Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies.

Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (±1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (±2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance loss.

Liraglutide decreases energy expenditure and does not affect the fat fraction of supraclavicular brown adipose tissue in patients with type 2 diabetes.

BACKGROUND AND AIMS: Several studies have shown that glucagon-like peptide-1 (GLP-1) analogues can affect resting energy expenditure, and preclinical studies suggest that they may activate brown adipose tissue (BAT). The aim of the present study was to investigate the effect of treatment with liraglutide on energy metabolism and BAT fat fraction in patients with type 2 diabetes. METHODS AND RESULTS: In a 26-week double-blind, placebo-controlled trial, 50 patients with type 2 diabetes were randomized to treatment with liraglutide (1.8 mg/day) or placebo added to standard care. At baseline and after treatment for 4, 12 and 26 weeks, we assessed resting energy expenditure (REE) by indirect calorimetry. Furthermore, at baseline and after 26 weeks, we determined the fat fraction in the supraclavicular BAT depot using chemical-shift water-fat MRI at 3T. Liraglutide reduced REE after 4 weeks, which persisted after 12 weeks and tended to be present after 26 weeks (week 26 vs baseline: liraglutide -52 ± 128 kcal/day; P = 0.071, placebo +44 ± 144 kcal/day; P = 0.153, between group P = 0.057). Treatment with liraglutide for 26 weeks did not decrease the fat fraction in supraclavicular BAT (-0.4 ± 1.7%; P = 0.447) compared to placebo (-0.4 ± 1.4%; P = 0.420; between group P = 0.911). CONCLUSION: Treatment with liraglutide decreases REE in the first 12 weeks and tends to decrease this after 26 weeks without affecting the fat fraction in the supraclavicular BAT depot. These findings suggest reduction in energy intake rather than an increase in REE to contribute to the liraglutide-induced weight loss. TRIAL REGISTRY NUMBER: NCT01761318.