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Related Experiment Video

Updated: Sep 13, 2025

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
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Human Visual Transduction Mechanism-Inspired Adaptive Enhancement Network during Endoscopy.

Xinzhen Ren, Wenju Zhou, Maoyu Jin

    IEEE Journal of Biomedical and Health Informatics
    |August 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    An adaptive enhancement network (AEN) improves disposable endoscope image quality by recovering lesion details. This AI-powered tool enhances diagnostic accuracy and visual clarity for better adenoma detection during endoscopy.

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    Area of Science:

    • Medical imaging
    • Artificial intelligence
    • Endoscopy

    Background:

    • Disposable endoscopes reduce cross-contamination but have lower resolution due to smaller sensors.
    • Lower resolution in disposable endoscopes degrades diagnostic performance for both subjective and automated analysis.
    • Improved adenoma detection rates are linked to clearer endoscopic imaging.

    Purpose of the Study:

    • To propose an adaptive enhancement network (AEN) to enhance lesion details in disposable endoscope images.
    • To improve the diagnostic performance of disposable endoscopes by recovering image resolution and clarity.
    • To leverage artificial intelligence inspired by human visual mechanisms for enhanced medical imaging.

    Main Methods:

    • Developed an adaptive enhancement network (AEN) mimicking the human visual pathway.
    • Incorporated rod cell modules (RCM) for shape-sensitive segmentation and cone cell modules (CCM) for detail-oriented enhancement.
    • Utilized a framework analogous to photoreceptor layers, outer plexiform layer, inner nuclear layer, inner plexiform layer, and ganglion cell layer.

    Main Results:

    • The AEN effectively enhanced lesion regions, producing sharper edges and finer textures with reduced blur and artifacts.
    • Experimental results demonstrated significant improvements in image quality for lesion detection.
    • Comprehensive evaluations across four datasets confirmed the generalizability and effectiveness of the AEN.

    Conclusions:

    • The AEN successfully recovers details in low-resolution disposable endoscope images, aiding endoscopists.
    • The proposed AI network offers real-time processing advantages for endoscopic diagnostics.
    • This technology has the potential to improve diagnostic accuracy and patient outcomes in procedures using disposable endoscopes.