Background: Multicolor flow cytometry (MFC) analysis iswidely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.

Methods: From2009 to 2016, 5333MFC data from1742AMLorMDSpatientswere collected. The 287MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype fromthe training set and input it to support vector machine (SVM) classifier after Gaussianmixture model (GMM) modeling, and the performance was evaluated in The validation set.

Findings: Promising accuracies (84·6% to 92·4%) andAUCs (0·921–0·950)were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p b 0·0001) and overall survival (13·6 vs 6·5 months, p b 0·0001) for AML.

Interpretation: Through large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests.

Fund: This workwas supported by theMinistry ofScience and Technology (107-2634-F-007-006 and 103–2314B-002-185-MY2) of Taiwan.