%0 Journal Article
%T Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning.
%A Zhang F
%A Zhan J
%A Wang Y
%A Cheng J
%A Wang M
%A Chen P
%A Ouyang J
%A Li J
%J Eur J Haematol
%V 112
%N 5
%D 2024 May 28
%M 38154920
%F 3.674
%R 10.1111/ejh.14160
%X <B>BACKGROUND: </B>Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China.<BR><B>OBJECTIVE: </B>To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population.<BR><B>METHODS: </B>Digital 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.<BR><B>RESULTS: </B>Non-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.<BR><B>CONCLUSIONS: </B>The 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.