular canine dimensions for sex determination in the Northern Ghanaian population and assessed the practical limitations of this approach. Methods : A cross-sectional study examined 212 participants aged 18-25 years [93 males (43.9%), 119 females (56.1%)] from the Tamale region of Northern Ghana (May-September 2022). Mesiodistal canine widths and inter-canine distances were measured using standardized anthropometric protocols with an inter-rater reliability assessment. Statistical analysis employed descriptive statistics, binary logistic regression, and diagnostic accuracy measures, including ROC curve analysis, with significance set at p<0.05. Results : Mandibular canines demonstrated modest sexual dimorphism, with males showing significantly larger dimensions: right mandibular canine width (7.26±0.67mm vs. 6.88±0.51mm, p<0.001), left mandibular canine width (7.15±0.60mm vs. 6.87±0.39mm, p<0.001), and inter-canine distance (30.88±2.74mm vs. 29.32±1.98mm, p<0.0001). Traditional forensic canine indices showed an accuracy equivalent to chance (49.5-51.9%, 95% CI: 43.2-58.6%). Binary logistic regression achieved marginally improved performance, with the left mandibular canine index reaching a maximum accuracy of 58.0% (95% CI: 51.1-64.7%, AUC=0.618). Conclusions : Despite statistically significant sexual dimorphism in Northern Ghanaian canines, forensic utility is severely limited by accuracy rates barely exceeding chance (58% vs. 50%). The 8% improvement over random classification falls substantially below the 80-90% threshold required for reliable forensic applications. Canine-based sex determination lacks practical forensic value as a standalone method in this population forensic odontology sexual dimorphism canine teeth sex determination forensic anthropology Ghana diagnostic accuracy Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Forensic identification faces significant challenges when skeletal remains are incomplete, compromised, or absent, creating critical gaps in establishing the victim’s identity (Christensen et al., 2019 ). Sexual dimorphism in dental structures has been proposed as a potential supplementary approach, particularly when traditional skeletal markers are unavailable. However, extensive research has revealed substantial limitations in their accuracy and reliability across different populations (Viciano et al., 2013 ; Acharya & Mainali, 2007 ). While DNA analysis remains the gold standard, with accuracy rates exceeding 99% (Butler, 2012 ), it requires specialized facilities and may be impossible when DNA is degraded. This has led to the investigation of alternative methods, including dental morphometric analysis (Hillson et al., 2005 ). 1.1 Canine Sexual Dimorphism and Its Limitations Canines have attracted forensic attention because of their preservation properties and reported sexual dimorphism (Ateş et al., 2006 ). Permanent tooth crowns develop early and remain stable, but dimorphism varies considerably across populations, with reported accuracy rates ranging from 53–87.5% (Zorba et al., 2011 ; Kaushal et al., 2003 ; Galdames et al., 2008 ). The biological basis involves Y-chromosome effects and testosterone exposure during the 6th-8th gestational weeks when canine tooth buds form (Schwartz & Dean, 2005 ). However, environmental factors—including traditional dietary practices (high-fiber foods requiring increased masticatory forces), nutritional availability during critical developmental periods, and cultural practices—substantially influence dimorphism expression, limiting generalizability across regions (Hillson et al., 2005 ; Townsend et al., 2009 ). In West African populations, genetic diversity reflects complex migration patterns and admixture events spanning millennia, which may affect baseline dimorphism patterns and further modulate the expression of sexual dimorphism. The Northern Ghanaian population presents unique characteristics, including diverse ethnic groups (predominantly Dagomba, Mamprusi, and Gonja), subsistence agriculture patterns, and distinct nutritional profiles that may influence dental development differently than populations where existing forensic standards were developed. These factors underscore the need for population-specific validation of forensic methods. 1.2 Study Objectives and Hypotheses This study addresses critical knowledge gaps regarding canine sexual dimorphism in West African populations. We hypothesized that: (1) canine dimensions would show statistically significant but modest sexual dimorphism insufficient for reliable forensic application; (2) mandibular canines would demonstrate greater dimorphism than maxillary canines; (3) binary logistic regression would provide only marginal improvements over traditional indices; and (4) overall classification accuracy would remain below acceptable forensic thresholds.​​​​​​​​​​​​​​​​ 2. MATERIALS AND METHODS 2.1 Study Design and Ethical Considerations This cross-sectional observational study was conducted at the Tamale Campus of the University for Development Studies in Ghana’s Northern Region, between May and September 2022. Ethical approval was obtained from the Institutional Review Board. Written informed consent was obtained from all participants after explaining the study objectives, potential risks, and the voluntary nature of participation in their preferred language, following the Declaration of Helsinki guidelines (World Medical Association, 2013). 2.2 Study Population and Sampling Sample Size Calculation: Based on previous studies reporting effect sizes of 0.3-0.5 for canine sexual dimorphism (Cohen, 1988; Khamis et al., 2014; Viciano et al., 2013), a minimum sample size of 176 participants was calculated (α=0.05, β=0.80) to detect clinically meaningful differences. To account for potential dropouts and measurement errors, we recruited 230 participants, of whom 212 completed all measurements. Inclusion Criteria : Participants aged 18-25 years who provided signed informed consent, were in good general health, and had all four canine teeth present without restoration (Ateş et al., 2006). Exclusion criteria: Participants with improper tooth alignment, missing anterior teeth, crowded or excessively spaced anterior teeth, abnormal overjet/overbite (>3 mm), active tooth decay, poor oral hygiene, ongoing orthodontic treatment, canine teeth showing pathological wear, history of canine tooth trauma, or dental restorations affecting canine morphology (Acharya & Mainali, 2007; Zorba et al., 2011). 2.3 Odontometric Measurements All measurements were performed intraorally in a clean, well-illuminated clinical setting, following strict aseptic precautions. A calibrated digital caliper (Mitutoyo Corporation, Japan; accuracy ±0.01 mm) was used for all measurements, following established protocols (Lund & Mörnstad, 1999). Each parameter was measured twice by the primary investigator, thewith measurements were averaged to reduce random error (Hillson et al., 2005). 2.3.1. Measured parameters: Canine mesiodistal width : Greatest mesiodistal distance between contact points for all four canines (Lund & Mörnstad, 1999) Inter-canine distance : Distance between cusp tips of right and left canines in both arches (Zorba et al., 2011) Canine Index : Calculated as (Width of canine/Inter-canine distance) × 100 (Rao et al., 1989) Sexual dimorphism: Calculated as [(Xm-Xf)/Xf] × 100, where Xm = mean male measurement and Xf = mean female measurement (Garn et al., 1967) 2.3.2 Inter-rater Reliability : A subset of 30 participants was independently measured by two trained examiners to assess inter-rater reliability using intraclass correlation coefficients (ICC), following established protocols (Shrout & Fleiss, 1979) 2.4 Statistical Analysis Data were analyzed using SPSS version 28.0 and R version 4.3.0. Normality of distributions was assessed using Shapiro-Wilk tests and visual inspection of Q-Q plots. Potential sex differences were evaluated using independent samples t-tests for normally distributed data or Mann-Whitney U tests for non-parametric data as appropriate. To control for Type I error inflation due to multiple comparisons, Bonferroni correction was applied consistently across all statistical tests. Effect sizes were calculated using Cohen’s d to assess the magnitude of sex differences beyond statistical significance, providing insight into the practical importance of observed differences. Diagnostic Accuracy Assessment: Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) Receiver Operating Characteristic (ROC) curve analysis with area under curve (AUC) 95% confidence intervals for all accuracy measures Binary logistic regression models were developed with sex as the dependent variable and canine measurements as predictors. Model performance was evaluated using the Hosmer-Lemeshow goodness-of-fit test ( Hosmer et al., 2013) 3. RESULTS 3.1 Inter-rater Reliability and Descriptive Statistics Inter-rater reliability demonstrated excellent agreement: maxillary measurements (ICC = 0.91–0.94), mandibular measurements (ICC = 0.89–0.93). The final sample consisted of 212 participants [93 males (43.9%), 119 females (56.1%)] with mean age 21.4 ± 2.1 years. 