A Digital Screening System for Alzheimer Disease Based on a Neuropsychological Test and a Convolutional Neural Network: System Development and Validation
Bibliographic record
Abstract
BACKGROUND: Alzheimer disease (AD) and other types of dementia are now considered one of the world's most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia. In addition to reducing the physical and psychological stress of patients' caregivers in the long term, it will also improve the everyday quality of life of patients. OBJECTIVE: The aim of this study was to design a digital screening system to discriminate between patients with MCI and AD and healthy controls (HCs), based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological test. METHODS: The study took place at National Taiwan University between 2018 and 2019. In order to develop the system, pretraining was performed using, and features were extracted from, an open sketch data set using a data-driven deep learning approach through a convolutional neural network. Later, the learned features were transferred to our collected data set to further train the classifier. The first data set was collected using pen and paper for the traditional method. The second data set used a tablet and smart pen for data collection. The system's performance was then evaluated using the data sets. RESULTS: The performance of the designed system when using the data set that was collected using the traditional pen and paper method resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.913 (SD 0.004) when distinguishing between patients with MCI and HCs. On the other hand, when discriminating between patients with AD and HCs, the mean AUROC was 0.950 (SD 0.003) when using the data set that was collected using the digitalized method. CONCLUSIONS: The automatic ROCF test scoring system that we designed showed satisfying results for differentiating between patients with AD and MCI and HCs. Comparatively, our proposed network architecture provided better performance than our previous work, which did not include data augmentation and dropout techniques. In addition, it also performed better than other existing network architectures, such as AlexNet and Sketch-a-Net, with transfer learning techniques. The proposed system can be incorporated with other tests to assist clinicians in the early diagnosis of AD and to reduce the physical and mental burden on patients' family and friends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".