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

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.