Review of a Decade of Research on Disordered Anderson Localizing Optical Fibers

Nearly a decade ago, transverse Anderson localization was observed for the first time in an optical fiber with a random transverse refractive index profile. This started the development of a whole new class of optical fibers that guide light, not in a conventional core-cladding setting based on total internal reflection, but utilizing Anderson localization, where light can guide at any location across the transverse profile of the fiber. These fibers have since been used successfully in high-quality endoscopic image transport. They also show interesting nonlinear and active (lasing) properties with promising applications. This review will cover a brief history of these fibers with personal accounts of the events that led to their development in our research groups. It will then follow with recent progress and future perspectives on science and applications of these fibers.

Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images

Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 × 32 to 512 × 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 × 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.

Possibility of new shielding device for upper gastrointestinal endoscopy

Background and study aims Infection control is essential when performing endoscopic procedures, especially during the COVID-19 pandemic. Therefore, we have developed a new shielding device called STEP for infection control in upper gastrointestinal endoscopy. Patients and methods STEP consists of a mask worn by the patient and a drape that is connected to the mask and covers the endoscope. A suction tube attached to the mask prevents aerosols from spreading. The endoscopist operates the endoscope through the drape. Three endoscopists performed a total of 18 examinations using an upper endoscopy training model with and without STEP. Endoscopic images were evaluated by three other endoscopists, using a visual analog scale. We also simulated contact, droplet, and aerosol infection and evaluated the utility of STEP. Results All examinations were conducted without a problem. Mean procedure time was 126.3 ± 11.6 seconds with STEP and 122.3 ± 10.0 seconds without STEP. The mean visual analog score was 90.7 ± 10.1 with STEP and 90.4 ± 10.0 without STEP. In the contact model, adherence of simulated contaminants was 4.9 ± 1.4 % without STEP and 0 % with STEP. In the droplet model, the number of simulated contaminants attached to the paper was 338 273 ± 90 735 pixels without STEP and 0 with STEP. In the aerosol model, the total number of particles was 346 837 ± 9485 without STEP and was significantly reduced to 222 ± 174 with STEP. Conclusions No effect on examination time or endoscopic image quality was observed when using STEP in upper gastrointestinal endoscopy. Using STEP reduced the diffusion of simulated contaminants in all three infection models.