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Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning.
- Source :
-
European journal of haematology [Eur J Haematol] 2024 May; Vol. 112 (5), pp. 692-700. Date of Electronic Publication: 2023 Dec 28. - Publication Year :
- 2024
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Abstract
- Background: Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China.<br />Objective: To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population.<br />Methods: 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 />Results: 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 />Conclusions: 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.<br /> (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)
Details
- Language :
- English
- ISSN :
- 1600-0609
- Volume :
- 112
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- European journal of haematology
- Publication Type :
- Academic Journal
- Accession number :
- 38154920
- Full Text :
- https://doi.org/10.1111/ejh.14160