The Model development workflow (Figure ) was organized into three iterative rounds of annotation. In Round 1 (the initial round) we obtained the initial dataset to start the Active Learning cycle. In Round 2, a classical ML modeling approach was employed to create an ensemble of models, allowing to balance selecting candidates for annotation between cases that were likely positive (specifically "class1 prob mean > 0.5 and class1 prob std < 0.1" yielding 287 candidate cases) and uncertain cases (specifically "class1 prob std ≥ 0.1 yielding 213 candidate cases). This process-after expert evaluation-resulted in a pool of 567 experiments affected by air bubbles. In Round 3 we utilized DeepLearning modeling (see Methods for details), which led -after expert evaluation-to a final accumulation of 700 experiments affected by air bubbles. At this stage, the pool of affected traces in the initial dataset appeared to be exhausted, and no further rounds of annotation were required. The accumulated data were then used to train the final ML model for deployment -with an optimized architecture and hyperparameters,Table ). The model performance was measured on prospective experiment validation -to assess generalizability -and on prospective equipment validation.