3.2 Sexual Dimorphism Analysis and Statistical Significance Table 1 demonstrates statistically significant differences in several parameters, with sexual dimorphism values ranging from 3.1–15.0%. Males consistently showed larger dimensions, with mandibular measurements demonstrating greater dimorphism than maxillary measurements. Effect sizes (Cohen’s d) ranged from moderate to large for significant differences. Table 1 Comprehensive Sexual Dimorphism Analysis Parameters Male Mean + SD Female Mean + SD Mean Difference Sexual Dimorphism Effect Size (Cohen’s d) p-value MxICD 38.95 ± 2.52 37.24 ± 1.85 1.71 4.6 0.78 < 0.0001 * RMxCW 7.85 ± 0.60 7.60 ± 0.52 0.25 3.3 0.44 0.0010 * LMxCW 7.90 ± 0.62 7.66 ± 0.52 0.24 3.1 0.41 0.0070 * RMxCI 0.22 ± 0.01 0.20 ± 0.10 0.02 10.0 0.27 0.745 LMxCI 0.21 ± 0.00 0.20 ± 0.02 0.01 5.0 0.67 < 0.0001 * MnICD 30.88 ± 2.74 29.32 ± 1.90 1.56 5.3 0.66 < 0.0001 * RMnCW 7.26 ± 0.60 6.88 ± 0.51 0.38 5.5 0.64 < 0.0001 * LMnCW 7.15 ± 0.60 6.87 ± 0.30 0.28 4.1 0.56 < 0.0001 * RMnCI 0.26 ± 0.00 0.24 ± 0.02 0.02 4.4 1.00 <0.0001 * LMnCI 0.24 ± 0.02 0.21 ± 0.03 0.03 15.0 1.73 < 0.0001 * MnICD = Mandibular Canine Index RMnCW = Right Mandibular Canine Width LMnCW = Left Mandibular Canine Width. RMnCI = Right Mandibular Canine Index LMnCI = Left Mandibular Canine Index. MxICD = Maxillary Inter-Canine Distance RMxCW = Right Maxillary Canine Width LMxCW = Left Maxillary Canine Width RMxCI = Right Maxillary Canine Index LMxCI = Left Maxillary Canine Index Statistical comparison of canine measurements between males and females, including means, standard deviations, sexual dimorphism percentages, effect sizes, and p-values. Asterisks indicate statistical significance after Bonferroni correction. 3.3. Distribution Overlap Analysis Despite the statistical significance, an extensive overlap existed between the male and female distributions for all parameters ( Fig. 1 ) . The violin plots demonstrate why statistically significant differences fail to translate into forensic utility: the substantial distributional overlap prevents reliable individual classification. 3.4. Diagnostic Accuracy Analysis All methods achieved accuracies barely exceeding chance (Fig. 2 ). The left mandibular canine index achieved the highest accuracy (58.0%) with binary logistic regression, representing only an 8% improvement over chance. Traditional forensic indices performed at chance levels (49.5–51.9%), whereas logistic regression models showed marginal improvements (56.0–58.0%). All AUC values (0.587–0.618) indicated poor discriminatory ability, falling well below the 80% threshold required for forensic applications. Table 2 provides a comprehensive performance comparison for each method Table 2 Comprehensive Diagnostic Performance Comparison Traditional Method Logistic Regression Improvement (%) Parameter Accuracy 95% CI AUC Accuracy 95% CI AUC RMxCI 24.85 42.6–56.4 0.48 26.40 49.2–62.8 0.59 + 6.6 LMxCI 5.10 43.1–56.9 0.47 5.55 50.7–64.1 0.60 + 7.5 RMnCI 0.15 44.5–58.3 0.54 0.16 49.2–62.8 0.58 + 4.7 LMnCI 0.18 45.0-58.8 0.50 0.16 51.1–64.7 0.62 + 6.1 RMCI = right maxillary canine index LMxCI = left maxillary canine index RMnCI = right mandibular canine index. LMnCI = left mandibular canine index Comparison of diagnostic accuracy between traditional canine indices and binary logistic regression models, showing accuracy percentages, 95% confidence intervals, AUC values, and percentage improvements. Figure 2 : Grouped bar chart comparing the classification accuracies of traditional canine indices (gray bars) versus binary logistic regression models (blue bars). Reference lines at 50% (chance, gray dashed) and 80% (forensic threshold, red solid) are indicated. All bars fall between 49.5% and 58.0%, with error bars showing 95% confidence intervals. None of the methods approaches the 80% threshold. As detailed in Table 2 , none of the methods reaches acceptable accuracy. For example, the best-performing logistic model (left mandibular CI) achieves only 58.0% accuracy (AUC = 0.618), reinforcing that all methods fall well below the forensic threshold. 3.6. ROC Curve Analysis Figure 3 presents the ROC curves demonstrating poor discriminatory ability across all methods, with AUC values ranging from 0.587 to 0.618. All curves lie close to the diagonal reference line (chance performance), confirming the insufficient discriminatory power for forensic applications. 3.7. Predictive Value Analysis The predictive values in Table 3 demonstrate consistently poor performance across all parameters. The positive predictive values ranged from 46.8–48.7%, indicating that fewer than half of the individuals classified as male were actually male across all methods. The negative predictive values (65.4–67.4%) showed modest improvement for female classification but remained insufficient for forensic applications, where misclassification carries serious consequences. Table 3 Detailed Diagnostic Performance Metrics (Binary Logistic Models) Parameters Sensitivity (%) Specificity (%) PPV (%) NPV (%) RMxCI 58.1 54.6 0.02 10.0 LMxCI 59.1 56.3 0.01 5.0 RMnCI 58.1 54.6 0.02 4.4 LMnCI 60.2 56.3 0.03 15.0 NPV: Negative Predictive Value PPV: Positive Predictive Value Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each parameter using binary logistic regression models. 3.8 Comprehensive Forensic Utility Assessment Figure 4 shows a comprehensive forensic utility assessment dashboard displaying multiple performance metrics simultaneously. Panel A shows the accuracy rates with a clear demarcation of the forensic threshold (80%), demonstrating that all methods fall substantially below acceptable standards. Panel B shows the positive and negative predictive values, revealing the clinical consequences of poor accuracy. Panel C presents a sensitivity versus specificity scatter plot with forensic adequacy zones clearly marked, showing that all methods cluster in the inadequate performance region. Panel D correlates effect sizes with accuracy, demonstrating that even large effect sizes (Cohen’s d=1.73 for LMnCI) fail to achieve forensic utility, highlighting the disconnect between statistical significance and practical application. See Figure 4 for the full forensic utility assessment dashboard. 3.9 Statistical Significance vs. Clinical Utility Although most canine measurements showed statistically significant sexual dimorphism (p < 0.001), their clinical utility remains severely limited. The highest-performing model achieved only 58.0% accuracy with an AUC of 0.618, representing poor discriminatory ability with minimal practical forensic value. The extensive overlap in the distributions between the sexes, as shown in Fig. 1 , explains why statistical significance fails to translate into forensic utility. 4. DISCUSSION 4.1 Key Findings and Forensic Implications This study provides the first systematic evaluation of canine sexual dimorphism for forensic applications in Northern Ghana, revealing a critical disconnect between the statistical significance and forensic utility. Although statistically significant differences exist between male and female canine dimensions, with sexual dimorphism values ranging from 3.1–14.3%, practical forensic applications face substantial limitations, with accuracy rates of only 49.5–58.0%. The maximum accuracy of 58.0% (left mandibular canine index, Cohen’s d = 1.73) represents merely an 8% improvement over random assignment, falling well below the 80–90% threshold required for reliable forensic applications. The positive predictive values of 46.8–48.7% indicate that fewer than half of the individuals classified as male would actually be male, presenting unacceptable error rates for forensic contexts. 