ALLocate: A Low-Cost Automatic Artificial Intelligence System for the Real-Time Localization and Classification of Acute Leukemia in Bone Marrow Smears

Overview

The precise and accurate leukemia detection in current clinical practice remains challenging due to limitations in cost, time, and medical experience. To address this issue, this research develops ALLocate, the first integrated low-cost automatic artificial intelligence system for the real-time localization and classification of acute myeloid leukemia in bone marrow smears. ALLocate consists of an automatic microscope scanner system, an image sampling system, and a deep learning-based detection system. The automatic microscope scanner system uses 3D-printed pieces controlled by stepper motors and a RAMPS control board. For image sampling, a region classifier using a convolutional neural network (CNN) model was developed to select usable regions from unusable blood and clot regions. To achieve cell segmentation, a U-net-based model was established in usable marrow regions. For real-time detection, the YOLOv8 model was developed and optimized. The key variables for optimization include the number of epochs, learning rate, and network architecture. These models show high performance with a region classifier accuracy of 96%, U-net accuracy of 85%, and YOLOv8 mAP of 91%. When ALLocate was applied to marrow smears, its leukemia detection results were statistically the same with a doctor (with only 1% difference), but ALLocate is much faster (<1 minute) than a doctor (30 minutes per slide) This is the first demonstration of an integrated deep learning system with a low-cost microscope automatic scanner system for leukemia detection. ALLocate can significantly improve the efficiency of leukemia detection from the marrow smears, especially in underserved communities, making healthcare more accessible to all.

Awards (4)

• Third Award of $1,000 $1,000
• Association for the Advancement of Artificial Intelligence: Honorable Mention (do not read aloud). Winners receive a student level membership. Information is included separately in the SAO Portal.
• Association for the Advancement of Artificial Intelligence: AAAI Student Memberships for each finalist that is part of the 1st, 2nd, and 3rd Prize Winning projects and 5 Honorable Mention winning projects (up to 3 students per project) (in-kind award / part of the 1st-3rd prize)
• Association for the Advancement of Artificial Intelligence: AAAI Membership for the School Libraries of All 8 Winners (in-kind award / part of 1st-3rd prize and honorable mentions' prize)

Competition history

• ISEF 2024 Translational Medical Science · Entry TMED064 · Los Angeles, California, United States Affiliated fair in MA, United States of America

Resources

• ISEF abstract page ↗

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