Cost-Effective MalariaX: An AI-Optomechatronic System Decoding Expert Reasoning Into a Sub-Micron, Pan-Species Clinically Traceable Pipeline

Overview

Inaccurate malaria diagnosis in remote areas drives drug resistance and fatal treatments. Rapid tests miss the critical P. knowlesi, while standard Whole Slide Imaging (WSI) microscopes are prohibitively expensive ($35,000) and grid-dependent. Furthermore, existing AI lacks the morphological explainability required for clinical trust. This study presents Cost-Effective MalariaX, an AI-optomechatronic diagnostic platform addressing these barriers. Phase 1 (MALA-Sight) introduces an AI pipeline trained on 25,000+ images. To address the “black-box” limitation, it utilizes a Morpho-Species Map designed for explainability, providing WHO-aligned visual outputs. The model was optimized for edge-device deployment via INT8 quantization for localized inference in resource-limited settings. Phase 2 (XcopeRapid-Sight) engineered an automated scanner designed to approximate FDA WSI imaging guidelines. Key hardware adaptations include: (1) transforming low-cost unipolar stepper motors into a bipolar configuration for optimized torque, (2) designing a custom spindle-sleeve precision lead screw, and (3) a custom PCB bridging hardware-software synchronization, enabling the MALA-Sight AI to command mechanical motion on a localized Edge AI platform. These modifications achieved a 0.33 µm Z-axis mechanical resolution. At $257, this 4th-iteration hardware prototype represents a 99.27% cost reduction relative to commercial systems. Within a 1.93kg grid-independent, compact, and robust, IPX4-rated enclosure, the architecture enables autonomous diagnosis. Validated at two endemic field centers (98.80% Sp) and through cross-country samples (98.40% Sp), this study demonstrates a feasible engineering approach to assist volunteers in remote malaria diagnosis.

Awards (1)

• First Award of $6,000 $6,000

Competition history

• ISEF 2026 Biomedical Engineering · Entry ENBM075T

Resources

• ISEF abstract page ↗

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