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The aim of this study was to test the sexual dimorphism of orbital measurements in the Croatian population using multi-slice computed tomography (MSCT) images. We have retrospectively taken 414 head CT scans of adults from Croatian clinical hospitals in Split and Zagreb (214 males and 200 females) with slice thickness < 1 mm and no pathological or traumatic changes that could affect the measurements. DICOM files were imported into Stratovan Checkpoint Software and viewed in 2D and 3D using semi-transparent 3D volume rendering. Eight standard measurements were calculated based on twelve orbital landmarks (six paired). Principal component analysis (PCA) was used to explore sexual and regional differences, and linear discriminant analysis was used to develop sex classification models. The PCA showed separation based on sex and region, and additional analysis demonstrated that females and males in Split and Zagreb differed in four orbital measurements (P ≤ 0.001). Only those measurements that did not show regional differences were further analyzed, and all showed statistically significant sexual dimorphism. The accuracy of univariate functions for sex estimation ranged from 53.43 to 71.88%, and for multivariate function, the accuracy was 73.45%. The orbital measurements of the Croatian population showed restricted forensic significance for sex classification. On the other hand, we have shown that they can have a potential for exploring the inter- and intra-population differences., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Binary classification techniques are commonly used in forensic examination to test if a specimen belongs to a particular group and base the expert opinion on the questioned evidence. However, most of the currently used methods do not achieve sufficient accuracy due to the ignoring of the specimens classified in the overlapping area. To address the issue, we proposed a novel Adjusted binary classification (ABC) algorithm that automatically adjusts posterior probabilities to reach classification accuracy and positive/negative predicted values (PPV, NPV) of 95 %. In the presented example, we used three handprint measurements from 160 participants (80 males and 80 females) to develop models that would classify sex from their dimensions. The sample was split into the training/cross-validated (70 %) and testing sample (30 %). We developed four classification models using linear discriminant analysis (LDA) by employing traditional single cut-off values and ABC approach that for each group provides a specific posterior probability cut-off threshold. In the cross-validated sample, the accuracy of traditional models was 78.7-92.5 %, while PPVs/NPVs ranged between 78.2 and 93 %. ABC models provided 95 % accuracy, PPV, and NPV, and could classify 35.5-88.1 % of specimens. In the testing sample, ABC models achieved accuracy of 97.3-100 %, PPV/NPV 95.4-100 %, and could be applied to 29.1-87.5 % of specimens. The study demonstrated that the ABC approach could adjust classification models to reach predefined values of accuracy, PPV, and NPV. Therefore, it could be an efficient tool for conducting a binary classification in forensic settings and minimizing the possibilities of incorrect classifications., Competing Interests: Declaration of Competing Interest The authors reported no declarations of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The osteometric methods are the most reliable way to estimate the sex of skeletons when DNA analysis is not used. However, as osteometric studies usually ignore the overlap in male and female skeletal dimensions, they rarely achieve accuracy sufficient for forensic application. To resolve this issue, recent studies suggest sex estimation only when posterior probability (pp) is greater than 0.95, but that approach does not always provide sufficient accuracy and creates a large proportion of unsexed skeleton. Thus, our study aimed to explore whether it is possible to adjust pp on skeletal measurements with pronounced sexual dimorphism to meet 95% accuracy and to enable sex estimation on a reasonable proportion of individuals. From 207 skeletons, we included 65 postcranial measurements and selected 10% of variables with the highest sexual dimorphism. We computed univariate and bivariate discriminant functions using pp threshold of 0.5, 0.95, and the threshold required to achieve accuracy of ≥ 95%. Discriminant functions with pp=0.5 obtained accuracy of 85%-93%, while those with pp≥0.95 and adjusted posterior probabilities obtained 94%-99%. However, we showed that by selecting a particular threshold, sex could be estimated on a greater proportion of individuals than for pp≥0.95: 42%-86% vs. 24%-62% for univariate and 69%-95% vs. 49%-78% for bivariate functions. Therefore, when developing sex estimation models, we suggest not to use fixed pp level, but to adjust pp to achieve 95% accuracy and to minimize the percentage of unsexed