Alkali injury-induced pathological lymphangiogenesis in the iris facilitates the infiltration of T cells and ocular inflammation.

Inflammatory lymphangiogenesis is intimately linked to immune regulation and tissue homeostasis. However, current evidence has suggested that classic lymphatic vessels are physiologically absent in intraocular structures. Here, we show that neolymphatic vessels were induced in the iris after corneal alkali injury (CAI) in a VEGFR3-dependent manner. Cre-loxP-based lineage tracing revealed that these lymphatic endothelial cells (LECs) originate from existing Prox1+ lymphatic vessels. Notably, the ablation of iridial lymphangiogenesis via conditional deletion of VEGFR3 alleviated the ocular inflammatory response and pathological T cell infiltration. Our findings demonstrate that iridial neolymphatics actively participate in pathological immune responses following injury and suggest intraocular lymphangiogenesis as a valuable therapeutic target for the treatment of ocular inflammation.

A digital mask to safeguard patient privacy.

The storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extracted from such images. To minimize these risks, we developed a new technology, called the digital mask (DM), which is based on three-dimensional reconstruction and deep-learning algorithms to irreversibly erase identifiable features, while retaining disease-relevant features needed for diagnosis. In a prospective clinical study to evaluate the technology for diagnosis of ocular conditions, we found very high diagnostic consistency between the use of original and reconstructed facial videos (κ ≥ 0.845 for strabismus, ptosis and nystagmus, and κ = 0.801 for thyroid-associated orbitopathy) and comparable diagnostic accuracy (P ≥ 0.131 for all ocular conditions tested) was observed. Identity removal validation using multiple-choice questions showed that compared to image cropping, the DM could much more effectively remove identity attributes from facial images. We further confirmed the ability of the DM to evade recognition systems using artificial intelligence-powered re-identification algorithms. Moreover, use of the DM increased the willingness of patients with ocular conditions to provide their facial images as health information during medical treatment. These results indicate the potential of the DM algorithm to protect the privacy of patients' facial images in an era of rapid adoption of digital health technologies.

An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos.

BACKGROUND: Strabismus affects approximately 0.8-6.8% of the world's population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice. METHODS: An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020. RESULTS: In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1-99.7%], a specificity of 98.3% (95% CI, 94.6-99.5%), and an AUC of 0.998 (95% CI, 0.993-1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion. CONCLUSIONS: The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.

Contralateral lateral rectus muscle recession in a patient with unilateral exotropic Duane retraction syndrome type II: A case report.

A fixation preference for the affected eye is uncommon in patients with unilateral Duane retraction syndrome (DRS), and surgery on the fellow eye is rarely advocated. We are presenting a case report of a 9-year-old boy with unilateral DRS type II in the left eye who received lateral rectus muscle recession in his right amblyopic eye. The patient was orthophoric and his face turn was gone 6 months postoperatively. Surgery on the fellow amblyopic eye is a good choice for unilateral DRS where the affected eye dominants the fixation, and the satisfactory outcome suggests that alignment in the primary position can correct the face turn effectively despite the muscle duction deficit in the affected eye and further extend the binocular single visual field.