Assessment improved of cognitive impairment with artificial intelligence in the user‐web‐mobile application
Bibliographic record
Abstract
Abstract Background The World Health Organization forecasts a population of 2,000 million people over 60 years by the year 2050, with 7% of this population suffering from dementia. Making a constant clinical‐technological evaluation of older adults allows early detection of the disease and provides a better quality of life for the patient. In this sense, the research and development of innovative technological systems for the early detection of the disease, its monitoring and management of the growing number of patients with cognitive diseases has increased in recent years, integrating data collection and its automatic processing based on geriatric metrics into these systems using artificial intelligence (AI) methods. Method This research presents an interactive web platform that allows users with any intelligent device with internet access, to remotely perform an automated assessment of the Montreal Cognitive Assessment (MoCA) test. We use AI and neural network methods for binary and multiclass classification to obtain assessment scores according to geriatric metrics. The application provides an automated evaluation of the MoCA test, which can then be validated remotely by a mental health specialist. Result The tests performed show a correct correspondence in the handling of the information and results of each MoCA item with respect to the database. For the test database evaluated with the application, results are obtained with 100% accuracy and equal to the evaluations performed by specialists. Conclusion This automated assessment provides great help to the medical specialist in the process of detection and evaluation of cognitive impairment, significantly improving the quality of healthcare. The management and organization for the follow‐up of the patient’s cognitive impairment is done through the information of the tests performed, their evaluation and the clinical history of each patient. This information is consulted and managed by the doctor for the patient’s follow‐up; the caregiver/family member has access to tests performed, their evaluation and all the follow‐up that the doctor gives to the patient. The interface was developed thinking in the elderly. It is intuitive, with the relevant information and graphic elements, the procedure for using the application is explained step‐by‐step, the colors used are comfortable for eye care.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".