4.2 Population-Specific Factors Affecting Dimorphism The modest dimorphism observed in the Northern Ghanaian populations likely reflects several interconnected factors. The genetic diversity in West African populations is among the highest globally, potentially diluting the sex-specific developmental signals that drive canine dimorphism. The complex genetic admixture patterns in Ghana, involving Gur-speaking groups, Mande influences, and historical population movements, may contribute to reduced sexual dimorphism compared to more genetically homogeneous populations. Environmental and nutritional factors during critical developmental periods (6th-8th gestational weeks for canine bud formation) may also influence dimorphism expression. Traditional Ghanaian diets, characterized by millet, sorghum, and yam staples with seasonal nutritional variation, differ substantially from those of European or Asian populations, where higher dimorphism has been reported. Chronic nutritional stress during development may attenuate the effects of sex hormones on dental development. Cultural practices, including traditional feeding patterns, weaning practices, and childhood nutrition, may further modulate sexual dimorphism. The practice of prolonged breastfeeding (often 18–24 months) followed by the gradual introduction of traditional foods may create different developmental environments than those in populations with earlier weaning and different nutritional profiles. Masticatory functional adaptations may also play a role in this regard. Traditional Ghanaian diets require extensive processing of fibrous plant materials, potentially leading to increased masticatory forces that could influence canine development. The functional demands of processing millet, sorghum, and other traditional foods may override subtle sex-based developmental differences, contributing to the reduced dimorphism. 4.3 Literature Comparison and Population Variation Our accuracy rates (49.5–58.0%) fall within the lower range of global reports, contrasting with higher accuracies reported in European populations (Zorba et al., 2011 : 87.5%), Indian populations (Kaushal et al., 2003 : 84.2%), and Brazilian samples (Galdames et al., 2008 : 76.3%). This substantial variation highlights the critical importance of population-specific validation and suggests limitations in the application of standards developed in other geographic regions. The dimorphism values observed (3.1–14.3%) align with Lund and Mörnstad’s ( 1999 ) findings in Scandinavian populations but show reduced magnitude compared to some Asian studies, possibly reflecting environmental factors specific to West African populations, including genetic diversity and nutritional patterns during critical developmental periods. The wide international variation may also indicate publication bias favoring positive results in the existing literature. 4.4 Clinical Implications and Cost-Effectiveness Analysis The 58% accuracy rate renders canine-based sex determination forensically unacceptable, with a 42% misidentification risk that could potentially compromise criminal investigations and legal proceedings in West Africa. Poor cost-benefit ratios make this method an inefficient resource allocation when DNA analysis and traditional anthropological methods offer substantially higher accuracies. Forensic laboratories should prioritize building capacity for established methods rather than implementing protocols with known limitations. 4.5 Forensic Practice Guidelines and Recommendations Forensic laboratories must establish a minimum 80% accuracy threshold for morphometric methods before implementation, with regular validation studies and transparent reporting of diagnostic performance metrics. Practitioners currently using canine-based methods should immediately reconsider their utility in the Northern Ghanaian population. If used, such methods should only serve as preliminary screening tools with explicit acknowledgment of their severe limitations and never as primary identification methods. Training programs should emphasize the distinction between statistical significance and forensic utility. 4.6 Study Limitations and Methodological Considerations Key limitations include age restriction (18–25 years) limiting forensic applicability, geographic restriction to university populations potentially limiting demographic representation, and cross-sectional design preventing assessment of age-related changes. However, performance in this optimal testing scenario suggests limited improvement potential in broader populations. Population Representativeness The university-based sample may not fully represent the broader Northern Ghanaian population, particularly regarding socioeconomic diversity and rural populations. Future studies should include community-based sampling to enhance generalizability and assess whether different environmental exposures might influence dimorphism patterns. 4.7. Future Directions and Research Priorities Three-dimensional morphological analysis using micro-CT technology and machine learning approaches merits exploration, although biological constraints may limit substantial accuracy improvements. Future research should expand the age range and geographic representation while maintaining forensic reliability standards. Advanced statistical methods, including ensemble learning and neural networks, could be explored, although fundamental biological limitations suggest modest improvement potential. Research priorities should focus on establishing comprehensive forensic databases for West African populations and developing population-specific standards for established methods, rather than pursuing methods with demonstrated limitations. 5. CONCLUSION Canine-based sex determination lacks forensic value in Northern Ghana, achieving only 58% accuracy, which is substantially below the 80–90% threshold required for reliable forensic applications. This study represents the first systematic evaluation of dental sexual dimorphism in West African populations and demonstrates the critical importance of rigorous population-specific validation before method implementation. The extensive overlap in canine dimensions between sexes, despite statistically significant dimorphism, renders this approach unsuitable for individual identification. The 42% misidentification risk presents unacceptable consequences for criminal investigations and for legal proceedings. Resources are better allocated toward DNA analysis capabilities or validated morphological methods, including pelvic and cranial assessments, which have demonstrated higher accuracy rates. This study provides valuable negative findings that strengthen the evidence base for informed forensic practice decisions and advance the field’s commitment to scientific rigor. The results emphasize that statistical significance does not guarantee forensic utility and highlight the need for a comprehensive diagnostic accuracy assessment during method validation. The population-specific factors identified, including genetic diversity, environmental influences, and cultural practices, underscore the complexity of applying forensic methods across different populations and the dangers of assuming the universal applicability of techniques developed in specific geographic or demographic contexts. Abbreviations AUC: Area Under Curve CI: Confidence Interval ICC: Intraclass Correlation Coefficient LMnCI: Left Mandibular Canine Index LMxCI: Left Maxillary Canine Index LMxCW: Left Maxillary Canine Width MnICD: Mandibular Inter-Canine Distance MxICD: Maxillary Inter-Canine Distance NPV: Negative Predictive Value PPV: Positive Predictive Value RMnCI: Right Mandibular Canine Index RMnCW: Right Mandibular Canine Width RMxCI: Right Maxillary Canine Index RMxCW: Right Maxillary Canine Width ROC: Receiver Operating Characteristic Declarations Ethics approval and consent to participate Ethical approval was obtained from the Institutional Review Board of the University for Development Studies, Ghana. Written informed consent was obtained from all participants in their preferred language following Declaration of Helsinki guidelines Consent for publication Consent for publication was obtained from all participants. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution S.E. Conceptualization, methodology, investigation, formal analysis, writing-original draft, project administration, supervision. E.K.F. : Methodology, validation, data collection, writing-review and editing. M.B.: Supervision, validation, statistical analysis, writing-review and editing. All authors contributed to the interpretation of results, critically reviewed the manuscript, and approved the final version for publication. The corresponding author has full access to all study data and takes complete responsibility for data integrity and analytical accuracy. Acknowledgement The authors express their gratitude to all participants who willingly participated in the study. 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Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin , 86(2), 420-428. Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279-282. Townsend, G., Hughes, T., Luciano, M., Bockmann, M., & Brook, A. (2009). Genetic and environmental influences on human dental variation: A critical evaluation of studies involving twins. Archives of Oral Biology, 54(Suppl 1), S45-S51. Viciano, J., López-Lázaro, S., & Alemán, I. (2013). Sex estimation based on deciduous and permanent dentition in a contemporary Spanish population. American Journal of Physical Anthropology , 152(1), 31-43. Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209-212. World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA , 310(20), 2191-2194. Zorba, E., Moraitis, K., & Manolis, S. K. (2011). Sexual dimorphism in permanent teeth of modern Greeks. Forensic Science International , 210(1-3), 74-81. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7025187","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488623672,"identity":"63760074-bb72-46d5-b842-cd66d4eb14f8","order_by":0,"name":"Sarah Eshun¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYJACZiBOYGDg//jgA5DFxk68FgZjwxkgLcwkaDGT5oFx8QH59rMPPxdU1OUZ3G5INrb5tU2ej5mB8cPHHNxaDM6kG0vPOHO42ODOgYOPc/tuG7YxMzBLztyGRwtDGoM0b9uBxA03EpuNc3tuMwK1sDHz4tEi3/+M+TfvvzqglmQ2acue2/YEtTDcSGOT5m1gBmoBMhh+3E4kqMXgxjM2a55jhxNn3shhNuxtuJ3cxszYjNcv8v1pzLd5auoS+27kMD748ee27fz25oMfPuJzGApgbAOTDcSqB4E/pCgeBaNgFIyCkQIAGJlRrVEz7kQAAAAASUVORK5CYII=","orcid":"","institution":"University for Development Studies","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Eshun¹","suffix":""},{"id":488623673,"identity":"6ee4ad7b-589f-4a91-80d0-459add3fb4c7","order_by":1,"name":"Moses Banyeh¹","email":"","orcid":"","institution":"University for Development Studies","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"","lastName":"Banyeh¹","suffix":""},{"id":488623674,"identity":"65047a20-c0d3-4fff-99f1-6abb51627c25","order_by":2,"name":"Emmanuel Kwame Frimpong¹","email":"","orcid":"","institution":"University for Development Studies","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Kwame","lastName":"Frimpong¹","suffix":""}],"badges":[],"createdAt":"2025-07-02 05:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7025187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7025187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87345213,"identity":"dbc901f6-f0bd-4ba1-b0b9-6297f5964e76","added_by":"auto","created_at":"2025-07-23 02:08:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":207454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDistribution Overlap Between Sexes\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour-panel violin plot showing male (blue) vs. female (orange) distributions for LMnCI, RMnCI, MnICD, and RMnCW. Vertical lines indicate means. Extensive overlap is visible across all parameters, with males showing slightly higher means but substantial distributional overlap.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7025187/v1/2aa5ef69510cc43a1fee5cd3.png"},{"id":87345212,"identity":"3f5b4c19-d015-46f6-945f-e5a4e3629fee","added_by":"auto","created_at":"2025-07-23 02:08:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDiagnostic Accuracy Comparison\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGrouped bar chart comparing traditional indices (gray bars) vs. logistic regression (blue bars) accuracies. Horizontal reference lines at 50% (chance, gray dashed) and 80% (forensic threshold, red solid). All bars fall between 49.5-58.0%, with error bars showing 95% confidence intervals. No method approaches the forensic threshold.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7025187/v1/55c4fa7bc1b2bf95a3aa1625.png"},{"id":87345221,"identity":"dc09f941-249e-461f-8590-86514414a98c","added_by":"auto","created_at":"2025-07-23 02:08:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eROC Curves for Best-Performing Parameters\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eROC curve plot showing four curves for LMnCI (AUC=0.618), LMxCI (AUC=0.604), RMxCI (AUC=0.591), and RMnCI (AUC=0.587). Diagonal reference line shows chance performance (AUC=0.5). All curves lie close to the diagonal, indicating poor discriminatory power.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7025187/v1/7230ffc18b34020e8ca68388.jpeg"},{"id":87345215,"identity":"be565d63-4de8-498a-bc3e-39960c5afb94","added_by":"auto","created_at":"2025-07-23 02:08:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":372768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eForensic Utility Assessment Dashboard\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour-panel dashboard: Panel A shows accuracy rates with forensic threshold line (80%); Panel B displays positive and negative predictive values; Panel C presents sensitivity vs. specificity scatter plot with forensic adequacy zones; Panel D correlates effect sizes with accuracy, showing that even large effect sizes fail to achieve forensic utility.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7025187/v1/e15cd5e182edb699f108092a.jpeg"},{"id":90400441,"identity":"f58feeca-3fba-4da9-a10c-557262151167","added_by":"auto","created_at":"2025-09-02 10:08:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2549973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7025187/v1/e9ab7622-1979-4fd1-bbae-1f7326330fe1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sexual Dimorphism in Canine Teeth for Forensic Sex Determination: A Population-Specific Study from Northern Ghana","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eForensic identification faces significant challenges when skeletal remains are incomplete, compromised, or absent, creating critical gaps in establishing the victim\u0026rsquo;s identity (Christensen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sexual dimorphism in dental structures has been proposed as a potential supplementary approach, particularly when traditional skeletal markers are unavailable. However, extensive research has revealed substantial limitations in their accuracy and reliability across different populations (Viciano et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Acharya \u0026amp; Mainali, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile DNA analysis remains the gold standard, with accuracy rates exceeding 99% (Butler, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), it requires specialized facilities and may be impossible when DNA is degraded. This has led to the investigation of alternative methods, including dental morphometric analysis (Hillson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Canine Sexual Dimorphism and Its Limitations\u003c/h2\u003e\u003cp\u003eCanines have attracted forensic attention because of their preservation properties and reported sexual dimorphism (Ateş et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Permanent tooth crowns develop early and remain stable, but dimorphism varies considerably across populations, with reported accuracy rates ranging from 53\u0026ndash;87.5% (Zorba et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kaushal et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Galdames et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe biological basis involves Y-chromosome effects and testosterone exposure during the 6th-8th gestational weeks when canine tooth buds form (Schwartz \u0026amp; Dean, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, environmental factors\u0026mdash;including traditional dietary practices (high-fiber foods requiring increased masticatory forces), nutritional availability during critical developmental periods, and cultural practices\u0026mdash;substantially influence dimorphism expression, limiting generalizability across regions (Hillson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Townsend et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In West African populations, genetic diversity reflects complex migration patterns and admixture events spanning millennia, which may affect baseline dimorphism patterns and further modulate the expression of sexual dimorphism.\u003c/p\u003e\u003cp\u003eThe Northern Ghanaian population presents unique characteristics, including diverse ethnic groups (predominantly Dagomba, Mamprusi, and Gonja), subsistence agriculture patterns, and distinct nutritional profiles that may influence dental development differently than populations where existing forensic standards were developed. These factors underscore the need for population-specific validation of forensic methods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Study Objectives and Hypotheses\u003c/h2\u003e\u003cp\u003eThis study addresses critical knowledge gaps regarding canine sexual dimorphism in West African populations. We hypothesized that: (1) canine dimensions would show statistically significant but modest sexual dimorphism insufficient for reliable forensic application; (2) mandibular canines would demonstrate greater dimorphism than maxillary canines; (3) binary logistic regression would provide only marginal improvements over traditional indices; and (4) overall classification accuracy would remain below acceptable forensic thresholds.