Year: 2026
Type: article
Abstract: Current artificial intelligence systems for leukemia detection typically rely on costly whole-slide scanners, limiting accessibility in low-resource settings. We present ALLocate, a low-cost, artificial intelligence-powered microscope plugin that enables self-driving microscopy for leukemia detection. ALLocate attaches directly to conventional microscopes and provides automated analysis at a fraction of the cost of a whole-slide scanner. We evaluate its robustness at three levels: region-of-interest identification, cell detection, and end-to-end slide-level diagnosis. The system is trained and evaluated using more than 11,000 annotated regions and 130,000 annotated cells and is further validated using independent multi-institutional cohorts, including 165 physical bone marrow smear slides. ALLocate achieves an area under the receiver operating characteristic curve greater than 0.99 for region-of-interest identification, a mean average precision at 50% intersection over union of 0.90 for cell detection, and 88% accuracy for slide-level diagnosis on glass slides without requiring a whole-slide scanner. These results suggest that ALLocate provides an accurate, generalizable, and cost-effective approach for automated bone marrow smear screening, helping bridge the gap between AI innovation and practical deployment in resource-limited settings where access to specialist expertise may be limited.
Source: Nature Communications
Authors: Ethan S. Yan, Shenghuan Sun, Zhanghan Yin, Ali Kamali, Jacob Van Cleave, Irem S. Isgor, Brenda Fried, Cesar Colorado-Jimenez, Deepika Dilip, Jeeyeon Baik, Allyne Manzo, Sean Paulsen, Siddharth Singi, Scott Drapeau, Ozgur Can Eren, Joanne K. Chun, Aijazuddin Syed, Anthony Cardillo, Melissa Pulitzer, Maly Fenelus, David Kim, Jamal Benhamida, Dianna Ng, Orly Ardon, Mikhael Roshal, Ahmet Dogan, Chad Vanderbilt, Khawaja H. Bilal, Gregory M. Goldgof
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7204446355
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