AI-Powered Urinalysis for Automated Sediment Detection
Automated, accurate, and efficient urinalysis with CNN-based embedded vision system
Urine sediment examination plays an important role in diagnosing kidney and urinary tract disorders. However, manual microscopy is slow, dependent on user skill, and often inconsistent. Laboratories require higher throughput, stable accuracy, and predictable costs without adding complexity to daily workflows.
Compact embedded vision for real-time sediment classification
Basler provides an embedded vision solution that combines a Basler dart camera module, an NXP i.MX 8M Plus embedded board with integrated NPU, and pylon AI software tools for automated urine sediment classification. The system captures high-quality images of the sample, performs image preprocessing, and uses a trained convolutional neural network (CNN) to detect and classify relevant structures such as red and white blood cells, bacteria, casts, and crystals. The classification results are transferred to the host application for reporting and review.
How it works
Image capture and preprocessing
The Basler dart camera acquires detailed images of the sample and performs preprocessing tasks such as debayering, denoising, and scaling to ensure consistent data quality.
Detection and classification with CNN
The trained CNN model runs directly on the NPU of the i.MX 8M Plus to identify red and white blood cells, bacteria, casts, and crystals in real time.
Visualization and output
The pylon Viewer displays bounding boxes and class labels, and the results are transferred to the laboratory information system (LIS) via standard interfaces for documentation and review.
Reliable AI results through consistent image data
Accurate classification depends on stable and repeatable imaging results. The combination of the Basler dart camera, embedded processing, and pylon AI modules ensures consistent data quality and reliable AI inference. This integrated approach enables laboratories and device manufacturers to achieve high throughput, consistent accuracy, and compact system integration while reducing total system cost through an embedded hardware-software platform.
Automated, accurate, and efficient urinalysis with embedded CNN vision
Urine sediment examination plays an important role in diagnosing kidney and urinary tract disorders. However, manual microscopy is slow, dependent on user skill, and often inconsistent. Laboratories require higher throughput, stable accuracy, and predictable costs—without adding complexity to daily workflows.
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