Introduction

Early detection of neoplasia in Barrett’s Esophagus (BE) remains challenging. Although computer-aided detection (CADe) systems are increasingly available to support endoscopists, successful CADe implementation largely depends on the quality of endoscopic images, which can vary significantly in clinical practice. This study introduces an innovative computer-aided quality (CAQ) system designed for BE endoscopy, capable of assessing various image quality parameters, providing real-time feedback to the endoscopist, and operating in conjunction with an established CADe system.

Aims & Methods

The CAQ system was trained on 4,926 images from 243 BE patients to assess both objective (e.g., blurriness, illumination, and specular reflections) and subjective (i.e., mucosal cleanliness and esophageal expansion) image quality factors. The system is able to provide real-time feedback on these quality parameters to the endoscopist. Additionally, it is configured to function as a preprocessing filter for a previously developed CADe system, automatically excluding lower-quality images. To validate the CAQ system’s performance as a standalone tool, we constructed an endoscopic image quality test set, which included 647 images from 51 non-dysplastic BE patients across eight hospitals. These images were annotated for objective (e.g. motion blur, lighting) and subjective (i.e. esophageal expansion, mucosal cleanliness) image quality parameters by two experts. Additionally, to evaluate the CAQ system’s role as a preprocessing step for an established CADe system, we created the Barrett CADe test set, containing 956 frames from 62 neoplastic BE patients and 557 frames from 35 non-dysplastic BE patients from 12 Barrett referral centers. Outcome measures included agreement scores with experts for standalone image quality assessment and sensitivity, specificity, and Area-under-the-Curve (AUC) for BE neoplasia detection.

Results

As a stand-alone system, the CAQ system achieved Cohen’s Kappa scores for agreement with expert annotators of 0.73, 0.91, and 0.89 for objective image quality parameters, mucosal cleanliness, and esophageal expansion, respectively—comparable to inter-annotator scores of 0.73, 0.93, and 0.83. When used as a preprocessing filter for the CADe system, the CAQ system improved the CADe's sensitivity from 82% to 90% and the AUC from 87% to 91%, while specificity remained stable at 75% (Table 1).

Table 1. Performance of the CADe system for Barrett’s neoplasia detection with and without the CAQ system.

Conclusion

This study introduces the first CAQ system specifically aimed at automated quality control in BE endoscopy. As a standalone tool, the system effectively distinguishes poorly from well-visualized mucosa. When combined with an established CADe system, it enhances detection rates for neoplasia, highlighting its potential to improve diagnostic accuracy in BE surveillance.