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iWaste: Video-Based Medical Waste Detection and Classification.

Junbo Chen, Jeffrey Mao, Cassandra Thiel

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    A new system, iWASTE, uses video analysis to automatically detect and classify medical waste in operating rooms. This technology offers a safer, more efficient alternative to manual waste auditing for improved waste reduction strategies.

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    Area of Science:

    • Computer Science
    • Medical Engineering
    • Environmental Science

    Background:

    • Manual medical waste auditing in operating rooms is time-consuming and poses risks.
    • Effective waste auditing is crucial for reducing medical waste in resource-intensive environments.

    Purpose of the Study:

    • To develop an automated system (iWASTE) for detecting and classifying medical waste using video data.
    • To replace manual waste auditing with a safer and more efficient method.

    Main Methods:

    • A video dataset of four common waste items (gloves, hairnet, mask, shoecover) was collected.
    • A motion detection preprocessing method was used to extract relevant video frames.
    • A novel R3D+C2D architecture combining 2D and 3D convolutional neural networks was proposed for waste classification.

    Main Results:

    • The iWASTE system achieved 79.99% accuracy in classifying medical waste on a challenging dataset.
    • The R3D+C2D model effectively combined features from 2D and 3D CNNs for improved classification.

    Conclusions:

    • The iWASTE system provides consistent, real-time monitoring of solid waste generation in operating rooms.
    • This technology can support the enforcement of medical waste sorting policies and inform waste reduction strategies.