NeuroLingo – Smart Brain Health Test in Indian Languages
Bibliographic record
Abstract
Early detection of cognitive reduction is essential in directing the neurodegenerative conditions like dementia and Alzheimer's disease. Although conventional screening tools such as the Mini-Mental state Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were mostly restricted to English, limiting their applicability among the rural and the low-literacy populations in India. To overcome these obstacles, we recommend NeuroLingo, a multilingual, AI-driven brain health assessment platform that uses natural language processing (NLP), The automatic speech recognition (ASR), and machine learning models were comprehensively cognitive screening. The system provides simple games like exercises to check to evaluate memory, attention, understanding, and language abilities using both spoken and written responses in Indian languages. The system uses a mix of rule-based scoring and machine learning models, while adjusting the scores according to a person's age and education level so the results are fair for everyone. Reports generated include domain wise scores and overall brain health index (BHI), offering actionable insights for individuals, caregivers, and clinicians. By combining multilingual accessibility with AI- powered analysis, NeuroLingo provides a scalable, cost effective, and inclusive solution for early brain health screening in underserved populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".