25gmds189 10.3205/25gmds189 urn:nbn:de:0183-25gmds1897 Meeting Abstract Unger Unger Sebastian S

Witten/Herdecke University, Witten, Germany

author Ostermann Ostermann Thomas T

Universität Witten/Herdecke, Witten, Germany

author Berger Berger Bettina B author Martin Martin David D

Witten/Herdecke University, Faculty of Health, Department of Human Medicine, Witten/Herdecke, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 cognitive psychology projective technique children drawing detection algorithms feasibility study 20260401 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 189 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) V: Bilddaten Jena 20250907 20250911 Abstr. 332 TextIntroduction: With the increasing awareness of mental health, the number of tools designed to measure mental health metrics, such as well-being or depression, is also increasing. In most cases, these tools rely on verbal and textual assessment scales , which can be a barrier, especially for children. This is because children may not be able to express their feelings appropriately or without inhibition due to a limited verbal repertoire . Projective tests, such as the House-Person-Tree Drawing Test (HPT), could offer an easy-to-understand and engaging alternative. Given the current lack of digital solutions aimed at data collection and analysis of HPT drawings , this investigation presents a semantic scene segmentation approach for detecting age and gender differences in children's HPT drawings.Methods: From a cross-sectional study, 303 HPT drawings from children (175 girls, 127 boys, and one of unknown gender) aged between 6 and 12 years (M = 8.5, SD = 1.14) were available. All drawings, except for the one where the child's gender was missing, were processed by a Scene Sketch Segmentation Model (SSSM) to calculate similarity matrices for the house, person, and tree objects. Using these matrices and a threshold of 0.71, at which the chosen SSSM performed best in its evaluation of performance with the test data , the number of pixels exceeding this threshold was determined. Subsequently, these pixels counts were analyzed using a two-way Multivariate Analysis of Variance (MANOVA), with age and gender serving as predictors.Results: According to the results of the two-way MANOVA, there are significant multivariate effects. First, age exhibits a strong influence on the number of pixels (Pillai's Trace = .044, F = 4.525, p = .004). Second, with similar influence, gender is also a statistically significant predictor (Pillai's Trace = .061, F = 3.125, p = .005). An interaction effect between age and gender, however, could not be found (Pillai's Trace = .008, F = .817, p = .485).Discussion: The analysis indicates that differences in pixel counts based on age and gender can be detected using a SSSM. The age effect, in particular, is in accordance with findings in children’s expressive happy and sad drawings , suggesting that children noticeably develop their drawing skills with age. But further research is needed as these age and gender effects might be explained by the frequency with which the three objects had been drawn, rather than solely by limited drawing skills. Furthermore, the results are highly dependent on the SSSM’s quality and may contain a bias, even if this SSSM can be considered state-of-the-art.Conclusion: Interpreting children's HPT drawings with an SSSM seems feasible. Consequently, this approach could potentially supplement conventional HPT assessment methods for identifying developmental disorders in children. However, further research is needed to strengthen these results.The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. 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