Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

Abstract

BACKGROUND Artificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice. AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies. METHODS With high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital. RESULTS The CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time ( P = 0.32; P = 0.16, respectively). CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.

Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies

“…Some retrospective studies suggest that FP activations result in the negligible increase of the total withdrawal time, as most of them are immediately discarded by the endoscopists [10, 11]. Other studies using for example eye-tracking glasses suggest that CADe and FPs activations might have an impact on the visualization pattern of the endoscopists [8, 22]. Therefore, further studies using eye tracking technology during endoscopic examinations in a prospective manner should be performed in order to analyze the influence of short FP activations on the examiner and the withdrawal time.…”

Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy

“…Shichijo et al (16) constructed a convolutional neural network (CNN) (17,18) and evaluated its ability to diagnose Helicobacter pylori infection, and the accuracy and sensitivity were 87.7% and 88.9%, respectively. Zhao et al (19) developed a DL-based assisted diagnostic system for the localization of colon polyps, with a sensitivity of 98.4% in prospective validation. AI also showed excellent ability in CAG diagnosis.…”

“…Zhao et al. ( 19 ) developed a DL-based assisted diagnostic system for the localization of colon polyps, with a sensitivity of 98.4% in prospective validation. AI also showed excellent ability in CAG diagnosis.…”

Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore

“…Artificial intelligence (AI) aims to aid the endoscopists to reduce the complexity of colonoscopy. AI, first mentioned in the 1950s, has greatly improve after the turn of the millennium, especially after the incorporation of deep learning algorithms [ 11 – 19 ]. AI has increasingly been incorporated into various aspect of healthcare.…”