Publications by authors named "Yuelan Gao"

2 Publications

  • Page 1 of 1

A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video).

Transl Vis Sci Technol 2021 Apr;10(4):22

Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.

Purpose: The purpose of this study was to construct a deep learning system for rapidly and accurately screening retinal detachment (RD), vitreous detachment (VD), and vitreous hemorrhage (VH) in ophthalmic ultrasound in real time.

Methods: We used a deep convolutional neural network to develop a deep learning system to screen multiple abnormal findings in ophthalmic ultrasonography with 3580 images for classification and 941 images for segmentation. Sixty-two videos were used as the test dataset in real time. External data containing 598 images were also used for validation. Another 155 images were collected to compare the performance of the model to experts. In addition, a study was conducted to assess the effect of the model in improving lesions recognition of the trainees.

Results: The model achieved 0.94, 0.90, 0.92, 0.94, and 0.91 accuracy in recognizing normal, VD, VH, RD, and other lesions. Compared with the ophthalmologists, the modal achieved a 0.73 accuracy in classifying RD, VD, and VH, which has a better performance than most experts (P < 0.05). In the videos, the model had a 0.81 accuracy. With the model assistant, the accuracy of the trainees improved from 0.84 to 0.94.

Conclusions: The model could serve as a screening tool to rapidly identify patients with RD, VD, and VH. In addition, it also has potential to be a good tool to assist training.

Translational Relevance: We developed a deep learning model to make the ultrasound work more accurately and efficiently.
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April 2021

The potential protective effects of miR-497 on corneal neovascularization are mediated via macrophage through the IL-6/STAT3/VEGF signaling pathway.

Int Immunopharmacol 2021 May 10;96:107745. Epub 2021 May 10.

Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan 430061, Hubei Province, PR China. Electronic address:

Corneal neovascularization (CoNV) can cause abnormal blood vessels to grow in the transparent cornea, leading to various sight-threatening eye diseases. MicroRNAs are known to play essential roles in the regulation of numerous biological functions. We try to clarify the role of a specific microRNA, miR‑497, which has been shown to regulate the growth of tumor cells and angiogenesis on the basis of available data. However, the association between miR-497 and vascularized cornea remains unclear. Therefore, it is urgently needed to understand the molecular mechanism of miR497 in the progress of corneal neovascularization. Animal model of CoNV was established in wildtype (WT) C57BL/6 mice, CRISPR/Cas9 mediated miR-497 knockout (KO) and overexpressed (TG) C57BL/6 mice. MiR-497, expressed in corneas, was actively involved in alkali burn-induced corneal neovascularization via targeting STAT3 and negatively regulating its expression, attenuating macrophage infiltration and M2 polarization. Knockdown of miR-497 enhanced the formation of corneal angiogenesis through targeting STAT3 and facilitating its expression, promoting recruitment of macrophages, while overexpression of miR-497 restrained blood vessel sprouting via regulating downstream STAT3 and VEGFA expression, reducing macrophage activation and inhibiting M2 polarization. Moreover, miR-497 knockout-mediated damage effect can be rescued through the inhibition of STAT3 signaling. Mechanically, miR-497 might serve as a potential strategy for pathological corneal neovascularization via macrophage through the IL-6/STAT3/VEGFA signaling pathway.
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May 2021