Intelligent Web Application of Montreal Cognitive Assessment Testing for Dementia Screening : iMOCA
Bibliographic record
Abstract
This paper presents the development of a web-based application designed to provide accessible cognitive screening for dementia using a subset of the Montreal Cognitive Assessment (MoCA) test. Aimed at individuals who may not have access to traditional healthcare facilities, the application delivers five key cognitive tests: Trail Making, Clock Drawing, Memory Recall, Animal Naming, and Serial Subtraction (subtracting 7 from 100 five times). The application was developed using Flutter, leveraging its capability to compile cross-platform applications into a web-based interface for ease of deployment and user access. While most cognitive tasks are processed directly within the Flutter front end, the Clock Drawing Test requires backend support for image-based prediction. This is achieved through a Flask API built with Python, which hosts a machine learning model trained to evaluate clock drawings and return predictive results to the front end. This hybrid architecture provides both interactive user experience and AI-powered assessment, demonstrating a scalable and cost-effective tool for preliminary dementia screening. [1]
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".