​​​​​​​​​​​​​​​​\u003c/p\u003e\u003c/div\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1 Study Design and Ethical Considerations\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis cross-sectional observational study was conducted at the Tamale Campus of the University for Development Studies in Ghana\u0026rsquo;s Northern Region, between May and September 2022. Ethical approval was obtained from the Institutional Review Board. Written informed consent was obtained from all participants after explaining the study objectives, potential risks, and the voluntary nature of participation in their preferred language, following the Declaration of Helsinki guidelines (World Medical Association, 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 \u0026nbsp;Study Population and Sampling\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSample Size Calculation: Based on previous studies reporting effect sizes of 0.3-0.5 for canine sexual dimorphism (Cohen, 1988; Khamis et al., 2014; Viciano et al., 2013), a minimum sample size of 176 participants was calculated (\u0026alpha;=0.05, \u0026beta;=0.80) to detect clinically meaningful differences. To account for potential dropouts and measurement errors, we recruited 230 participants, of whom 212 completed all measurements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInclusion Criteria\u003c/em\u003e\u003c/strong\u003e: Participants aged 18-25 years who provided signed informed consent, were in good general health, and had all four canine teeth present without restoration (Ateş et al., 2006).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExclusion criteria:\u003c/em\u003e\u003c/strong\u003eParticipants with improper tooth alignment, missing anterior teeth, crowded or excessively spaced anterior teeth, abnormal overjet/overbite (\u0026gt;3 mm), active tooth decay, poor oral hygiene, ongoing orthodontic treatment, canine teeth showing pathological wear, history of canine tooth trauma, or dental restorations affecting canine morphology (Acharya \u0026amp; Mainali, 2007; Zorba et al., 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3 \u0026nbsp;Odontometric Measurements\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll measurements were performed intraorally in a clean, well-illuminated clinical setting, following strict aseptic precautions. A calibrated digital caliper (Mitutoyo Corporation, Japan; accuracy \u0026plusmn;0.01 mm) was used for all measurements, following established protocols (Lund \u0026amp; M\u0026ouml;rnstad, 1999). Each parameter was measured twice by the primary investigator, thewith measurements were averaged to reduce random error (Hillson et al., 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.1. Measured parameters:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eCanine mesiodistal width\u003c/em\u003e: Greatest mesiodistal distance between contact points for all four canines \u003cem\u003e(Lund \u0026amp; M\u0026ouml;rnstad, 1999)\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eInter-canine distance\u003c/em\u003e: Distance between cusp tips of right and left canines in both arches \u003cem\u003e(Zorba et al., 2011)\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eCanine Index\u003c/em\u003e: Calculated as (Width of canine/Inter-canine distance) \u0026times; 100 \u003cem\u003e(Rao et al., 1989)\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eSexual dimorphism:\u003c/em\u003e Calculated as [(Xm-Xf)/Xf] \u0026times; 100, where Xm = mean male measurement and Xf = mean female measurement \u003cem\u003e(Garn et al., 1967)\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e2.3.2 Inter-rater Reliability\u003c/em\u003e: A subset of 30 participants was independently measured by two trained examiners to assess inter-rater reliability using intraclass correlation coefficients (ICC), following established protocols (Shrout \u0026amp; Fleiss, 1979)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4 Statistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS version 28.0 and R version 4.3.0. Normality of distributions was assessed using Shapiro-Wilk tests and visual inspection of Q-Q plots. Potential sex differences were evaluated using independent samples t-tests for normally distributed data or Mann-Whitney U tests for non-parametric data as appropriate.\u003c/p\u003e\n\u003cp\u003eTo control for Type I error inflation due to multiple comparisons, Bonferroni correction was applied consistently across all statistical tests. Effect sizes were calculated using Cohen\u0026rsquo;s d to assess the magnitude of sex differences beyond statistical significance, providing insight into the practical importance of observed differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiagnostic Accuracy Assessment:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)\u003c/li\u003e\n \u003cli\u003eReceiver Operating Characteristic (ROC) curve analysis with area under curve (AUC)\u003c/li\u003e\n \u003cli\u003e95% confidence intervals for all accuracy measures\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBinary logistic regression models were developed with sex as the dependent variable and canine measurements as predictors. Model performance was evaluated using the Hosmer-Lemeshow goodness-of-fit test (\u003cem\u003eHosmer et al., 2013)\u003c/em\u003e\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Inter-rater Reliability and Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eInter-rater reliability demonstrated excellent agreement: maxillary measurements (ICC\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.94), mandibular measurements (ICC\u0026thinsp;=\u0026thinsp;0.89\u0026ndash;0.93). The final sample consisted of 212 participants [93 males (43.9%), 119 females (56.1%)] with mean age 21.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 years.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Sexual Dimorphism Analysis and Statistical Significance\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003edemonstrates statistically significant differences in several parameters, with sexual dimorphism values ranging from 3.1\u0026ndash;15.0%. Males consistently showed larger dimensions, with mandibular measurements demonstrating greater dimorphism than maxillary measurements. Effect sizes (Cohen\u0026rsquo;s d) ranged from moderate to large for significant differences.\u003c/p\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive Sexual Dimorphism Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e+\u0026thinsp;SD\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e+\u0026thinsp;SD\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSexual Dimorphism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect Size (Cohen\u0026rsquo;s d)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMxICD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMxCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMxCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0070\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMxCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMxCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMnICD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMnCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMnCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMnCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMnCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eMnICD\u0026thinsp;=\u0026thinsp;Mandibular Canine Index RMnCW\u0026thinsp;=\u0026thinsp;Right Mandibular Canine Width LMnCW\u0026thinsp;=\u0026thinsp;Left Mandibular Canine Width. RMnCI\u0026thinsp;=\u0026thinsp;Right Mandibular Canine Index LMnCI\u0026thinsp;=\u0026thinsp;Left Mandibular Canine Index. MxICD\u0026thinsp;=\u0026thinsp;Maxillary Inter-Canine Distance RMxCW\u0026thinsp;=\u0026thinsp;Right Maxillary Canine Width LMxCW\u0026thinsp;=\u0026thinsp;Left Maxillary Canine Width RMxCI\u0026thinsp;=\u0026thinsp;Right Maxillary Canine Index LMxCI\u0026thinsp;=\u0026thinsp;Left Maxillary Canine Index\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical comparison of canine measurements between males and females, including means, standard deviations, sexual dimorphism percentages, effect sizes, and p-values. Asterisks indicate statistical significance after Bonferroni correction.