Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists
Bibliographic record
Abstract
PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the non-invasive PH assessment. This study aimed to develop a machine learning (ML)-based educational resource for classifying PH cases via CECT according to ESC/ERS groups. METHODS: We retrospectively included 172 PH patients who underwent CECT at two University Hospitals (Udine and Brescia). Three chest-devoted radiologists independently reviewed the CECTs, reporting on 13 features, including lung conditions, heart abnormalities, chronic thromboembolism, and mediastinal findings. Readers assigned the features as absent/present except for the left atrium (LA) anteroposterior diameter (measured in millimeters) and classified PH cases I-V with likelihood scores (1-100 %) for each group. The majority decisions for features and average LA diameter were used as ML inputs. The highest average likelihood scores determined group assignments, serving as ground truth. Various ML algorithms were tested using the Weka software and evaluated by accuracy, area under the ROC curve (AUROC), and F1-score. RESULTS: After excluding three group V patients to avoid imbalance, the Naïve-Bayes algorithm showed 0.72 accuracy, 0.84 AUROC, and 0.72 F1-score. Accuracy values for group I-IV were 0.75, 0.78, 0.51, 0.79; AUROC values were 0.78, 0.84, 0.86, 0.87; F1-scores were 0.63, 0.79, 0.61, 0.84, respectively. CONCLUSIONS: This study is the first to develop an ML-driven tool for classifying PH via chest CECT. While performance metrics require improvement, including the need for a larger sample size, the resource can potentially train non-dedicated radiologists in PH classification, supporting multidisciplinary reasoning.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".