In Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVE : Convolutional neural networks (CNN) for computer-aided diagnosis (CADx) of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSL) often excluded. This study developed a CNN from videos to classify polyps as adenomatous or non-adenomatous using standard narrow-band imaging (NBI) and NBI-near focus (NBI-NF) and created a publicly accessible polyp video database.
METHODS : We trained a CNN with 16,832 high and moderate-quality frames from 229 polyp videos (56 SSLs). It was evaluated with 222 polyp videos (36 SSLs) across two test-sets. Test-set I consists of 14,320 frames (157 polyps, 111 diminutive). Test-set II, which is publicly accessible, 3,317 video frames (65 polyps, 41 diminutive) which was benchmarked with three expert and three non-expert endoscopists.
RESULTS : Sensitivity for adenoma characterisation was 91.6% in test-set I and 89.7% in test-set II. Specificity was 91.9% and 88.5%. Sensitivity for diminutive polyps was 89.9% and 87.5%; specificity 90.5% and 88.2%. In NBI-NF, sensitivity was 89.4% and 89.5%, with a specificity of 94.7% and 83.3%. In NBI, sensitivity was 85.3 % and 91.7%, with a specificity of 87.5% and 90.0%, respectively. The CNN achieved PIVI-1 and PIVI-2 thresholds for each test-set. In the benchmarking of test-set II, the CNN was significantly more accurate than non-experts (13.8% difference (95%CI 3.2-23.6), p=0.01) with no significant difference with experts.
CONCLUSIONS : A single CNN can differentiate adenomas from SSL and hyperplastic polyps in both NBI and NBI-NF. A publicly accessible NBI polyp video database was created and benchmarked.
Kader Rawen, Cid-Mejias Anton, Brandao Patrick, Islam Shahraz, Hebbar Sanjith, González-Bueno Puyal Juana, Ahmad Omer F, Hussein Mohamed, Toth Daniel, Mountney Peter, Seward Ed, Vega Roser, Stoyanov Danail, Lovat Laurence B
2022-Dec-17
Artificial intelligence, colonic polyps, colonoscopy, colorectal neoplasms, deep learning
