An Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi-Modal Deep Neural Network.

Background: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment plans for patients.

Methods: In this study, we propose a multi-modal deep neural network-based framework for early and efficient screening of ALL. Both white blood cell (WBC) scattergrams and complete blood count (CBC) are employed for ALL detection. The dataset comprises medical data from 233 patients with ALL, 283 patients with infectious mononucleosis (IM), and 183 healthy controls (HCs).

Results: The combination of CBC data with WBC scattergrams achieved an accuracy of 98.43% in fivefold cross-validation and a sensitivity of 96.67% in external validation, demonstrating the efficacy of our method. Additionally, the area under the curve (AUC) of this model surpasses 0.99, outperforming well-trained medical technicians.

Conclusions: To the best of our knowledge, this framework is the first to incorporate WBC scattergrams with CBC data for ALL screening, proving to be an efficient method with enhanced sensitivity and specificity. Integrating this framework into the screening procedure shows promise for improving the early diagnosis of ALL and reducing the burden on medical technicians. The code and dataset are available at https://github.com/cvi-szu/ALL-Screening.