The novel mutation P36R in LRP5L contributes to congenital membranous cataract via inhibition of laminin γ1 and c-MAF.

PURPOSE: The present study investigated a pathogenic mutation and its mechanism on membranous cataract in a congenital membranous cataract family. METHODS: An autosomal dominant four-generation Chinese congenital membranous cataract family was recruited and whole-exome sequencing was performed to screen for sequence variants. Candidate variants were validated using polymerase chain reaction and Sanger sequencing. Wild-type and mutant low-density lipoprotein receptor-related protein 5-like (LRP5L) plasmids were constructed and transfected into human lens epithelial cells (HLE B-3) and human anterior lens capsules. The cell lysates, nuclear and cytoplasmic proteins, and basement membrane components of HLE B-3 cells were harvested. LRP5L and laminin γ1 were knocked down in HLE B-3 cells using specific small-interfering RNA. The protein expression levels of LRP5L, laminin γ1, and c-MAF were detected using immunoblotting and immunofluorescence. RESULTS: We identified a novel suspected pathogenic mutation in LRP5L (c.107C > G, p.P36R) in the congenital membranous cataract family. This mutation was absent in 300 normal controls and 300 age-related cataract patients. Bioinformatics analysis with PolyPhen-2 and SIFT suggested that LRP5L-P36R was pathogenic. LRP5L upregulated laminin γ1 expression in the cytoplasmic proteins of HLE B-3 cells and human anterior lens capsules, and LRP5L-P36R inhibited the effects of LRP5L. LRP5L upregulated c-MAF expression in the nucleus and cytoplasm of HLE B-3 cells, and LRP5L-P36R inhibited c-MAF expression via inhibition of laminin γ1. CONCLUSION: Our study identified a novel gene, LRP5L, associated with congenital membranous cataract, and its mutant LRP5L-P36R contributed to membranous cataract development via inhibition of laminin γ1 and c-MAF.

[Clinical evaluation of a thermodynamic treatment system for meibomian gland dysfunction].

OBJECTIVE: To evaluate the effectiveness and safety of a single thermodynamic treatment system (LipiFlow) for meibomian gland dysfunction (MGD). METHODS: Retrospective series case study. Forty-eight subjects with meibomian gland dysfunction were analyzed before and after 12 minutes LipiFlow system treatment. All subjects were examined before, 4 weeks and 12 weeks after this treatment. Subjective symptoms, lipid layer thickness (LLT), expressible meibomian gland, tear break-up time, meibomian gland assessment and ocular surface staining were measured. Statistical analysis was performed using SPSS 11.5 software. Analysis included those independent sample two-tailed t-tests for comparison of the mean change from baseline to 4 weeks and baseline to 12 weeks after Lipiflow treatment. Statistically significant difference was based on α=0.05 (P<0.05). RESULTS: A total of 48 cases completed the 12-week follow-up. MGD patients with LipiFlow treatment had a significant reduction in the Ocular Surface Disease Index (OSDI) scores with 45.36±19.34 before treatment, 23.82±11.94 at 4 weeks (t=2.009, P=0.035) and 25.66±14.12 at 12 weeks (t=1.976 P=0.038). LipiFlow resulted in a higher number of expressible glands from 2.91±1.13 (baseline) to 6.27±2.37 (4 weeks, t=3.505, P<0.001) and 5.15±2.08 (12 weeks, t=2.004, P= 0.027) and change of secretion quality from 6.18±2.48 (baseline) to 13.55±3.46 (4 weeks,t=2.698,P=0.005) and 12.67±3.41 (12 weeks,t=2.403,P=0.009). In addition, a single thermodynamic treatment increase the LLT from (42.13±9.67)nm (baseline) to (59.02±16.39)nm (4 weeks, t=2.971, P=0.002) and (54.65±12.52)nm (12 weeks, t=2.021, P=0.021). The rate of partial blink was relieved from 0.37±0.30 (baseline) to 0.15±0.14 (4 weeks, t=3.428, P=0.035) and 0.12±0.13 (12 weeks, t=1.986, P=0.026). BUT was increased from (4.73± 2.34)s (baseline) to (9.32 ± 2.18)s (4 weeks, t=3.385, P<0.001) and (9.91 ± 3.01)s (12 weeks, t=3.253, P< 0.001). There were no unanticipated or serious device-related adverse events reported. Compared with baseline, there was no statistically significant difference in BCVA, corneal staining and intraocular pressure (P=0.141, 0.376, 0.421). CONCLUSION: The LipiFlow system was a safety and effectiveness treatment of MGD in the 12-week study.

