Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning.
Eur J Haematol
; 112(5): 692-700, 2024 May.
Article
in En
| MEDLINE
| ID: mdl-38154920
ABSTRACT
BACKGROUND:
Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China.OBJECTIVE:
To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population.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.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.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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Thalassemia
/
Beta-Thalassemia
Limits:
Humans
Language:
En
Journal:
Eur J Haematol
Journal subject:
HEMATOLOGIA
Year:
2024
Type:
Article
Affiliation country:
China