Development of criteria to optimize manual smear review of automated complete blood counts using a machine learning model.

Objective: In this study, we aim to determine if machine learning can reduce manual smear review (MSR) rates while meeting or exceeding the performance of traditional MSR criteria.

Method: 9938 automated CBCs with paired MSRs were performed on samples from rhesus and cynomolgus macaques. The definition of a positive (abnormal) smear was determined. Two expert-derived MSR criteria were created: criteria adapted from published, standardized human laboratory criteria (Adapted International Consensus Guidelines[aICG]) and internally generated criteria (Center Consensus Guidelines [CCG]). An ensemble machine learning model was trained on an independent subset of the data to optimize the balanced accuracy of classification, a combined measure of sensitivity and specificity. The resulting machine learning model and the two expert-derived MSR criteria were applied to a test dataset, and their performance compared.

Results: aICG criteria demonstrated high sensitivity (80.8%) and MSR rate (74.2%) while CCG criteria demonstrated lower sensitivity (57.1%) and MSR rate (36.1%). The machine learning model integrated with CCG criteria had a superior combination of both sensitivity (76.8%) and MSR rate (45.1%) achieving a false negative rate of 1.6%.

Conclusion: Machine learning in combination with expert-derived criteria can optimize the selection of samples for MSR thus decreasing MSR rates and labor efforts required for CBC performance.