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Distribution Overlap Analysis\u003c/h2\u003e\n \u003cp\u003eDespite the statistical significance, an extensive overlap existed between the male and female distributions for all parameters \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. The violin plots demonstrate why statistically significant differences fail to translate into forensic utility: the substantial distributional overlap prevents reliable individual classification.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Diagnostic Accuracy Analysis\u003c/h2\u003e\n \u003cp\u003eAll methods achieved accuracies barely exceeding chance (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The left mandibular canine index achieved the highest accuracy (58.0%) with binary logistic regression, representing only an 8% improvement over chance. Traditional forensic indices performed at chance levels (49.5\u0026ndash;51.9%), whereas logistic regression models showed marginal improvements (56.0\u0026ndash;58.0%). All AUC values (0.587\u0026ndash;0.618) indicated poor discriminatory ability, falling well below the 80% threshold required for forensic applications. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides a comprehensive performance comparison for each method\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive Diagnostic Performance Comparison\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTraditional Method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eImprovement (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMxCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.6\u0026ndash;56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.2\u0026ndash;62.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e+\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMxCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.1\u0026ndash;56.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.7\u0026ndash;64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e+\u0026thinsp;7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMnCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.5\u0026ndash;58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.2\u0026ndash;62.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e+\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMnCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.0-58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.1\u0026ndash;64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e+\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eRMCI\u0026thinsp;=\u0026thinsp;right maxillary canine index LMxCI\u0026thinsp;=\u0026thinsp;left maxillary canine index RMnCI\u0026thinsp;=\u0026thinsp;right mandibular canine index. LMnCI\u0026thinsp;=\u0026thinsp;left mandibular canine index\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComparison of diagnostic accuracy between traditional canine indices and binary logistic regression models, showing accuracy percentages, 95% confidence intervals, AUC values, and percentage improvements.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e: Grouped bar chart comparing the classification accuracies of traditional canine indices (gray bars) versus binary logistic regression models (blue bars). Reference lines at 50% (chance, gray dashed) and 80% (forensic threshold, red solid) are indicated. All bars fall between 49.5% and 58.0%, with error bars showing 95% confidence intervals. None of the methods approaches the 80% threshold.\u003c/p\u003e\n \u003cp\u003eAs detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, none of the methods reaches acceptable accuracy. For example, the best-performing logistic model (left mandibular CI) achieves only 58.0% accuracy (AUC\u0026thinsp;=\u0026thinsp;0.618), reinforcing that all methods fall well below the forensic threshold.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. ROC Curve Analysis\u003c/h2\u003e\n \u003cp\u003eFigure 3 presents the ROC curves demonstrating poor discriminatory ability across all methods, with AUC values ranging from 0.587 to 0.618. All curves lie close to the diagonal reference line (chance performance), confirming the insufficient discriminatory power for forensic applications.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Predictive Value Analysis\u003c/h2\u003e\n \u003cp\u003eThe predictive values in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrate consistently poor performance across all parameters. The positive predictive values ranged from 46.8\u0026ndash;48.7%, indicating that fewer than half of the individuals classified as male were actually male across all methods. The negative predictive values (65.4\u0026ndash;67.4%) showed modest improvement for female classification but remained insufficient for forensic applications, where misclassification carries serious consequences.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed Diagnostic Performance Metrics (Binary Logistic Models)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMxCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMxCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMnCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMnCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003eNPV: Negative Predictive Value PPV: Positive Predictive Value\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each parameter using binary logistic regression models.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Comprehensive Forensic Utility Assessment\u003c/h2\u003e\n \u003cp\u003eFigure 4 shows a comprehensive forensic utility assessment dashboard displaying multiple performance metrics simultaneously. Panel A shows the accuracy rates with a clear demarcation of the forensic threshold (80%), demonstrating that all methods fall substantially below acceptable standards. Panel B shows the positive and negative predictive values, revealing the clinical consequences of poor accuracy. Panel C presents a sensitivity versus specificity scatter plot with forensic adequacy zones clearly marked, showing that all methods cluster in the inadequate performance region. Panel D correlates effect sizes with accuracy, demonstrating that even large effect sizes (Cohen\u0026rsquo;s d=1.73 for LMnCI) fail to achieve forensic utility, highlighting the disconnect between statistical significance and practical application. See \u003cem\u003eFigure 4\u0026nbsp;\u003c/em\u003efor the full forensic utility assessment dashboard.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9 Statistical Significance vs. Clinical Utility\u003c/h2\u003e\n \u003cp\u003eAlthough most canine measurements showed statistically significant sexual dimorphism (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), their clinical utility remains severely limited. The highest-performing model achieved only 58.0% accuracy with an AUC of 0.618, representing poor discriminatory ability with minimal practical forensic value. The extensive overlap in the distributions between the sexes, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, explains why statistical significance fails to translate into forensic utility.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Key Findings and Forensic Implications\u003c/h2\u003e\u003cp\u003eThis study provides the first systematic evaluation of canine sexual dimorphism for forensic applications in Northern Ghana, revealing a critical disconnect between the statistical significance and forensic utility. Although statistically significant differences exist between male and female canine dimensions, with sexual dimorphism values ranging from 3.1\u0026ndash;14.3%, practical forensic applications face substantial limitations, with accuracy rates of only 49.5\u0026ndash;58.0%.\u003c/p\u003e\u003cp\u003eThe maximum accuracy of 58.0% (left mandibular canine index, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.73) represents merely an 8% improvement over random assignment, falling well below the 80\u0026ndash;90% threshold required for reliable forensic applications. The positive predictive values of 46.8\u0026ndash;48.7% indicate that fewer than half of the individuals classified as male would actually be male, presenting unacceptable error rates for forensic contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Population-Specific Factors Affecting Dimorphism\u003c/h2\u003e\u003cp\u003eThe modest dimorphism observed in the Northern Ghanaian populations likely reflects several interconnected factors. The genetic diversity in West African populations is among the highest globally, potentially diluting the sex-specific developmental signals that drive canine dimorphism. The complex genetic admixture patterns in Ghana, involving Gur-speaking groups, Mande influences, and historical population movements, may contribute to reduced sexual dimorphism compared to more genetically homogeneous populations.