Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?

Table 3 Deep learning applications in wireless capsule endoscopy for improving the reading efficiency of wireless capsule endoscopy

Ref.	Experiment type	Scope of WCE reading /device	Conventional reading	Deep learning assisted reading	P value
Aoki et al[39], 2019, Japan	Retrospective study using anonymized data	SB section only/Pillcam SB3	mean reading time (min): Trainee: 20.7; Expert: 12.2	mean reading time (min): Trainee: 5.2; Expert: 3.1	< 0.001
			Overall lesion detection rate: Trainee: 47%; Expert: 84%	Overall lesion detection rate: Trainee: 55%; Expert: 87%	NS
Ding et al[20], 2019, China	Retrospective study by randomly selected videos	Small bowel abnormalities/SB-CE by Ankon Technologies	mean reading time ± standard deviation (min): 96.6 ± 22.53	mean reading time ± standard deviation (min): 5.9 ± 2.23	< 0.001
			Overall lesion detection rate: 41.43%	Overall lesion detection rate: 47.00%	NA1

¹In per-patient analysis, deep learning assisted physician significantly outperformed conventional reading in detecting lymphangiectasia, lymphatic follicular, hyperplasia, inflammation, protruding lesion, and polyps. CE: Capsule endoscopy; NA: Not available; NS: Not significant; SB: Small bowel.