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'). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications

Challenging a Myth and Misconception: Red-Light Vision in Rats

Due to the lack of L-cones in the rodent retina, it is generally assumed that red light is invisible to rodents. Thus, red lights and red filter foils are widely used in rodent husbandry and experimentation allowing researchers to observe animals in an environment that is thought to appear dark to the animals. To better understand red-light vision in rodents, we assessed retinal sensitivity of pigmented and albino rats to far-red light by electroretinogram. We examined the sensitivity to red light not only on the light- but also dark-adapted retina, as red observation lights in husbandry are used during the dark phase of the light cycle. Intriguingly, both rods and cones of pigmented as well as albino rats show a retinal response to red light, with a high sensitivity of the dark-adapted retina and large electroretinogram responses in the mesopic range. Our results challenge the misconception of rodents being red-light blind. Researchers and housing facilities should rethink the use of red observation lights at night

Macular thickness measurements of healthy, naïve cynomolgus monkeys assessed with spectral-domain optical coherence tomography (SD-OCT).

The purpose of this study was to measure central macular thickness in an unprecedented number of cynomolgus monkeys. Macular thickness was measured with Heidelberg spectral-domain OCT in 320 eyes of healthy and treatment-naïve cynomolgus monkeys (80 males and 80 females). The macula was successfully measured in all 320 eyes. Macular thickness was not significantly different between the sexes. The mean central macular thickness was 244 μm (+/- 21 μm). Macular thicknesses in the quadrants were 327 +/-17 μm (temporal inner), 339 +/- 17 μm (inferior inner), 341 +/- 14 μm (superior inner), 341 +/-18 μm (nasal inner), and 299 +/- 20 μm (temporal outer), 320 +/- 16 μm (superior outer), 332 +/-23 μm (inferior outer), and 337 +/-18 μm (nasal outer). Highly significant differences between the nasal and temporal quadrants were detected. This study successfully demonstrated the feasibility of retinal thickness measurements in healthy cynomolgus monkeys. The present findings indicate that the macula is thicker in cynomolgus monkeys than in humans and provide important normative data for future studies

CCDC 2155478: Experimental Crystal Structure Determination

Related Article: Robin Heckershoff, Tobias Schnitzer, Tim Diederich, Lukas Eberle, Petra Krämer, Frank Rominger, Matthias Rudolph, A. Stephen K. Hashmi|2022|J.Am.Chem.Soc.|144|8306|doi:10.1021/jacs.2c0239

CCDC 2155479: Experimental Crystal Structure Determination

Related Article: Robin Heckershoff, Tobias Schnitzer, Tim Diederich, Lukas Eberle, Petra Krämer, Frank Rominger, Matthias Rudolph, A. Stephen K. Hashmi|2022|J.Am.Chem.Soc.|144|8306|doi:10.1021/jacs.2c0239