Cross-validated prediction of therapeutically relevant genetic groups using the GFEN on the discovery cohort. Deep learning models were trained to predict therapy relevant genetic subgroups from bone marrow smears. The patient-level performance of the GFEN is evaluated for every genetically defined group (ELN 2017 favorable risk, CBFB::MYH11 fusions, NPM1 mutations, FLT3-ITD and -tyrosine kinase domain (TKD) mutations, and MRC cytogenetics) with the AUROC and 2-sided P value for the prediction scores. Values for the validation runs of the fivefold cross-validation are given for the individual folds (cv0, cv1, cv2, cv3, and cv4) and all validation folds of the complete cohort combined (compiled). Error bars show 95% CI. The dashed line represents a P value <.05.