Thalassemia, a prevalent monogenic disease worldwide, leads to hemolytic anemia due to impaired globin synthesis (1, 2). It is particularly prevalent in regions such as South Africa, the Middle East, and Southeast Asia, as well as in low- and middle-income areas like coastal cities in southern China and rural areas in western China. China bears the highest burden of thalassemia globally, with approximately 30 million individuals affected by thalassemia-related mutations and 3 million suffering from moderate to severe forms, posing significant challenges to families and society (3–5). Due to the autosomal recessive inheritance pattern of thalassemia, parents who are asymptomatic can still have children affected by thalassemia. When both parents carry the thalassemia gene, there is a substantial likelihood of their child developing severe thalassemia, which typically necessitates lifelong blood transfusions and is accompanied by various complications. This imposes a significant burden on affected children, their families, and society. Consequently, early detection of thalassemia during pregnancy assumes paramount importance. Existing approaches for thalassemia screening and diagnosis encompass osmotic fragility tests, assessment of red blood cell (RBC) smears, identification of inclusion bodies, evaluation of red blood cell indices, hemoglobin electrophoresis, high-performance liquid chromatography (HPLC), and genetic testing (6, 7). However, the cost and time associated with genetic testing often hinder timely diagnosis for many thalassemia patients. Conversely, blood routine indicators play a crucial role in the early identification of thalassemia due to the widespread availability of blood routine tests and the ability to distinguish different types of anemia based on red blood cell morphology (8, 9).

In this study, we analyzed the genetic test and blood routine results of 7,621 pregnant women being tested for thalassemia in the Jiangjin area of Chongqing from 2018 to 2022. Additionally, we employed a machine learning model to investigate the predictive value of blood routine indicators for thalassemia. The goal was to offer novel strategies for the early diagnosis, genetic counseling, and treatment of thalassemia in pregnant women within the Chongqing region.

The rapid advancement of artificial intelligence (AI) technology has garnered significant attention worldwide, particularly in the realm of disease diagnosis. Machine learning and other related technologies have been widely explored to aid in this process. In comparison to manual diagnosis, computer-based diagnostic methods offer increased accuracy and efficiency, effectively reducing the misdiagnosis rate. Consequently, these methods enable more effective disease diagnoses at a lower cost (10–12). By harnessing the power of big data and clinical information, machine learning techniques have greatly improved the accuracy and efficiency of clinical diagnoses. This progress has propelled laboratory medicine toward precision medicine and intelligent testing (13, 14). Applying machine learning to the early diagnosis of thalassemia, for instance, allows for the prediction of thalassemia types with higher severity using a blood sample with high prevalence. This approach can effectively reduce the cost and time required for genetic identification of thalassemia, enabling healthcare providers to initiate early treatment measures and offer prompt genetic counseling to pregnant women with thalassemia. Such efforts are important in reducing the birth rate of children with moderate and severe thalassemia and conducting population health surveys in middle and low-income areas.

In this study, a total of 7,621 pregnant women underwent blood routine and thalassemia detection. Among them, 569 cases of α-thalassemia were identified, yielding a positive rate of 7.47%. Additionally, 267 cases of β-thalassemia were detected, with a positive rate of 3.5%. Furthermore, 11 cases of α- combined with β-thalassemia were observed, resulting in a positive rate of 0.14%. These prevalence rates were found to be lower compared to coastal cities such as Guangdong, particularly in relation to α thalassemia, indicating a significant difference as compared to previous studies (4, 15). To mitigate the cost and time associated with genetic diagnosis of thalassemia, a machine learning model was constructed in this study. The model employed routine blood test results for thalassemia prediction. Among the five genotypes with the highest incidence of thalassemia [α3.7/αα, –SEA/αα, CD17 (A-T), CD41-42 (-TCTT), and IVS-II-654 (C-T)], the four coefficients that exhibited the highest correlation with thalassemia were selected: MCV, MCH, RBC, and MCHC. Notably, the prediction performance of MCV and MCH demonstrated superior results. These findings align with previous studies, which have consistently reported reduced MCV and MCH levels across almost all types of thalassemia (15–17).

The prediction model was developed using four variables: MCV, MCH, RBC, and MCHC. The model’s predictive performance was assessed using ROC analysis, yielding AUC values of 0.910, 0.909, 0.807, and 0.801, respectively. These results indicate significant diagnostic value for thalassemia detection. The overall AUC value of the model was found to be 0.913, surpassing the individual variables in terms of prediction accuracy. This result demonstrates the successful construction of the model. To further optimize the model for complex clinical data, adjustments were made to each variable. Subsequently, the entire dataset was utilized for verification purposes. The obtained AUC values for MCV, MCH, RBC, and MCHC were 0.907, 0.906, 0.796, and 0.795, respectively. These values were slightly lower than those obtained during the training phase. However, the model still exhibited a good prediction effect, with an overall AUC value of 0.911. This minor decrease in AUC can be attributed to the inclusion of data from normal pregnant women. Their physiological anemia can interfere with the model, but incorporating such data ensures that the predictive performance of the model aligns more closely with clinical reality, thereby holding significant clinical significance.

It is worth mentioning that the main positive data in the model primarily consist of common thalassemia genotypes, such as -α3.7/αα, –SEA/αα, and CD17 (A-T). Consequently, the model’s predictions may be more accurate for these genotypes. Furthermore, it is crucial to note that unlike the findings of Khan et al. and El-Beshlawy et al. (18, 19), the current model’s predictive capability is limited when it comes to β-thalassemia in regions such as Africa, the Middle East, and other areas, as it primarily focuses on Asia, where α-thalassemia is prevalent. Additionally, since the study subjects predominantly consist of pregnant women, the model may face challenges in accurately predicting thalassemia in men and children. Another limitation of the model is its current inability to differentiate between varying degrees of thalassemia severity, such as severe, moderate, or characteristic types. Furthermore, research by Ferih et al. (20) provides us with a good idea that our model may face challenges in distinguishing between thalassemia and iron-deficiency anemia, as the diagnosis of iron-deficiency anemia often requires additional hematological indicators that may not be fully incorporated into the current model, which need further study.

This study aimed to predict thalassemia in pregnancy by means of economical and rapid blood routine detection by establishing a data prediction model. Through the analysis of 7,621 cases, MCV, MCH, RBC, and MCHC were selected as high correlation indicators. Subsequently, a machine learning prediction model was constructed, incorporating these four indicators as variables, and the results were verified using ROC analysis. The AUC values for MCV, MCH, RBC, and MCHC were 0.907, 0.906, 0.796, and 0.795, respectively. In particular, the prediction model achieved an AUC value of 0.911, demonstrating its effectiveness in thalassemia prediction and provided a novel strategy for the early screening of thalassemia.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by Ethics Committee of The Second People’s Hospital of Jiangjin District, Chongqing. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from primarily isolated as part of your previous study for which ethical approval was obtained. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

YL: Writing – original draft, Writing – review & editing. WB: Writing – review & editing.