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Related Experiment Video

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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Identifying key mechanisms leading to visual recognition errors for missed colorectal polyps using eye-tracking

Omer F Ahmad1,2,3, Evangelos Mazomenos1, Francois Chadebecq1

  • 1Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.

Journal of Gastroenterology and Hepatology
|January 18, 2023
PubMed
Summary

Cognitive errors, not seeing polyps, are the main cause of missed colorectal cancers. Artificial intelligence shows promise in improving polyp detection compared to medical trainees.

Keywords:
artificial intelligencecolonic polypscolonoscopycolorectal cancer

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Human Perception

Background:

  • Interval colorectal cancers are often caused by missed polyp detection.
  • Mechanisms behind perceptual variations in polyp recognition are poorly understood.
  • Subtle and advanced colorectal neoplasia pose significant diagnostic challenges.

Purpose of the Study:

  • To evaluate visual recognition errors in colorectal polyp detection.
  • To investigate the underlying mechanisms of these perceptual errors.
  • To offer novel mechanistic insights into polyp recognition failures.

Main Methods:

  • Eye-tracking technology was used to assess gaze and cognitive errors in 11 participants viewing 25 polyps.
  • Cognitive errors were defined as observed lesions not recognized as polyps.
  • Polyp recognition performance was compared between human participants and a convolutional neural network using 39 subtle polyps.

Main Results:

  • Cognitive errors (65.6%) were more frequent than gaze errors, particularly among trainees.
  • The convolutional neural network achieved significantly higher polyp detection sensitivity (79.5%) than trainees (30.0%) and medical students (15.4%).

Conclusions:

  • Cognitive errors represent the primary cause of visual recognition failures in polyp detection.
  • The role of artificial intelligence in improving polyp recognition and its impact on learning curves requires further study.
  • A new, publicly accessible colonoscopy polyp perception database has been established to aid future research.