Identification of Misdiagnosis Factors in Allergic Bronchopulmonary Mycosis Using Explainable Machine Learning.

Abstract

Background: Allergic bronchopulmonary aspergillosis/mycosis (ABPA/ABPM) is frequently misdiagnosed as pulmonary tuberculosis due to overlapping clinical and radiological features.

Methods: In a retrospective cohort of 89 multidisciplinary team (MDT)-confirmed ABPA/ABPM patients, we investigated determinants of misdiagnosis and disentangled causal drivers from spurious associations. Machine learning models with SHAP interpretation were utilized alongside a Double Machine Learning framework to estimate the average treatment effects of five key features while adjusting for major confounders.

Results: Immunological markers were identified as the dominant contributors to misdiagnosis. Total serum IgE showed the strongest protective causal effect against misdiagnosis, followed by Aspergillus fumigatus-specific IgE. Bronchiectasis demonstrated a modest protective effect. These findings were robust across covariate balance, overlap, and placebo analyses.

Conclusion: Our results indicate that ABPA/ABPM misdiagnosis is driven primarily by underrecognition of immunological features rather than imaging findings, underscoring the importance of systematic immunologic assessment to reduce diagnostic delay and unnecessary anti-tuberculosis treatment, particularly in tuberculosis-endemic settings.