Validation pipeline for machine learning algorithm assessment for multiple vendors

Abstract

A standardized objective evaluation method is needed to compare machine learning (ML) algorithms as these tools become available for clinical use. Therefore, we designed, built, and tested an evaluation pipeline with the goal of normalizing performance measurement of independently developed algorithms, using a common test dataset of our clinical imaging. Three vendor applications for detecting solid, part-solid, and groundglass lung nodules in chest CT examinations were assessed in this retrospective study using our data-preprocessing and algorithm assessment chain. The pipeline included tools for image cohort creation and de-identification; report and image annotation for ground-truth labeling; server partitioning to receive vendor “black box” algorithms and to enable model testing on our internal clinical data (100 chest CTs with 243 nodules) from within our security firewall; model validation and result visualization; and performance assessment calculating algorithm recall, precision, and receiver operating characteristic curves (ROC). Algorithm true positives, false positives, false negatives, recall, and precision for detecting lung nodules were as follows: Vendor-1 (194, 23, 49, 0.80, 0.89); Vendor-2 (182, 270, 61, 0.75, 0.40); Vendor-3 (75, 120, 168, 0.32, 0.39). The AUCs for detection of solid (0.61–0.74), groundglass (0.66–0.86) and part-solid (0.52–0.86) nodules varied between the three vendors. Our ML model validation pipeline enabled testing of multi-vendor algorithms within the institutional firewall. Wide variations in algorithm performance for detection as well as classification of lung nodules justifies the premise for a standardized objective ML algorithm evaluation process.