Adaptive enhancement of cataractous retinal images for contrast standardization.

Cataract affects the quality of fundus images, especially the contrast, due to lens opacity. In this paper, we propose a scheme to enhance different cataractous retinal images to the same contrast as normal images, which can automatically choose the suitable enhancement model based on cataract grading. A multi-level cataract dataset is constructed via the degradation model with quantified contrast. Then, an adaptive enhancement strategy is introduced to choose among three enhancement networks based on a blurriness classifier. The blurriness grading loss is proposed in the enhancement models to further constrain the contrast of the enhanced images. During test, the well-trained blurriness classifier can assist in the selection of enhancement networks with specific enhancement ability. Our method performs the best on the synthetic paired data on PSNR, SSIM, and FSIM and has the best PIQE and FID on 406 clinical fundus images. There is a 7.78% improvement for our method compared with the second on the introduced [Formula: see text] score without over-enhancement according to [Formula: see text], which demonstrates that the proper enhancement by our method is close to the high-quality images. The visual evaluation on multiple clinical datasets also shows the applicability of our method for different blurriness. The proposed method can benefit clinical diagnosis and improve the performance of computer-aided algorithms such as vessel tracking and vessel segmentation.

Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder.

Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples; 2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous Auto-Encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder.

Anomaly detection is an important yet challenging task in medical image analysis. Most anomaly detection methods are based on reconstruction, but the performance of reconstruction-based methods is limited due to over-reliance on pixel-level losses. To address the limitation, we propose a patch-wise contrastive learning-based auto-encoder for medical anomaly detection. The key contribution is the patch-wise contrastive learning loss that provides supervision on local semantics to enforce semantic consistency between corresponding input-output patches. Contrastive learning pulls corresponding patch pairs closer while pushing non-corresponding ones apart between input and output, enabling the model to learn local normal features better and improve discriminability on anomalous regions. Additionally, we design an anomaly score based on local semantic discrepancies to pinpoint abnormalities by comparing feature difference rather than pixel variations. Extensive experiments on three public datasets (i.e., brain MRI, retinal OCT, and chest X-ray) achieve state-of-the-art performance, with our method achieving over 99% AUC on retinal and brain images. Both the contrastive patch-wise supervision and patch-discrepancy score provide targeted advancements to overcome the weaknesses in existing approaches.

Clinical effects of neuroendoscopic infratentorial supracerebellar approach surgical technique for resecting pineal tumors: a retrospective study.

OBJECTIVE: Pineal tumors are relatively rare central nervous system lesions with a predilection for the pediatric population. This article aims to explore the clinical effects of neuroendoscopic infratentorial supracerebellar approach for resecting tumors in the pineal area. METHODS: This is a retrospective study that included patients who underwent neuroendoscopic infratentorial supracerebellar approach to resect nine tumors in the pineal area at the Department of Neurosurgery of the Second Hospital of Lanzhou University from December 2017 to October 2023. RESULTS: The results of postoperative MRI revealed that all tumors were resected. Five patients received postoperative radiotherapy, three patients received radiotherapy along with chemotherapy, and one patient received neither radiotherapy nor chemotherapy. The pathological results showed that four patients were diagnosed with germinoma, two patients with teratoma, two patients with mixed germ cell tumors, and one patient with central neurocytoma. After surgery, one patient developed psychiatric symptoms, two patients developed binocular upward vision and diplopia, and one patient developed unstable walking and diplopia. With a follow-up of 1.7-4.8 years, all nine patients lived normally. Furthermore, none of them had tumor recurrence or death. CONCLUSION: The simple neuroendoscopic infratentorial supracerebellar approach has some safety and efficacy. It is suitable for tumors in the pineal region where the disease is mainly located below the Galen vein complex.

Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma.

Purpose: This study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around optic disc regions in fundus photography trained by optical coherence tomography (OCT) and diagnose glaucoma based on the predicted comprehensive information about RNFLT. Methods: A total of 1403 pairs of fundus photographs and OCT RNFLT scans from 1403 eyes of 1196 participants were included. A residual deep neural network was trained to predict the RNFLT for each local image in a fundus photograph, and then a RNFLT report was generated based on the local images. Two indicators were designed based on the generated report. The support vector machines (SVM) algorithm was used to diagnose glaucoma based on the two indicators. Results: A strong correlation was found between the predicted and actual RNFLT values on local images. On three testing datasets, we found the Pearson r to be 0.893, 0.850, and 0.831, respectively, and the mean absolute error of the prediction to be 14.345, 17.780, and 19.250 µm, respectively. The area under the receiver operating characteristic curves for discriminating glaucomatous from healthy eyes was 0.860 (95 % confidence interval, 0.799-0.921). Conclusions: We established a novel local image-based DL approach to provide comprehensive quantitative information on RNFLT in fundus photographs, which was used to diagnose glaucoma. In addition, training a deep neural network based on local images to predict objective detail information in fundus photographs provided a new paradigm for the diagnosis of ophthalmic diseases.

Deep Learning-Enabled Vasculometry Depicts Phased Lesion Patterns in High Myopia Progression.

PURPOSE: To investigate the potential phases in myopic retinal vascular alterations for further elucidating the mechanisms underlying the progression of high myopia (HM). METHODS: For this retrospective study, participants diagnosed with high myopia at Beijing Tongren Hospital were recruited. Based on bionic mechanisms of human vision, an intelligent image processing model was developed and utilized to extract and quantify the morphological characteristics of retinal vasculatures in different regions measured by papilla-diameter (PD), including vascular caliber, arteriole-to-venule ratio (AVR), tortuosity, the angle of the vascular arch (AVA), the distance of the vascular arch (DVA), density, fractal dimension, and venular length. In addition, the optic disc and the area of peripapillary atrophy (PPA) were also quantified. The characteristics of the overall population, as well as patients aged less than 25 years old, were compared by different genders. Univariate and multiple linear regression analyses were conducted to investigate the correlation of retinal vasculature parameters with PPA width, and the detailed trends of the vascular indicators were analyzed to explore the potential existence of staged morphological changes. FINDINGS: The study included 14,066 fundus photographs of 5,775 patients (aged 41.2 ± 18.6 years), of whom 7,379 (61.2%) were female. The study included 12,067 fundus photographs of 5,320 patients (aged 41.2 ± 18.6 years). Significant variations in the morphological parameters of retinal vessels were observed between males and females. After adjusting for age and sex, multiple linear regression analysis showed that an increased PPA width ratio was associated with lower AVA (1PD), DVA (1PD), vascular caliber (0.5-1.0 PD), tortuosity (0.5-1.0 PD), density and fractal dimension (all P < 0.001, Spearman's ρ < 0). Overall, the changes in retinal vascular morphology showed two phases: tortuosity (0.5-1.0PD) and AVA (1PD) decreased rapidly in the first stage but significantly slowly in the second stage, while vascular density and fractal dimension showed a completely opposite trend with an initial slow and a subsequent rapid decline. CONCLUSIONS: This study identified two distinct phases of retinal vascular morphological changes during the progression of HM. Traction lesions were predominant in the initial stage, while atrophic lesions were predominant in the later stage. These findings provide further insight into the development mechanism of HM from the perspective of retinal vasculature.

A C-terminal fragment BIGH3 protein with an RGDRGD motif inhibits corneal neovascularization in vitro and in vivo.