\u003c/p\u003e\u003cp\u003eEnvironmental and nutritional factors during critical developmental periods (6th-8th gestational weeks for canine bud formation) may also influence dimorphism expression. Traditional Ghanaian diets, characterized by millet, sorghum, and yam staples with seasonal nutritional variation, differ substantially from those of European or Asian populations, where higher dimorphism has been reported. Chronic nutritional stress during development may attenuate the effects of sex hormones on dental development.\u003c/p\u003e\u003cp\u003eCultural practices, including traditional feeding patterns, weaning practices, and childhood nutrition, may further modulate sexual dimorphism. The practice of prolonged breastfeeding (often 18\u0026ndash;24 months) followed by the gradual introduction of traditional foods may create different developmental environments than those in populations with earlier weaning and different nutritional profiles.\u003c/p\u003e\u003cp\u003eMasticatory functional adaptations may also play a role in this regard. Traditional Ghanaian diets require extensive processing of fibrous plant materials, potentially leading to increased masticatory forces that could influence canine development. The functional demands of processing millet, sorghum, and other traditional foods may override subtle sex-based developmental differences, contributing to the reduced dimorphism.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Literature Comparison and Population Variation\u003c/h2\u003e\u003cp\u003eOur accuracy rates (49.5\u0026ndash;58.0%) fall within the lower range of global reports, contrasting with higher accuracies reported in European populations (Zorba et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e: 87.5%), Indian populations (Kaushal et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e: 84.2%), and Brazilian samples (Galdames et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e: 76.3%). This substantial variation highlights the critical importance of population-specific validation and suggests limitations in the application of standards developed in other geographic regions.\u003c/p\u003e\u003cp\u003eThe dimorphism values observed (3.1\u0026ndash;14.3%) align with Lund and M\u0026ouml;rnstad\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) findings in Scandinavian populations but show reduced magnitude compared to some Asian studies, possibly reflecting environmental factors specific to West African populations, including genetic diversity and nutritional patterns during critical developmental periods. The wide international variation may also indicate publication bias favoring positive results in the existing literature.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Clinical Implications and Cost-Effectiveness Analysis\u003c/h2\u003e\u003cp\u003eThe 58% accuracy rate renders canine-based sex determination forensically unacceptable, with a 42% misidentification risk that could potentially compromise criminal investigations and legal proceedings in West Africa. Poor cost-benefit ratios make this method an inefficient resource allocation when DNA analysis and traditional anthropological methods offer substantially higher accuracies. Forensic laboratories should prioritize building capacity for established methods rather than implementing protocols with known limitations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Forensic Practice Guidelines and Recommendations\u003c/h2\u003e\u003cp\u003eForensic laboratories must establish a minimum 80% accuracy threshold for morphometric methods before implementation, with regular validation studies and transparent reporting of diagnostic performance metrics. Practitioners currently using canine-based methods should immediately reconsider their utility in the Northern Ghanaian population. If used, such methods should only serve as preliminary screening tools with explicit acknowledgment of their severe limitations and never as primary identification methods. Training programs should emphasize the distinction between statistical significance and forensic utility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Study Limitations and Methodological Considerations\u003c/h2\u003e\u003cp\u003eKey limitations include age restriction (18\u0026ndash;25 years) limiting forensic applicability, geographic restriction to university populations potentially limiting demographic representation, and cross-sectional design preventing assessment of age-related changes. However, performance in this optimal testing scenario suggests limited improvement potential in broader populations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePopulation Representativeness\u003c/strong\u003e\u003cp\u003eThe university-based sample may not fully represent the broader Northern Ghanaian population, particularly regarding socioeconomic diversity and rural populations. Future studies should include community-based sampling to enhance generalizability and assess whether different environmental exposures might influence dimorphism patterns.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.7. Future Directions and Research Priorities\u003c/h2\u003e\u003cp\u003eThree-dimensional morphological analysis using micro-CT technology and machine learning approaches merits exploration, although biological constraints may limit substantial accuracy improvements. Future research should expand the age range and geographic representation while maintaining forensic reliability standards. Advanced statistical methods, including ensemble learning and neural networks, could be explored, although fundamental biological limitations suggest modest improvement potential. Research priorities should focus on establishing comprehensive forensic databases for West African populations and developing population-specific standards for established methods, rather than pursuing methods with demonstrated limitations.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eCanine-based sex determination lacks forensic value in Northern Ghana, achieving only 58% accuracy, which is substantially below the 80\u0026ndash;90% threshold required for reliable forensic applications. This study represents the first systematic evaluation of dental sexual dimorphism in West African populations and demonstrates the critical importance of rigorous population-specific validation before method implementation.\u003c/p\u003e\u003cp\u003eThe extensive overlap in canine dimensions between sexes, despite statistically significant dimorphism, renders this approach unsuitable for individual identification. The 42% misidentification risk presents unacceptable consequences for criminal investigations and for legal proceedings. Resources are better allocated toward DNA analysis capabilities or validated morphological methods, including pelvic and cranial assessments, which have demonstrated higher accuracy rates.\u003c/p\u003e\u003cp\u003eThis study provides valuable negative findings that strengthen the evidence base for informed forensic practice decisions and advance the field\u0026rsquo;s commitment to scientific rigor. The results emphasize that statistical significance does not guarantee forensic utility and highlight the need for a comprehensive diagnostic accuracy assessment during method validation.\u003c/p\u003e\u003cp\u003eThe population-specific factors identified, including genetic diversity, environmental influences, and cultural practices, underscore the complexity of applying forensic methods across different populations and the dangers of assuming the universal applicability of techniques developed in specific geographic or demographic contexts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eAUC: Area Under Curve\u003c/li\u003e\n \u003cli\u003eCI: Confidence Interval\u003c/li\u003e\n \u003cli\u003eICC: Intraclass Correlation Coefficient\u003c/li\u003e\n \u003cli\u003eLMnCI: Left Mandibular Canine Index\u003c/li\u003e\n \u003cli\u003eLMxCI: Left Maxillary Canine Index\u003c/li\u003e\n \u003cli\u003eLMxCW: Left Maxillary Canine Width\u003c/li\u003e\n \u003cli\u003eMnICD: Mandibular Inter-Canine Distance\u003c/li\u003e\n \u003cli\u003eMxICD: Maxillary Inter-Canine Distance\u003c/li\u003e\n \u003cli\u003eNPV: Negative Predictive Value\u003c/li\u003e\n \u003cli\u003ePPV: Positive Predictive Value\u003c/li\u003e\n \u003cli\u003eRMnCI: Right Mandibular Canine Index\u003c/li\u003e\n \u003cli\u003eRMnCW: Right Mandibular Canine Width\u003c/li\u003e\n \u003cli\u003eRMxCI: Right Maxillary Canine Index\u003c/li\u003e\n \u003cli\u003eRMxCW: Right Maxillary Canine Width\u003c/li\u003e\n \u003cli\u003eROC: Receiver Operating Characteristic\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Review Board of the University for Development Studies, Ghana. Written informed consent was obtained from all participants in their preferred language following Declaration of Helsinki guidelines\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eConsent for publication was obtained from all participants.