Application of multi-scale feature extraction and explainable machine learning in chest x-ray position evaluation within an integrated learning framework

Question How can AI-driven interpretable methods be utilized to assess patient positioning in chest radiography and enhance radiographers' accuracy? Findings The Random Forest Fusion Network (RFFN) outperformed Threshold Classification (TC) and Multivariate Logistic Regression (MLR) in positioning classification (AUC = 0.98). Clinical relevance An integrated framework that combines deep learning and machine learning achieves accurate image positioning classification, identifies critical operational factors, enables expert-level image quality assessment, and delivers automated feedback to radiographers.