Background and study aims: Early neoplasia in Barrett’s esophagus is difficult to detect and often overlooked
during Barrett’s surveillance. An automatic detection system could be beneficial,
by assisting endoscopists with detection of early neoplastic lesions. The aim of this
study was to assess the feasibility of a computer system to detect early neoplasia
in Barrett’s esophagus.
Patients and methods: Based on 100 images from 44 patients with Barrett’s esophagus, a computer algorithm,
which employed specific texture, color filters, and machine learning, was developed
for the detection of early neoplastic lesions in Barrett’s esophagus. The evaluation
by one endoscopist, who extensively imaged and endoscopically removed all early neoplastic
lesions and was not blinded to the histological outcome, was considered the gold standard.
For external validation, four international experts in Barrett’s neoplasia, who were
blinded to the pathology results, reviewed all images.
Results: The system identified early neoplastic lesions on a per-image analysis with a sensitivity
and specificity of 0.83. At the patient level, the system achieved a sensitivity and
specificity of 0.86 and 0.87, respectively. A trade-off between the two performance
metrics could be made by varying the percentage of training samples that showed neoplastic
tissue.
Conclusion: The automated computer algorithm developed in this study was able to identify early
neoplastic lesions with reasonable accuracy, suggesting that automated detection of
early neoplasia in Barrett’s esophagus is feasible. Further research is required to
improve the accuracy of the system and prepare it for real-time operation, before
it can be applied in clinical practice.
