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Development of optometry in western countries was studied on a viewpoint of the history of science. It was revealed that optometry had been formed on the basis of optics, a branch of physics, to which biomedical study was added. Optometry can be defined as a pioneering interdisciplinary field of study and as biomedical physics of the 19th century. It is pointed out that population-geographic factors have affected on the development of optometry in the U. S. A. and Australia. European development model is suggested for a strategy for the next-generation development of Korean optometry, rather than American model.
Wave front sensing of the surface of equal phase for a propagating electromagnetic wave is a vital technology in fields ranging from real time adaptive optics, to high accuracy metrology, to medical optometry. We have developed a new method of wavefront sensing that makes a direct measurement of the electromagnetic phase distribution, or path-length delay, across an optical wavefront. The method is based on techniques developed in radio astronomical interferometric imaging. The method employs optical interferometry using a 2-D aperture mask, a Fourier transform of the interferogram to derive interferometric visibilities, and self-calibration of the complex visibilities to derive the voltage amplitude and phase gains at each hole in the mask, corresponding to corrections for non-uniform illumination and wavefront distortions across the aperture, respectively. The derived self-calibration gain phases are linearly proportional to the electromagnetic path-length distribution to each hole in the aperture mask, relative to the path-length to the reference hole, and hence represent a wavefront sensor with a precision of a small fraction of a wavelength. The method was tested at $λ=400\,$n
Accurate diagnosis of ocular surface diseases is critical in optometry and ophthalmology, which hinge on integrating clinical data sources (e.g., meibography imaging and clinical metadata). Traditional human assessments lack precision in quantifying clinical observations, while current machine-based methods often treat diagnoses as multi-class classification problems, limiting the diagnoses to a predefined closed-set of curated answers without reasoning the clinical relevance of each variable to the diagnosis. To tackle these challenges, we introduce an innovative multi-modal diagnostic pipeline (MDPipe) by employing large language models (LLMs) for ocular surface disease diagnosis. We first employ a visual translator to interpret meibography images by converting them into quantifiable morphology data, facilitating their integration with clinical metadata and enabling the communication of nuanced medical insight to LLMs. To further advance this communication, we introduce a LLM-based summarizer to contextualize the insight from the combined morphology and clinical metadata, and generate clinical report summaries. Finally, we refine the LLMs' reasoning ability with domain-specific i
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography (OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We used data from 977 patients who experienced CVD within a 5-year interval post-image acquisition, alongside 1,877 control participants without CVD, totaling 2,854 subjects. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not. Our model, trained on both imaging modalities, achieved promising results (AUROC 0.78 +/- 0.02, accuracy 0.68 +/- 0.002, precision 0.74 +/- 0.02, sensitivity 0.73 +/- 0.02, and specificity 0.68 +/- 0.01), demonstrating its efficacy in identifying patients at risk of future CVD events based on their retinal images. This study highlights the potential of retinal OCT imaging and fundus ph
Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical coherence tomography (OCT) has gained recognition as a potential tool for early CVD risk prediction, though its use remains underexplored. In this study, we investigated the potential of OCT as an additional imaging technique to predict future CVD events. We analysed retinal OCT data from the UK Biobank. The dataset included 612 patients who suffered a myocardial infarction (MI) or stroke within five years of imaging and 2,234 controls without CVD (total: 2,846 participants). A self-supervised deep learning approach based on Variational Autoencoders (VAE) was used to extract low-dimensional latent representations from high-dimensional 3D OCT images, capturing distinct features of retinal layers. These latent features, along with clinical data, were used to train a Random Forest (RF) classifier to differentiate between patients at risk of future CVD events (MI or stroke) and healthy controls. Our model achieved an AUC of 0.75, sensitivity of 0.70, specificity of 0.70, and accuracy of 0.70, outperf
The global cobalt supply chain is more interconnected—and more vulnerable—than previously thought, with disruptions capable of triggering far-reaching cascades across multiple countries and industries。 Researchers warn that protecting battery supply chains will require system-wide coordination because critical bottlenecks can turn local shocks into
A new sunlight-powered material can convert visible light into higher-energy UV light, overcoming a challenge that has frustrated scientists for years。 The breakthrough could enable cleaner air purification, solar-driven chemistry, and advanced manufacturing technologies using nothing more than natural sunlight
The race to build data centers in space is gaining momentum as AI drives unprecedented demand for computing power。 Orbital facilities could tap into abundant solar energy and avoid many of the environmental challenges faced on Earth。 Yet space remains a harsh and expensive place to operate, with major hurdles including cooling, maintenance, radiati
Physicists have solved a long-standing problem involving systems that appear to violate Newton’s third law, such as bird flocks and bacterial swarms。 By adding carefully designed “imaginary partners” to their models, they can now simulate these complex systems with unprecedented accuracy
Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78\% and a Dice coefficient of 94.73\% for a disc segmentation from a retinal image in 0.03 seconds. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources.
Researchers found that a Chinese sodium-ion battery performs far better than expected, with production quality and design features comparable to Tesla’s batteries。 If engineers can improve cold-weather charging and energy density, sodium could become a cheaper and more abundant alternative to lithium for EVs and large-scale energy storage
Scientists have uncovered a new explanation for what powers Yellowstone and other supervolcanoes。 Instead of a deep plume rising from near Earth’s core, a broad “mantle wind” may push hot rock beneath Yellowstone, generating magma closer to the surface。 This process helps create a massive underground magma network and may explain how supervolcanoes
A new AI-powered framework could transform how astronomers measure the expansion of the Universe。 By analyzing images of Type Ia supernovae and modeling their environments in unprecedented detail, researchers can estimate cosmic distances with near-spectroscopic accuracy。 The technique is designed for the flood of data expected from the upcoming Ve
A newly proposed quantum sensing technique could make it much easier to identify one of physics’ newest and most intriguing classes of magnets: altermagnets。 These unusual materials, discovered only a few years ago, appear to combine the speed and efficiency of antiferromagnets with some of the useful electronic properties of traditional magnets, m