Investigating the Impact of Image Quality on Endoscopic AI Model Performance

Virtually all endoscopic AI models are developed with clean, high-quality imagery from expert centers, however, the clinical data quality is much more heterogeneous. Endoscopic image quality can degrade by e.g. poor lighting, motion blur, and image compression. This disparity between training, validation data, and real-world clinical practice can have a substantial impact on the performance of deep neural networks (DNNs), potentially resulting in clinically unreliable models. To address this issue and develop more reliable models for automated cancer detection, this study focuses on identifying the limitations of current DNNs. Specifically, we evaluate the performance of these models under clinically relevant and realistic image corruptions, as well as on a manually selected dataset that includes images with lower subjective quality. Our findings highlight the importance of understanding the impact of a decrease in image quality and the need to include robustness evaluation for DNNs used in endoscopy.