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.E. Conceptualization, methodology, investigation, formal analysis, writing-original draft, project administration, supervision. E.K.F. : Methodology, validation, data collection, writing-review and editing. M.B.: Supervision, validation, statistical analysis, writing-review and editing. All authors contributed to the interpretation of results, critically reviewed the manuscript, and approved the final version for publication. The corresponding author has full access to all study data and takes complete responsibility for data integrity and analytical accuracy.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors express their gratitude to all participants who willingly participated in the study. Additionally, special appreciation is extended to the dedicated staff of the Department of Biomedical Laboratory Science at the University for Development Studies for their valuable support in conducting this research.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data supporting the results can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcharya, A. B., \u0026amp; Mainali, S. (2007). 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Genetic control of sexual dimorphism in tooth size. \u003cem\u003eJournal of Dental Research\u003c/em\u003e, 46(5), 963-972.\u003c/li\u003e\n \u003cli\u003eHillson, S. (2005). \u003cem\u003eTeeth\u003c/em\u003e (2nd ed.). Cambridge University Press.\u003c/li\u003e\n \u003cli\u003eHillson, S., FitzGerald, C., \u0026amp; Flinn, H. (2005). Alternative dental measurements: Proposals and relationships with other measurements. \u003cem\u003eAmerican Journal of Physical Anthropology\u003c/em\u003e, 126(4), 413-426.\u003c/li\u003e\n \u003cli\u003eHosmer, D. W., Lemeshow, S., \u0026amp; Sturdivant, R. X. (2013). \u003cem\u003eApplied logistic regression\u0026nbsp;\u003c/em\u003e(3rd ed.). Wiley.\u003c/li\u003e\n \u003cli\u003eHowells, W. W. (1973). \u003cem\u003eCranial variation in man: A study by multivariate analysis of patterns of difference among recent human populations\u003c/em\u003e. Harvard University Press.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eİşcan, M. Y., \u0026amp; Steyn, M. (2013). \u003cem\u003eThe human skeleton in forensic medicine\u003c/em\u003e (3rd ed.). Charles C Thomas Publisher.\u003c/li\u003e\n \u003cli\u003eJaeschke, R., Guyatt, G. H., \u0026amp; Sackett, D. L. (1994). Users\u0026rsquo; guides to the medical literature: III. How to use an article about a diagnostic test B. What are the results and will they help me in caring for my patients? \u003cem\u003eJAMA\u003c/em\u003e, 271(9), 703-707.\u003c/li\u003e\n \u003cli\u003eKaushal, S., Patnaik, V. V. G., \u0026amp; Agnihotri, G. (2003). Mandibular canines in sex determination. \u003cem\u003eJournal of Anatomical Society of India\u003c/em\u003e, 52(2), 119-124.\u003c/li\u003e\n \u003cli\u003eKhamis, M. F., Taylor, J. A., Malik, S. N., \u0026amp; Townsend, G. C. (2014). Odontometric sex variation in Malaysians with application to sex prediction. \u003cem\u003eForensic Science International\u003c/em\u003e, 234, 183.e1-183.e7.\u003c/li\u003e\n \u003cli\u003eKoo, T. K., \u0026amp; Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. \u003cem\u003eJournal of Chiropractic Medicine\u003c/em\u003e, 15(2), 155-163.\u003c/li\u003e\n \u003cli\u003eLund, H., \u0026amp; M\u0026ouml;rnstad, H. (1999). Gender determination by odontometrics in a Swedish population. \u003cem\u003eJournal of Forensic Odonto-Stomatology\u003c/em\u003e, 17(2), 30-34.\u003c/li\u003e\n \u003cli\u003eRao, N. G., Rao, N. N., Pai, M. L., \u0026amp; Kotian, M. S. (1989). Mandibular canine index\u0026mdash;a clue for establishing sex identity. Forensic Science International, 42(3), 249-254.\u003c/li\u003e\n \u003cli\u003eRosenthal, R. (1979). 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Genetic and environmental influences on human dental variation: A critical evaluation of studies involving twins. \u003cem\u003eArchives of Oral Biology,\u003c/em\u003e 54(Suppl 1), S45-S51.\u003c/li\u003e\n \u003cli\u003eViciano, J., L\u0026oacute;pez-L\u0026aacute;zaro, S., \u0026amp; Alem\u0026aacute;n, I. (2013). Sex estimation based on deciduous and permanent dentition in a contemporary Spanish population. \u003cem\u003eAmerican Journal of Physical Anthropology\u003c/em\u003e, 152(1), 31-43.\u003c/li\u003e\n \u003cli\u003eWilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. \u003cem\u003eJournal of the American Statistical Association,\u0026nbsp;\u003c/em\u003e22(158), 209-212.\u003c/li\u003e\n \u003cli\u003eWorld Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. \u003cem\u003eJAMA\u003c/em\u003e, 310(20), 2191-2194.\u003c/li\u003e\n \u003cli\u003eZorba, E., Moraitis, K., \u0026amp; Manolis, S. K. (2011). Sexual dimorphism in permanent teeth of modern Greeks. \u003cem\u003eForensic Science International\u003c/em\u003e, 210(1-3), 74-81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"forensic odontology, sexual dimorphism, canine teeth, sex determination, forensic anthropology, Ghana, diagnostic accuracy","lastPublishedDoi":"10.21203/rs.3.rs-7025187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7025187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e: Sexual dimorphism in dental morphology is fundamental to forensic identification, and population-specific variations are critical for accurate sex determination. Despite its importance in medicolegal investigations, comprehensive data on dental sexual dimorphism remain limited in West African populations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e: This study evaluated the forensic utility of maxillary and mandibular canine dimensions for sex determination in the Northern Ghanaian population and assessed the practical limitations of this approach.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e A cross-sectional study examined 212 participants aged 18-25 years [93 males (43.9%), 119 females (56.1%)] from the Tamale region of Northern Ghana (May-September 2022). Mesiodistal canine widths and inter-canine distances were measured using standardized anthropometric protocols with an inter-rater reliability assessment. Statistical analysis employed descriptive statistics, binary logistic regression, and diagnostic accuracy measures, including ROC curve analysis, with significance set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e: Mandibular canines demonstrated modest sexual dimorphism, with males showing significantly larger dimensions: right mandibular canine width (7.26±0.67mm vs. 6.88±0.51mm, p\u0026lt;0.001), left mandibular canine width (7.15±0.60mm vs. 6.87±0.39mm, p\u0026lt;0.001), and inter-canine distance (30.88±2.74mm vs. 29.32±1.98mm, p\u0026lt;0.0001). Traditional forensic canine indices showed an accuracy equivalent to chance (49.5-51.9%, 95% CI: 43.2-58.6%). Binary logistic regression achieved marginally improved performance, with the left mandibular canine index reaching a maximum accuracy of 58.0% (95% CI: 51.1-64.7%, AUC=0.618).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e: Despite statistically significant sexual dimorphism in Northern Ghanaian canines, forensic utility is severely limited by accuracy rates barely exceeding chance (58% vs. 50%). The 8% improvement over random classification falls substantially below the 80-90% threshold required for reliable forensic applications. Canine-based sex determination lacks practical forensic value as a standalone method in this population\u003c/p\u003e","manuscriptTitle":"Sexual Dimorphism in Canine Teeth for Forensic Sex Determination: A Population-Specific Study from Northern Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 02:08:33","doi":"10.21203/rs.3.rs-7025187/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a162b64-5701-4f97-b850-24b7debbde29","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-02T10:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 02:08:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7025187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7025187","identity":"rs-7025187","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}