Enhancing thalassemia gene carrier identification in non‐anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning

Abstract

BackgroundNon‐anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China.ObjectiveTo use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non‐anemic population.MethodsDigital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology‐based Mindray MC‐100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model.ResultsNon‐anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non‐anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non‐anemic population.ConclusionsThe ML‐based model TT@Normal could efficiently identify TT carriers in non‐anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier.