Human selection bias drives the linear nature of the more ground truth effect in explainable deep learning optical coherence tomography image segmentation.

Supervised deep learning (DL) algorithms are highly dependent on training data for which human graders are assigned, for example, for optical coherence tomography (OCT) image annotation. Despite the tremendous success of DL, due to human judgment, these ground truth labels can be inaccurate and/or ambiguous and cause a human selection bias. We therefore investigated the impact of the size of the ground truth and variable numbers of graders on the predictive performance of the same DL architecture and repeated each experiment three times.

Cynomolgus monkey's retina volume reference database based on hybrid deep learning optical coherence tomography segmentation.

Cynomolgus monkeys (Macaca fascicularis) are commonly used in pre-clinical ocular studies. However, studies that report the morphological features of the macaque retina are based only on minimal sample sizes; therefore, little is known about the normal distribution and background variation. This study was conducted using optical coherence tomography (OCT) imaging to investigate the variations in retinal volumes of healthy cynomolgus monkeys and the effects of sex, origin, and eye side on the retinal volumes to establish a comprehensive reference database.

Iris Color Matters-A Contractility Analysis With Dynamic Volume-Rendered Optical Coherence Tomography Pupillometry.

Purpose: To analyze natural variability in pupillary contractility with dynamic volume-rendered optical coherence tomography (OCT) pupillometry regarding iris color, age, and sex in healthy Caucasian participants.

Methods: The intrapupillary spaces (IPSs) derived from anterior segment swept-source OCT of 71 healthy eyes were retrospectively analyzed. Baseline scotopic and photopic volumes and the functional parameters of pupillary ejection fraction (PEF), three-dimensional (3D) contractility, and relative light response (RLR) were measured on the swept-source OCT volumes.

Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.

Purpose: To evaluate the feasibility of automated segmentation of pigmented choroidal lesions (PCLs) in optical coherence tomography (OCT) data and compare the performance of different deep neural networks.

Methods: Swept-source OCT image volumes were annotated pixel-wise for PCLs and background. Three deep neural network architectures were applied to the data: the multi-dimensional gated recurrent units (MD-GRU), the V-Net, and the nnU-Net.

Graphene-Derived Carbon Support Boosts Proton Exchange Membrane Fuel Cell Catalyst Stability.

The lack of efficient and durable proton exchange membrane fuel cell electrocatalysts for the oxygen reduction reaction is still restraining the present hydrogen technology. Graphene-based carbon materials have emerged as a potential solution to replace the existing carbon black (CB) supports; however, their potential was never fully exploited as a commercial solution because of their more demanding properties. Here, a unique and industrially scalable synthesis of platinum-based electrocatalysts on graphene derivative (GD) supports is presented.

Toward the Continuous Production of Multigram Quantities of Highly Uniform Supported Metallic Nanoparticles and Their Application for Synthesis of Superior Intermetallic Pt-Alloy ORR Electrocatalysts.

A fast and facile pulse combustion (PC) method that allows for the continuous production of multigram quantities of high-metal-loaded and highly uniform supported metallic nanoparticles (SMNPs) is presented. Namely, various metal on carbon (M/C) composites have been prepared by using only three feedstock components: water, metal-salt, and the supporting material. The present approach can be elegantly utilized also for numerous other applications in electrocatalysis, heterogeneous catalysis, and sensors.

Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence.

Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization ('neural recording').

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip.

Purpose:  Sonographic diagnosis of developmental dysplasia of the hip allows treatment with a flexion-abduction orthosis preventing hip luxation. Accurate determination of alpha and beta angles according to Graf is crucial for correct diagnosis. It is unclear if algorithms could predict the angles.

Feasibility and Safety of a Coaxial Dual-Wavelength Optical Coherence Tomography Apparatus.

Purpose: To evaluate the feasibility and safety of a coaxial dual-wavelength optical coherence tomography (OCT) device (marked as Hydra-OCT).

Methods: Healthy participants without ocular pathology underwent retinal imaging using the Hydra-OCT allowing for simultaneous measurement of retinal scanning of 840 and 1,072 nm wavelength. Before and after measurement, best-corrected visual acuity and patients' comfort were assessed.

Validation of automated artificial intelligence segmentation of optical coherence tomography images.

Purpose: To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera.

Chlorine Dioxide-Pollutant Transformation and Formation of Hypochlorous Acid as a Secondary Oxidant.

Chlorine dioxide (ClO) has been used as a disinfectant in water treatment for a long time, and its use for micropollutant abatement in wastewater has recently been suggested. Surprisingly, a mechanistic understanding of ClO reactions in (waste)water matrices is largely lacking. The present study contributes to this mechanistic understanding by performing a detailed investigation of ClO reactions with organic matter using phenol as a surrogate for reactive phenolic moieties.

Comparison of contact radiographed and stained histological sections for osseointegration analysis of dental implants: an in vivo study.

Objectives: Histology is still regarded as the gold-standard to determine bone implant contact (BIC) as a parameter representing implant stability. As the further processing of cut slices for contact radiography (CR) to stained and polished histological sections is time consuming and error prone, our aim was to assess agreement between CR and Giemsa-eosin (GE) stained sections with regard to dental implants.

Study Design: Threaded dental titanium implants (n = 54) from the maxillae of Goettingen minipigs were evaluated.

Recent loss of self-incompatibility by degradation of the male component in allotetraploid Arabidopsis kamchatica.

The evolutionary transition from outcrossing to self-fertilization (selfing) through the loss of self-incompatibility (SI) is one of the most prevalent events in flowering plants, and its genetic basis has been a major focus in evolutionary biology. In the Brassicaceae, the SI system consists of male and female specificity genes at the S-locus and of genes involved in the female downstream signaling pathway. During recent decades, much attention has been paid in particular to clarifying the genes responsible for the loss of SI.