skeletons., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest. Some of the data have been previously presented as part of the Doctoral thesis of IJ “Analysis of sexual dimorphism of antique and late antique Salona population,” University of Zadar (Croatia), 2019., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Aim: To determine the sexual dimorphism of the sternum with standard measurements in a contemporary Croatian population sample using multi-slice computed tomography (MSCT) and to compare the data obtained by an automatic with those obtained by a manual approach., Methods: Five sternal measurements were obtained from MSCT images of 73 men and 55 women and three sternal indices were calculated. Custom image analysis software was developed for automatic segmentation and calculation of sternal measurements. Measurements of sexual dimorphism were automatically calculated and compared with manual measurements., Results: All of the sternal measurements exhibited significant differences between men and women. The discrepancies between manual and automatic measurements ranged from 2.8% to 3.6% of the mean average values obtained with the automatic approach. The most accurate single-variable discriminant function was sternal body length (82.8%), the most accurate index was sternal area (89.1%), and the discriminant function using three variables was manubrium width, sternal body length, and sternal body width (90.6%)., Conclusion: Sternal measurements are a reliable sex indicator and can be used in forensic casework. Computer-aided measurement methods can accelerate sex estimation and improve its precision and accuracy.

Aim: To develop discriminant functions for sex estimation on medieval Croatian population and test their application on contemporary Croatian population., Methods: From a total of 519 skeletons, we chose 84 adult excellently preserved skeletons free of antemortem or postmortem changes and took all standard measurements. Sex was estimated/determined using standard anthropological procedures and ancient DNA (amelogenin analysis) where pelvis was insufficiently preserved or where sex morphological indicators were not consistent. We explored which measurements showed sexual dimorphism and used them for developing univariate and multivariate discriminant functions for sex estimation. We included only those functions that reached accuracy rate ≥80%. We tested the applicability of developed functions on modern Croatian sample (n=37)., Results: From 69 standard skeletal measurements used in this study, 56 of them showed statistically significant sexual dimorphism (74.7%). We developed five univariate discriminant functions with classification rate 80.6%-85.2% and seven multivariate discriminant functions with an accuracy rate of 81.8%-93.0%. When tested on the modern population functions showed classification rates 74.1%-100%, and ten of them reached aimed accuracy rate. Females showed higher classified in the mediaeval populations, whereas males were better classification rates in the modern populations., Conclusion: Developed discriminant functions are sufficiently accurate for reliable sex estimation in both medieval Croatian population and modern Croatian samples and may be used in forensic settings. The methodological issues that emerged regarding the importance of considering external factors in development and application of discriminant functions for sex estimation should be further explored.

Aim: To investigate the usefulness of humerus measurement for sex determination in a sample of medieval skeletons from the Eastern Adriatic Coast. Additional aim was to compare the results with contemporary female population., Methods: Five humerus measurements (maximum length, epicondylar width, maximum vertical diameter of the head, maximum and minimum diameter of the humerus at midshaft) for 80 male and 35 female medieval and 19 female contemporary humeri were recorded. Only sufficiently preserved skeletons and those with no obvious pathological or traumatic changes that could affect the measurements were included. For ten samples, analysis of DNA was performed in order to determine sex using amelogenin., Results: The initial comparison of men and women indicated significant differences in all five measures (P<0.001). Discriminant function for sex determination indicated that as much as 85% of cases could be properly categorized, with better results in men (86%) than women (80%). Furthermore, the comparison of the medieval and contemporary women did not show significant difference in any of the measured features. Sex results obtained by anthropological and DNA analysis matched in all 10 cases., Conclusion: The results indicate that humerus measurement in Croatian medieval population may be sufficient to determine the sex of the skeleton. Furthermore, it seems that secular changes have not substantially affected contemporary population, suggesting that the results of this study are transferable to contemporary population as well.