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-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.