An Arg-Gly-Asp (RGD) motif in the fourth FAS1 domain of the human BIGH3 (transforming growth factor-ß1-inducible gene-h3) protein has been reported to play an important role in mediating tumor angiogenesis. The aim of this study was to investigate the inhibitory effect of a modified C-terminal fragment BIGH3 protein with an RGDRGD motif on corneal neovascularization in vitro and in vivo. Recombinant C-terminal fragment BIGH3 protein with wild-type sequence and modified C-terminal fragment BIGH3 protein containing an RGDRGD motif were successfully expressed and purified. We demonstrated that both proteins significantly inhibited vascular endothelial growth factor (VEGF)-induced human umbilical vein endothelial cell (HUVEC) adhesion, migration, and tube formation and induced cell apoptosis but failed to inhibit HUVEC proliferation. We determined that the mechanism underlying this activity was an interaction between BIGH3 and αvß3 integrin, which blocked the phosphorylation of PI3K/Akt and ERK signaling pathways. The inhibitory effects of wild-type and modified C-terminal fragment BIGH3 proteins on angiogenesis were confirmed by a rabbit corneal neovascularization assay. More importantly, we provided evidence that the modified C-terminal fragment BIGH3 protein with an RGDRGD motif inhibited angiogenic activity far more effectively than did wild-type C-terminal fragment BIGH3. Collectively, our data show that a C-terminal fragment BIGH3 protein containing an RGDRGD motif might be promising as an effective drug in treating corneal neovascularization.

A deep network using coarse clinical prior for myopic maculopathy grading.

Pathological Myopia (PM) is a globally prevalent eye disease which is one of the main causes of blindness. In the long-term clinical observation, myopic maculopathy is a main criterion to diagnose PM severity. The grading of myopic maculopathy can provide a severity and progression prediction of PM to perform treatment and prevent myopia blindness in time. In this paper, we propose a feature fusion framework to utilize tessellated fundus and the brightest region in fundus images as prior knowledge. The proposed framework consists of prior knowledge extraction module and feature fusion module. Prior knowledge extraction module uses traditional image processing methods to extract the prior knowledge to indicate coarse lesion positions in fundus images. Furthermore, the prior, tessellated fundus and the brightest region in fundus images, are integrated into deep learning network as global and local constrains respectively by feature fusion module. In addition, rank loss is designed to increase the continuity of classification score. We collect a private color fundus dataset from Beijing TongRen Hospital containing 714 clinical images. The dataset contains all 5 grades of myopic maculopathy which are labeled by experienced ophthalmologists. Our framework achieves 0.8921 five-grade accuracy on our private dataset. Pathological Myopia (PALM) dataset is used for comparison with other related algorithms. Our framework is trained with 400 images and achieves an AUC of 0.9981 for two-class grading. The results show that our framework can achieve a good performance for myopic maculopathy grading.

Non-invasive intracranial pressure estimation using ultrasonographic measurement of area of optic nerve subarachnoid space.

OBJECTIVE: To verify whether the area of the ONSAS (ONSASA) obtained by transorbital ultrasonography can be used to accurately evaluate the intracranial pressure (ICP). METHODS: The recorded indexes included the optic nerve diameter, the optic nerve sheath diameter (ONSD), the width of both sides of the ONSAS (ONSASW) at 3 mm from the optic nerve head and the entire ONSASA outlined between 3 and 7 mm. After exploring and comparing five models to describe the relationship between body mass index (BMI), mean arterial blood pressure (MABP), ONSASA and ICP, the best model was determined. RESULTS: In all, 90 patients with neurological diseases undergoing continuous invasive ICP monitoring were included in the study. In the training group, the correlation coefficient for the association between the ICP and ONSASA (Pearson's correlation r=0.953) was higher than that for the association of the ICP with the ONSD (r=0.672; p<0.0001) and ONSASW at 3 mm behind the globe (r=0.691; p<0.0001). In the training group, the weighting function for prediction of the ICP was as follows: non-invasive ICP=2.050×ONSASA-0.051×BMI +0.036*MABP-5.837. With 20 mm Hg as the cut-off point for a high or low ICP, the sensitivity and specificity of ONSASA predicting ICP was 1.00 and 0.92. Receiver operator curve analysis revealed that the calculated cut-off value for predicting elevated ICP was 19.96 (area under curve= 0.960, 95% CI 0.865 to 1.00). CONCLUSION: Measurement of the ONSASA using ultrasonography can serve as a practical method for rapid and non-invasive quantification for evaluating ICP through an accurate mathematical formula with the BMI and MABP considered as contributing parameters. TRIAL REGISTRATION NUMBER: The study was registered in the Chinese Clinical Trial Registry (Study no ChiCTR2100045274).

