Collagen fibers play a central role in normal eye mechanics and pathology. In ocular tissues, collagen fibers exhibit a complex 3-dimensional (3D) fiber orientation, with both in-plane (IP) and out-of-plane (OP) orientations. Imaging techniques traditionally applied to the study of ocular tissues only quantify IP fiber orientation, providing little information on OP fiber orientation. Accurate description of the complex 3D fiber microstructures of the eye requires quantifying full 3D fiber orientation. Herein, we present 3dPLM, a technique based on polarized light microscopy developed to quantify both IP and OP collagen fiber orientations of ocular tissues. The performance of 3dPLM was examined by simulation and experimental verification and validation. The experiments demonstrated an excellent agreement between extracted and true 3D fiber orientation. Both IP and OP fiber orientations can be extracted from the sclera and the cornea, providing previously unavailable quantitative 3D measures and insight into the tissue microarchitecture. Together, the results demonstrate that 3dPLM is a powerful imaging technique for the analysis of ocular tissues.
Background Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. Methods We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. Results The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81–0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83–0.95) and 0.88 (0.79–0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88–0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81–0.92) and 0.88 (0.83–0.94) in external test sets 1 and 2, respectively. Conclusion Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.
The measurement of soft tissue fiber orientation is fundamental to pathophysiology and biomechanical function in a multitude of biomedical applications. However, many existing techniques for quantifying fiber structure rely on transmitted light, limiting general applicability and often requiring tissue processing. Herein, we present a novel wide-field reflectance-based imaging modality, which combines polarized light imaging (PLI) and spatial frequency domain imaging (SFDI) to rapidly quantify preferred fiber orientation on soft collagenous tissues. PLI utilizes the polarization dependent scattering property of fibers to determine preferred fiber orientation; SFDI imaging at high spatial frequency is introduced to reject the highly diffuse photons and to control imaging depth. As a result, photons scattered from the superficial layer of a multi-layered sample are highlighted. Thus, fiber orientation quantification can be achieved for the superficial layer with optical sectioning. We demonstrated on aortic heart valve leaflet that, at spatial frequency of f = 1mm(-1) , the diffuse background can be effectively rejected and the imaging depth can be limited, thus improving quantification accuracy.
It is hypothesized that vision loss in glaucoma is due to excessive mechanical deformation within the neural tissue inside the scleral canal. This study proposes a new model for how the collagen of the peripapillary sclera surrounding the canal is organized to support the delicate neural tissue inside. Previous low-resolution studies of the peripapillary sclera suggested that the collagen fibers are arranged in a ring around the canal. Instead, we provide microscopic evidence suggesting that the canal is also supported by long-running interwoven fibers oriented tangentially to the canal. We demonstrate that this arrangement has multiple biomechanical advantages over a circular collagen arrangement and can explain previously unexplained experimental findings including contraction of the scleral canal under elevated intraocular pressure.
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