Background: Cranial cruciate ligament (CCL) rupture is the most common orthopedic pathology in dog and in men. In human, optical computer-assisted technique is considered as a repeatable and reliable method for the biomechanical assessment of joint kinematics and laxity in case of CCL surgery.
Aim: To evaluate the repeatability and reliability afforded by clinical tests in terms of laxity measured by means of a computer-assisted tracking system in two canine CCL conditions: CCL-Intact, CCL-Deficient.
Methods: Fourteen fresh frozen canine stifles were passively subjected to Internal/External (IE) rotation at 120° of flexion and Cranial drawer test (CC). To quantify the repeatability and the reliability, intra-class correlation coefficient (ICC) and the mean percent error were evaluated (Δ %).
Results: The study showed a very good intra-class correlation, before and after CCL resection for kinematics tests. It was found a minimum ICC = 0.73 during the IE rotation in CCL-Intact and a maximum value of ICC = 0.97 for the CC displacement in CC-Deficient. IE rotation with CCL-Intact is the condition with the greatest Δ % = 14%, while the lowest Δ % = 6% was obtained for CC displacement in CCL-Deficient.
Conclusion: The presented work underlined the possibility of using a computer-assisted method also for biomechanical studies concerning stifle kinematics and laxity.
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http://dx.doi.org/10.4314/ovj.v10i1.14 | DOI Listing |
PLoS One
January 2025
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Optical Coherence Tomography (OCT) offers high-resolution images of the eye's fundus. This enables thorough analysis of retinal health by doctors, providing a solid basis for diagnosis and treatment. With the development of deep learning, deep learning-based methods are becoming more popular for fundus OCT image segmentation.
View Article and Find Full Text PDFNat Commun
January 2025
Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland, USA.
Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations.
View Article and Find Full Text PDFSci Adv
January 2025
Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China.
Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the complexity, size, and cost of microscopic systems, making them less suitable for multifluorescence channel, high-speed imaging. Here, we present filter-free fluorescence microscopic imaging enabled with deep learning-based digital spectral filtering.
View Article and Find Full Text PDFPLoS One
December 2024
National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China.
Landmark detection is a common task that benefits downstream computer vision tasks. Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks. Although a well-trained encoder can effectively capture landmark information through image reconstruction, it overlooks the semantic relationships between landmarks.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, 134 East Street, Fuzhou, 350000, People's Republic of China.
More attention should be paid to the deviations between the actual diameter of the rhexis reference circle which projected by the Image-guided systems and its intended size, and assess the influence of ocular biometric parameters on the deviation. In this study, the Callisto eye image-guided system was employed to generate a digital rhexis reference circle (rhexis overlay) set at an intended diameter of 6 mm and a screenshot of the video was taken at the end of the cataract surgery, then to compare the deviation between the observed rhexis overlay diameter (ROD) and the optic diameter (6 mm). The factors influencing diameter deviation were identified with univariate and multivariate linear regression.
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