Deep ensemble learning for accurate retinal vessel segmentation.

Significant progress has been made in deep learning-based retinal vessel segmentation in recent years. However, the current methods suffer from low performance and the robust of the models is not that good. Our work introduces an novel framework for retinal vessel segmentation based on deep ensemble learning. The results of benchmarking comparisons indicate that our model outperforms the existing ones on multiple datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. It evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision Transformer and FCN-Transformer. We expect our proposed method can benefit and accelerate the development of accurate retinal vessel segmentation in this field.

A fundus image classification framework for learning with noisy labels.

Fundus images are widely used in the screening and diagnosis of eye diseases. Current classification algorithms for computer-aided diagnosis in fundus images rely on large amounts of data with reliable labels. However, the appearance of noisy labels degrades the performance of data-dependent algorithms, such as supervised deep learning. A noisy label learning framework suitable for the multiclass classification of fundus diseases is presented in this paper, which combines data cleansing (DC), adaptive negative learning (ANL), and sharpness-aware minimization (SAM) modules. Firstly, the DC module filters the noisy labels in the training dataset based on the prediction confidence. Then, the ANL module modifies the loss function by choosing complementary labels, which are neither the given labels nor the labels with the highest confidence. Moreover, for better generalization, the SAM module is applied by simultaneously optimizing the loss and its sharpness. Extensive experiments on both private and public datasets show that our method greatly promotes the performance for classification of multiple fundus diseases with noisy labels.

Cost-utility analysis of commonly used anti-glaucoma interventions for mild-to-moderate primary open-angle glaucoma patients in rural and urban China.

OBJECTIVE: An increasing number of studies have explored the clinical effects of antiglaucoma surgical procedures; however, economic evidence was scarce. We aimed to compare the cost-effectiveness between maximal medical treatment (MMT) and commonly used surgical procedures (trabeculectomy, Ahmed glaucoma valve implantation, gonioscopy-assisted transluminal trabeculotomy and ab interno canaloplasty). DESIGN AND SETTING: A Markov model study. PARTICIPANTS: A hypothetical cohort of 100 000 patients with mild-to-moderate primary open-angle glaucoma (POAG). OUTCOMES: Data were obtained from public sources. The main outcomes were incremental cost-utility ratios (ICURs) using quality-adjusted life-years (QALYs). Sensitivity analyses were conducted to verify the robustness and sensitivity of base-case results. MAIN RESULTS: Both cumulative costs and QALYs gained from surgical procedures (US$6045-US$13 598, 3.33-6.05 QALYs) were higher than those from MMT (US$3117-US$6458, 3.14-5.66 QALYs). Compared with MMT, all surgical procedures satisfied the cost-effectiveness threshold (lower than US$30 501 and US$41 568 per QALY gained in rural and urban settings, respectively). During the 5-year period, trabeculectomy produced the lowest ICUR (US$21 462 and US$15 242 per QALY gained in rural and urban settings, respectively). During the 10-year-follow-up, trabeculectomy still produced the lowest ICUR (US$13 379 per QALY gained) in urban setting; however, gonioscopy-assisted transluminal trabeculotomy (US$19 619 per QALY gained) and ab interno canaloplasty (US$18 003 per QALY gained) produced lower ICURs than trabeculectomy (US$19 675 per QALY gained) in rural areas. Base-case results were most sensitive to the utilities and costs of initial treatment and maintenance. CONCLUSIONS: The long-term cost-effectiveness of commonly used surgical procedures could be better than the short-term cost-effectiveness for mild-to-moderate POAG patients in China. Health economic studies, supported by more rigorous structured real-world data, are needed to assess their everyday cost-effectiveness.

