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Abstract Background To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans. Methods Data of this retrospective, case-control study was collected from March 2019 to June 2019 at the Department of Ophthalmology, Shanghai Ninth People’s Hospital, and then the patients were followed up for 3 months. One hundred fifty-two complete feature eyes from 100 voluntary patients with ptosis and satisfactory surgical results were selected, with 48 eyes excluded due to any severe condition or improper collection and shooting angle. Three experimental schemes were set as follows: use 2D distance alone, use 3D distance alone, and use two distances at the same time. The five most common evaluation indicators used in the binary classification problem to test the decision model were accuracy (ACC), precision, recall, F1-score, and area under the curve (AUC). Results For diagnostic discrimination, recall of “3D”, “2D” and “Both” schemes were 0.875, 0.875 and 0.938 respectively. And precision of the three schemes were 0.8333, 0.7778 and 1.0000 for the surgical procedure classification. Values of “Both” scheme that combined 2D and 3D data were the highest in two classifications. Conclusions In this study, 3D eye data are introduced into clinical practice to construct a decision model for ptosis surgery. Our decision model presents exceptional prediction effect, especially when 2D and 3D data employed jointly.

Abstract Background To introduce a treatment option: dacryoendoscopy-assisted incision of Hasner’s valve under nasoendoscopy and assess its efficacy in treating membranous congenital nasolacrimal duct obstruction (CNLDO) in children older than 1 year with history of initial probing failure. Methods 52 eyes of 43 children with membranous CNLDO who underwent dacryoendoscopy-assisted incision of Hasner’s valve under nasoendoscopy between May 2012 and October 2020 were enrolled in this retrospective study. All participants were older than 1 year and all the eyes had gone through probing at least once but still had symptoms of epiphora and discharge. Surgical success was defined as a normal fluorescein dye disappearance test (FDDT) and the absence of pre-operation complaints, including epiphora, muco-purulent discharge, increased tear lake or the symptoms of acute infection such as acute dacryocystitis. Patients’ demographics, clinical features and follow-up outcomes were evaluated. Results Of all these operated cases, surgical success was achieved in 52/52 eyes without any early or late complications. The overall success rate was 100%. Conclusions Dacryoendoscopy-assisted incision of Hasner’s valve under nasoendoscopy is an effective and minimally invasive surgical treatment in membranous CNLDO patients with initial probing failure.

Abstract Background This study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations. Methods A total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People’s Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks. Results Network A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021). Conclusions The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.