Factorial evaluation of DICOM harmonization and clinically constrained augmentation for multi-vendor TB triage

Tuberculosis (TB) triage from chest radiographs is influenced by image representation and training-data variability in addition to model architecture. This study quantified the independent and interaction effects of these workflow factors using 3522 examinations from 3100 patients in a single-institution multi-vendor PACS/DICOM cohort. A representation-matched 2 × 2 factorial design compared a conventional DICOM-to-PNG representation (P0) with DICOM-aware HyFusion preprocessing (P1), and the original training set (A0) with a deterministic clinically constrained augmented pool (A1). Four DenseNet121 conditions used identical patient-level partitions, optimization settings, three random seeds, and a fixed raw-probability threshold of 0.5. Evaluation included discrimination, operational burden per 1000 examinations, validation-only temperature scaling, 5000 paired patient-level bootstrap resamples, and adjusted generalized estimating equations. C3 (P1A0) achieved the highest mean AUROC (0.8732 ± 0.0085) and AUPRC (0.8809 ± 0.0194). Relative to C1 (P0A0), the AUROC and AUPRC differences were ＋0.0185 (95% CI −0.0070 to ＋0.0451) and ＋0.0217 (95% CI −0.0023 to ＋0.0487), respectively. At the fixed threshold, C3 increased specificity and PPV and reduced referrals by 78.5 and false positives by 53.5 per 1000 examinations. A1 reduced referrals and false positives but also reduced sensitivity and increased false negatives. Temperature scaling improved NLL, Brier score, and ECE10 across all conditions. The study establishes a reproducible workflow-factor attribution framework and identifies P1A0 as the DenseNet121 configuration with the highest mean discrimination and a favorable calibration and referral-efficiency profile in this cohort.