Identification Of Exercise-induced Expiratory Flow-limitation Using Deep Machine Learning In Healthy Adults Across The Lifespan
Bibliographic record
Abstract
BACKGROUND: Expiratory flow limitation (EFL) is an indicator of ventilatory constraint that provides important information in the context of cardiopulmonary exercise testing (CPET), but its widespread use during CPET has been hampered by technical and logistical challenges. To overcome these challenges, we developed a convolutional neural network (CNN) to identify EFL; however, this CNN requires validation in a larger cohort with a wider age range. It is also unclear if accounting for the effects of thoracic gas compression and exercise-induced bronchodilation when assessing EFL influences CNN performance. PURPOSE: To determine the effectiveness of a CNN at identifying exercise-induced EFL in healthy adults across the lifespan. METHODS: n = 82 healthy adults (aged 20-80 y; n = 40 males, n = 42 females) completed pulmonary function testing and an incremental CPET on a cycle ergometer. During CPET, EFL was assessed by overlaying tidal expiratory flow-volume (TEFV) curves with maximal expiratory flow-volume curves generated: i) from a forced vital capacity manoeuvre performed prior to exercise (MEFVPRE), and ii) by combining a series of forced vital capacity manoeuvres performed at varying efforts before and after exercise to account for the effects of thoracic gas compression and exercise-induced bronchodilation (MEFVCOMB). TEFV curves were input into the CNN with one of three labels: 1) no EFL based on MEFVPRE and MEFVCOMB, 2) EFL based on MEFVPRE, or 3) EFL based on MEFVCOMB. A CNN with seven hidden layers and a 4-neuron softmax output layer was used to analyze TEFV curve geometry and classify each curve as EFL or no EFL according to labels 1-3. RESULTS: The CNN processed 6647 TEFV curves; 4453 (67 %) were used for training, and 2194 (33 %) were used for testing. Overall, the CNN correctly classified 85 % of the TEFV curves, with 94 % correctly classified as no EFL based on MEFVPRE and MEFVCOMB, 36 % correctly classified as EFL based on MEFVPRE, and 73 % correctly classified as EFL based on MEFVCOMP. CONCLUSION: These data suggest that our CNN is an effective tool to assess EFL during exercise. However, the CNN’s poor performance in categorizing TEFV curves based on MEFVPRE emphasizes the importance of accounting for the effects of thoracic gas compression and exercise-induced bronchodilation in the assessment of EFL.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".