The value of machine learning inference to predict multidisciplinary discussion diagnosis of interstitial lung diseases.

Abstract

Objectives: Multidisciplinary discussion (MDD) is the reference standard for diagnosing interstitial lung diseases (ILD), however, it requires considerable expertise, and up to 25% of patients remain unclassifiable. We evaluated whether machine learning (ML) models trained on multimodal data can accurately replicate MDD consensus ILD diagnoses.

Materials & methods: We retrospectively identified 428 patients with ILD confirmed by MDD consensus at a tertiary academic center from 2010 to 2021. Using comprehensive clinical and radiological data, we trained and cross-validated multiple ML models for three classifications: (1) three-level: connective tissue disease associated ILD (CTD-ILD), usual interstitial pneumonia (UIP), or unclassifiable; (2) UIP/IPF versus all other, and (3) unclassifiable versus all other. Logistic regression, random forest, and neural networks ML models were trained with five-fold cross-validation. Performance was compared via area under the curve (AUC) with 95% confidence intervals.

Results: MDD diagnoses were UIP/IPF in 131 patients (30.6%), CTD-ILD in 130 (30.4%), and unclassifiable ILD in 167 (39.0%). Clinical variables and CT imaging features were more predictive than PFTs. ML models achieved AUC values ranging from 0.66 to 0.92. For UIP/IPF vs. other, the neural network reached an AUC of 0.88. Notably, the Lasso regression model exhibited high specificity (0.966), suggesting utility as a high-confidence "rule-in" AI tool.

Conclusion: ML classification closely approximated MDD diagnoses of ILD, especially for UIP/IPF, relying primarily on clinical and CT imaging features, even without pathology information, allowing expert-level diagnostic performance where MDD resources are not available.