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

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.

Field-Induced Wettability Gradients for No-Loss Transport of Oil Droplets on Slippery Surfaces.

Transporting oil droplets is crucial for a wide range of industrial and biomedical applications but remains highly challenging due to the large contact angle hysteresis on most solid surfaces. A liquid-infused slippery surface has a low hysteresis contact angle and is a highly promising platform if sufficient wettability gradient can be created. Current strategies used to create wettability gradient typically rely on the engineering of the chemical composition or geometrical structure. However, these strategies are inefficient on a slippery surface because the infused liquid tends to conceal the gradient in the chemical composition and small-scale geometrical structure. Magnifying the structure, on the other hand, will significantly distort the surface topography, which is unwanted in practice. In this study, we address this challenge by introducing a field-induced wettability gradient on a flat slippery surface. By printing radial electrodes array, we can pattern the electric field, which induces gradient contact angles. Theoretical analysis and experimental results reveal that the droplet transport behavior can be captured by a nondimensional electric Bond number. Our surface enables no-loss transport of various types of droplets, which we expect to find important applications such as heat transfer, anticontamination, microfluidics, and biochemical analysis.