Impact of Case and Control Selection on Training AI Screening of Cardiac Amyloidosis
Bibliographic record
Abstract
Abstract Background Recent studies suggest that cardiac amyloidosis (CA) is significantly underdiagnosed. For rare diseases like CA, the optimal selection of cases and controls for artificial intelligence (AI) model training is unknown and can significantly impact model performance. Objectives This study evaluates the performance of ECG waveform-based AI models for CA screening and assesses impact of different criteria for defining cases and controls. Methods Models were trained using different criteria for defining cases and controls including amyloidosis by ICD 9/10 code, cardiac amyloidosis, patients seen in CA clinic). The models were then tested on test cohorts with identical selection criteria as well as population-prevalence cohorts. Results In matched held out test datasets, different model AUCs ranged from 0.660 to 0.898. However, these same algorithms exhibited variable generalizability when tested on a population cohort, with AUCs dropping to 0.467 to 0.880. More stringent case definitions during training result in higher AUCs on the similarly constructed test cohort; however representative population controls matched for age and sex resulted in the best population screening performance. Conclusions AUC in isolation is insufficient to evaluate the performance of a deep learning algorithm, and the evaluation in the most clinically meaningful population is key. Models designed for disease screening are best with matched population controls and performed similarly irrespective of case definitions.
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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 | 0.045 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".