Detection and differentiation of esophageal squamous neoplasia (ESN) are of value in improving patient outcomes. Probe-based confocal laser endomicroscopy (pCLE) can diagnose ESN accurately.However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
Study Type
OBSERVATIONAL
Enrollment
57
Suspected esophageal mucosal lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI. After a washout period, nonexpert endoscopists take the second assessment with AI assistance.
Qilu Hospital, Shandong University
Jinan, Shandong, China
The diagnosis efficiency of Artificial Intelligence
The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing esophageal mucosal disease on real-time pCLE examination.
Time frame: 3 years
Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing esophageal mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.
Time frame: 1 month
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