BACKGROUND: Recent studies have shown that narrow-band imaging (NBI) is a powerful diagnostic tool for the differentiation between neoplastic and non-neoplastic colorectal polyps. OBJECTIVE: To develop a computer-based method for classification of colorectal polyps. DESIGN: A prospective study. SETTING: University hospital. PATIENTS: A total of 214 patients with colorectal polyps who underwent a zoom NBI colonoscopy. INTERVENTIONS: A total of 434 detected polyps 10 mm or smaller were imaged and subsequently removed for histological analysis. MAIN OUTCOME MEASUREMENTS: Diagnostic performance in polyp classification by 2 experts, 2 nonexperts, and a computer-based algorithm. RESULTS: The expert group and the computer-based algorithm achieved a comparable diagnostic performance (expert group: 93.4% sensitivity, 91.8% specificity, and 92.7% accuracy; computer-based algorithm: 95.0% sensitivity, 90.3% specificity, and 93.1% accuracy) and were both significantly superior to the nonexpert group (86.0% sensitivity, 87.8% specificity, and 86.8% accuracy) in terms of sensitivity, negative predictive value, and accuracy. Subgroup analysis of 255 polyps 5 mm or smaller revealed comparable results without significant differences in the overall analysis of all polyps. LIMITATIONS: No fully automatic classification system. CONCLUSIONS: The study demonstrates that computer-based classification of colon polyps can be achieved with high diagnostic performance.
BACKGROUND: Recent studies have shown that narrow-band imaging (NBI) is a powerful diagnostic tool for the differentiation between neoplastic and non-neoplastic colorectal polyps. OBJECTIVE: To develop a computer-based method for classification of colorectal polyps. DESIGN: A prospective study. SETTING: University hospital. PATIENTS: A total of 214 patients with colorectal polyps who underwent a zoom NBI colonoscopy. INTERVENTIONS: A total of 434 detected polyps 10 mm or smaller were imaged and subsequently removed for histological analysis. MAIN OUTCOME MEASUREMENTS: Diagnostic performance in polyp classification by 2 experts, 2 nonexperts, and a computer-based algorithm. RESULTS: The expert group and the computer-based algorithm achieved a comparable diagnostic performance (expert group: 93.4% sensitivity, 91.8% specificity, and 92.7% accuracy; computer-based algorithm: 95.0% sensitivity, 90.3% specificity, and 93.1% accuracy) and were both significantly superior to the nonexpert group (86.0% sensitivity, 87.8% specificity, and 86.8% accuracy) in terms of sensitivity, negative predictive value, and accuracy. Subgroup analysis of 255 polyps 5 mm or smaller revealed comparable results without significant differences in the overall analysis of all polyps. LIMITATIONS: No fully automatic classification system. CONCLUSIONS: The study demonstrates that computer-based classification of colon polyps can be achieved with high diagnostic performance.