Vision training

PURPOSE: Damage to the adult primary visual cortex (V1) causes vision loss in the contralateral hemifield, initiating a process of transsynaptic retrograde degeneration (TRD). Here, we examined retinal correlates of TRD using a new metric to account for global changes in inner retinal thickness and asked if perceptual training in the intact or blind field impacts its progression. METHODS: We performed a meta-analysis of optical coherence tomography data in 48 participants with unilateral V1 stroke and homonymous visual defects who completed clinical trial NCT03350919...

Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size...

BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. PURPOSE: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI...

BACKGROUND: Neuro-ophthalmology, bridging neurology and ophthalmology, highlights the nervous system's crucial role in vision, encompassing afferent and efferent pathways. The evolution of this field has emphasized the importance of neuroanatomy for precise surgical interventions, presenting educational challenges in blending complex anatomical knowledge with surgical skills. This review examines the interplay between neuroanatomy and surgical practices in neuro-ophthalmology, aiming to identify educational gaps and suggest improvements...

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. METHODS: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes...

We use data produced by industrial wood grading machines to train a machine learning model for predicting strength-related properties of wood lamellae from colour images of their surfaces. The focus was on samples of Norway spruce ( Picea abies ) wood, which display visible fibre pattern formations on their surfaces. We used a pre-trained machine learning model based on the residual network ResNet50 that we trained with over 15 000 high-definition images labelled with the indicating properties measured by the grading machine...

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer...

PURPOSE: The primary objective of this study was to establish a benchmark by collecting baseline data on surgical education in obstetrics and gynecology in Germany, including factual number of operations performed. MATERIALS AND METHODS: A nationwide anonymous survey was conducted in Germany between January 2019 and July 2019 utilizing a specially designed questionnaire which addressed both residents and senior trainers. RESULTS: A total of 601 participants completed the survey, comprising 305 trainees and 296 trainers...

Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume...

PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. METHODS: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported...

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability...

Background: In the popular fighting sport of boxing, opponents strike each other above the belt line in the face, chest, and belly. The physical parts most exposed are therefore the nose and eyes. In amateur boxing, fights go only three rounds - three minutes for men and one minute for women - with a one-minute break in between. They wear gloves, but the head protection used in the men's game has been removed by AIBA due to the high likelihood of concussion when using head protection. Because chronic ocular changes may take longer than the expected short-term effects, this study included at least 3 years of competitive sports participation...

BACKGROUND: In the end stage of kidney disease, abnormal levels of blood calcium, phosphorus, and parathyroid hormone lead to bone metabolism disorders, manifesting as osteoporosis or fibrocystic osteoarthritis. X-ray, CT, and MR are useful for detecting bone lesions in dialysis patients, but currently, computer vision has not yet been used for this purpose. METHODS: ResNet is a powerful deep CNN model, which has not yet been used to distinguish between the bones of dialysis patients and healthy people...

BACKGROUND: Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM...

BACKGROUND: Good postural stability control is dependent upon the complex integration of incoming sensory information (visual, somatosensory, vestibular) with neuromotor responses that are constructed in advance of a voluntary action or in response to an unexpected perturbation. AIMS: To examine whether differences exist in how sensory inputs are used to control standing balance in children with and without Prader-Willi syndrome (PWS). METHODS AND PROCEDURES: In this cross-sectional study, 18 children with PWS and 51 children categorized as obese but without PWS (without PWS) ages 8-11 completed the Sensory Organization Test®...

Blessed by vast amounts of data, learning-based methods have achieved remarkable performance in countless tasks in computer vision and medical image analysis. Although these deep models can simulate highly nonlinear mapping functions, they are not robust with regard to the domain shift of input data. This is a significant concern that impedes the large-scale deployment of deep models in medical images since they have inherent variation in data distribution due to the lack of imaging standardization. Therefore, researchers have explored many domain generalization (DG) methods to alleviate this problem...

Although direct behavioural observations are widely used, they are time-consuming, prone to error, require knowledge of the observed species, and depend on intra/inter-observer consistency. As a result, they pose challenges to the reliability and repeatability of studies. Automated video analysis is becoming popular for behavioural observations. Sleep is a biological metric that has the potential to become a reliable broad-spectrum metric that can indicate the quality of life and understanding sleep patterns can contribute to identifying and addressing potential welfare concerns, such as stress, discomfort, or health issues, thus promoting the overall welfare of animals; however, due to the laborious process of quantifying sleep patterns, it has been overlooked in animal welfare research...

A teaching strategy using 3D-printed models of the canine upper digestive tract (UDT) for anatomy demonstration and upper endoscopy instruction was evaluated. The canine UDT (esophagus-stomach-duodenum) was scanned and 3D-printed molds were manufactured using silicone casting. First-year students were introduced to these 3D models in practical sessions alongside real specimens. Simultaneously, fifth-year students were trained in endoscope handling and anatomical recognition using 3D specimens. Both groups completed an anonymous survey...

With declining populations in the wild, captive rescue and breeding have become one of the most important ways to protect pangolins from extinction. At present, the success rate of artificial breeding is low, due to the insufficient understanding of the breeding behavior characteristics of pangolins. The automatic recognition method based on machine vision not only monitors for 24 h but also reduces the stress response of pangolins. This paper aimed to establish a temporal relation and attention mechanism network (Pangolin breeding attention and transfer network, PBATn) to monitor and recognize pangolin behaviors, including breeding and daily behavior...

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18-59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous)...