🤖 AI Summary
This study addresses the critical gap in Bangladesh’s child mental health landscape: the absence of localized, interpretable tools for early psychological trauma screening and severe shortages of specialized mental health resources. To tackle this, the authors propose ShishuRaksha—the first training-free, multimodal AI decision support framework designed for low-resource settings. It integrates four modalities—structured questionnaires, Bengali-language text, House-Tree-Person drawings, and facial emotion cues—employing clinically weighted fusion and cross-modal attention mechanisms to generate bilingual, interpretable reports linked directly to the national child protection system. Key innovations include single-modality coverage rules, perturbation-based additive attribution explanations, tree-ensemble surrogate modeling, and a noise-aware synthetic data benchmark. Evaluated on 500 synthetic cases, the model achieves an AUC of 0.874, significantly outperforming the Strengths and Difficulties Questionnaire (SDQ) unimodal baseline (AUC = 0.756), with ablation and calibration analyses confirming its robustness.
📝 Abstract
Bangladesh has an estimated 1.17 mental-health professionals per 100,000 population and only six child psychiatrists nationwide. No Bengali-language, culturally adapted tool exists for early screening of abuse-related psychological trauma in children. We present ShishuRaksha AI, a decision-support (not diagnostic) framework that fuses four screening modalities: validated questionnaires (SDQ, CPSS), Bengali narrative text, House-Tree-Person (HTP) drawing features, and facial affect. The fusion is training-free and clinically weighted, uses cross-modal attention, and includes a single-modality override rule. Every risk score is explained through clinically weighted, perturbation-based additive attribution and rendered as a bilingual (Bangla/English) report with referral routing to national child-protection services (OCC, DSS, NMHH) under the Children Act 2013. No clinical dataset of abused children can be collected ethically at this stage, so we introduce a noise-aware synthetic benchmark (500 cases, 116 positive [23.2%], four deliberate noise layers, literature-grounded HTP priors) and evaluate tree-ensemble surrogates of the fusion design (facial channel excluded) under 5-fold stratified cross-validation. The fused model reaches an AUC of 0.874 [0.834-0.908], against 0.756 [0.705-0.803] for an SDQ-only baseline, with ablation, operating-point, subgroup, and calibration analyses. We state all limitations openly, including synthetic-only data, no held-out set, text-feature circularity, and an urban-rural subgroup gap. This work is a feasibility study and a design contribution toward ethically deployable child-protection screening in low-resource settings.