External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study.

OBJECTIVES: To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort. METHODS: We designed external validation in multiple scenarios, consisting of 3049 images from Qilu Hospital of Shandong University in China (QHSDU, validation dataset 1), 7495 images from three other hospitals in China (validation dataset 2), and 516 images from high myopia (HM) population of QHSDU (validation dataset 3). The corresponding sensitivity, specificity and accuracy of this AI diagnostic system to identify glaucomatous optic neuropathy (GON) were calculated. RESULTS: In validation datasets 1 and 2, the algorithm yielded accuracy of 93.18% and 91.40%, area under the receiver operating curves (AUC) of 95.17% and 96.64%, and significantly higher sensitivity of 91.75% and 91.41%, respectively, compared to manual graders. On the subsets complicated with retinal comorbidities, such as diabetic retinopathy or age-related macular degeneration, in validation datasets 1 and 2, the algorithm achieved accuracy of 87.54% and 93.81%, and AUC of 97.02% and 97.46%, respectively. In validation dataset 3, the algorithm achieved comparable accuracy of 81.98% and AUC of 87.49%, with a sensitivity of 83.61% and specificity of 81.76% on GON recognition specifically in the HM population. CONCLUSIONS: With acceptable generalization capability across varying levels of image quality, different clinical centres, or certain retinal comorbidities, such as HM, the automatic AI diagnostic system had the potential to provide expert-level glaucoma detection.

Implementing a digital comprehensive myopia prevention and control strategy for children and adolescents in China: a cost-effectiveness analysis.

Background: Children and adolescents' myopia is a major public problem. Although the clinical effect of various interventions has been extensively studied, there is a lack of national-level and integral assessments to simultaneously quantify the economics and effectiveness of comprehensive myopia prevention and control programs. We aimed to compare the cost-effectiveness between traditional myopia prevention and control strategy, digital comprehensive myopia prevention and control strategy and school-based myopia screening program in China. Methods: A Markov model was used to compare the cost-utility and cost-effectiveness among school-based myopia screening, traditional myopia prevention and control strategy, and digital comprehensive myopia prevention and control strategy among 6 to 18-year-old rural and urban schoolchildren. Parameters were collected from published sources. The primary outcomes were quality-adjusted life-year, disability-adjusted life-year, incremental cost-utility ratio, and incremental cost-effectiveness ratio. Extensive sensitivity analyses were performed to test the robustness and sensitivity of base-case analysis. Findings: Compared with school-based myopia screening strategy, after implementing digital comprehensive myopia prevention and control strategy, the prevalence of myopia among 18-year-old students in rural and urban areas was reduced by 3.79% and 3.48%, respectively. The incremental cost-utility ratio per quality-adjusted life-year gained with the digital myopia management plan ($11,301 for rural setting, and $10,707 for urban setting) was less than 3 times the per capita gross domestic product in rural settings ($30,501) and less than 1 time the per capita gross domestic product in urban settings ($13,856). In cost-effectiveness analysis, the incremental cost-effectiveness ratio produced by digital comprehensive myopia management strategy ($37,446 and $41,814 per disability-adjusted life-year averted in rural and urban settings) slightly exceeded the cost-effectiveness threshold. When assuming perfect compliance, full coverage of outdoor activities and spectacles satisfied the cost-effectiveness threshold, and full coverage of outdoor activities produced the lowest cost ($321 for rural settings and $808 for urban settings). Interpretations: Health economic evidence confirmed the cost-effectiveness of promoting digital comprehensive myopia prevention and control strategies for schoolchildren at the national level. Sufficient evidence provides an economic and public health reference for further action by governments, policy-makers and other myopia-endemic countries. Funding: National Natural Science Foundation of China, NSFC (82171051), Beijing Natural Science Foundation (JQ20029), Capital Health Research and Development of Special (2020-2-1081), National Natural Science Foundation of China, NSFC (82071000), National Natural Science Foundation of China, NSFC (8197030562).