{Reference Type}: Journal Article
{Title}: Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning.
{Author}: Zhang F;Zhan J;Wang Y;Cheng J;Wang M;Chen P;Ouyang J;Li J;
{Journal}: Eur J Haematol
{Volume}: 112
{Issue}: 5
{Year}: 2024 May 28
{Factor}: 3.674
{DOI}: 10.1111/ejh.14160
{Abstract}: <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.