Abstract C002: MAD Leukemia AI: A deep learning–based artificial intelligence system for early risk stratification in pediatric acute lymphoblastic leukemia
Moubarak IBRAHIMA MADOUGOUAbstract
Background:
Pediatric acute lymphoblastic leukemia (ALL) is the most common childhood malignancy and remains a leading cause of cancer-related morbidity worldwide. Early risk stratification is essential for optimizing treatment and improving outcomes. This study describes the development and preliminary technical evaluation of MAD Leukemia AI, a deep learning–based artificial intelligence system designed to support early risk stratification and clinical decision support in pediatric ALL.
Methods:
MAD Leukemia AI was developed using a deep learning–based architecture for automated analysis of annotated pediatric leukemia datasets. The prototype integrates data quality assessment, preprocessing, feature extraction, pattern recognition, and AI-assisted classification to identify leukemia-associated features and support risk stratification. Preliminary technical validation was performed on annotated datasets, while prospective clinical validation is currently underway.
Results:
Preliminary technical evaluation demonstrated the feasibility of MAD Leukemia AI for automated analysis of pediatric leukemia data. The system performed data quality assessment, feature extraction, pattern recognition, and AI-assisted risk classification, supporting its potential as a computer-aided clinical decision support system for pediatric ALL. Prospective clinical validation is ongoing to evaluate diagnostic performance, reproducibility, generalisability, and clinical utility.
Conclusions:
MAD Leukemia AI represents a promising deep learning–based clinical decision support system for pediatric acute lymphoblastic leukemia. Further prospective multicentre validation studies will determine its diagnostic performance and potential integration into pediatric oncology workflows.
AI Disclosure:
During the preparation of this work, the authors used ChatGPT (OpenAI) to assist with language refinement and drafting support. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
Citation Format:
Moubarak IBRAHIMA MADOUGOU. MAD Leukemia AI: A deep learning–based artificial intelligence system for early risk stratification in pediatric acute lymphoblastic leukemia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Bridging Discovery and Clinical Impact in Pediatric Cancer; 2026 Sep 22-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_1):Abstract nr C002.