OBJECTIVE: To evaluate the long-term outcomes of three surgical procedures for the treatment of primary congenital glaucoma (PCG). INTRODUCTION: PCG is one of the main causes of blindness in children. There is a paucity of contemporary data on PCG in China. METHODS: A retrospective study of 48 patients (81 eyes) with PCG who underwent primary trabeculectomy, trabeculotomy, or combined trabeculotomy and trabeculectomy (CTT). RESULTS: All patients were less than 4 years (yrs) of age, with a mean age of 2.08 ± 1.23 yrs. The mean duration of follow-up was 5.49 ± 3.09 yrs. The difference in success rates among the three surgical procedures at 1, 3, 6 and 9 yrs was not statistically significant (p = 0.492). However, in patients with over 4 yrs of follow-up, Kaplan-Meier survival analysis revealed that the success rates of trabeculectomy and CTT declined more slowly than that of trabeculotomy. Among the patients, 66.22% acquired good vision (VA > 0.4), 17.57% acquired fair vision (VA = 0.1 - 0.3), and 16.22% acquired poor vision (VA < 0.1). The patients with good vision were mostly in the successful surgery group. Myopia was more prevalent postoperatively (p = 0.009). Reductions in the cup-disc ratio and corneal diameter were only seen in the successful surgery group (p = 0.000). In addition, the successful surgery group contained more patients that complied with a regular follow-up routine (p = 0.002). DISCUSSION: Our cases were all primary surgeries. Primary trabeculectomy was performed in many cases because no treatment was sought until an advanced stage of disease had been reached. CONCLUSIONS: In contrast to most reports, in the present study, trabeculectomy and CTT achieved higher long-term success rates than trabeculotomy. The patients with successful surgical results had better vision. Compliance with a routine of regular follow-up may increase the chances of a successful surgical outcome

PurposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.MethodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.ResultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.ConclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

The corneal response parameters provided by CST are informative for the assessment of corneal biomechanics. Patients with POAG showed significantly greater A1T and lower DA and A2T values than healthy controls, indicating a less deformable cornea in POAG patients.

BackgroundTo report the thickness of the retina, retinal ganglion cell (RGC)-related layers, and choroid in healthy subjects using swept source optical coherence tomography (SS-OCT).MethodsOne hundred and forty-six healthy volunteers were consecutively recruited for this prospective observational study. Thickness of retina, RGC-related layers, and choroid in the standard early treatment of diabetic retinopathy study (ETDRS) grid were automatically measured using one SS-OCT (DRI OCT-1, Topcon, Japan). The IOL Master (Carl Zeiss Meditec, Germany) was used to measure axial length (AL).ResultsThicknesses of the average macular ganglion cell complex (GCC) and ganglion cell-inner plexiform layer (GCIPL) were 105.3 ± 9.7 and 78.5 ± 6.2 um respectively. Neither of them was significantly related with sex, age, or AL. Both showed strong correlations with retinal thickness (r = 0.793, p = 0.000; r = 0.813, p = 0.000, respectively) and with similar topographic distributions within the retina. The thicknesses of retina and GCC/GCIPL in the inner sectors were significantly higher than in the outer sectors of the EDTRS area, while in the same region of the macula, the choroid exhibited completely different patterns of topographic variation. Men had 7.8 um thicker retina and 34.9 um thicker choroid than women after adjustment for age and AL (all p < 0.05). Age and AL could significantly influence the choroidal thickness but not the retina (all p < 0.05).ConclusionThickness of GCC/GCIPL in healthy Chinese individuals is not dramatically different across gender, age, and AL groups in terms of ETDRS grid, but sex is critical for retinal and choroidal thickness. Choroidal structure (but not retinal) can be significantly influenced by age and AL.Electronic supplementary materialThe online version of this article (doi:10.1186/s12886-015-0110-3) contains supplementary material, which is available to authorized users.

BackgroundTo measure the anterior and posterior ocular biometric characteristics concurrently and to determine the relationship between the iris and choroid in healthy Chinese subjects.MethodsA total of 148 subjects (270 eyes) were enrolled in this cross-section study. The anterior and posterior ocular biometric characteristics were measured simultaneously by anterior segment optical coherence tomography (AS-OCT) and swept-source optical coherence tomography (SS-OCT).ResultsCompared with male eyes, female eyes had narrower anterior biometric parameters that presented with smaller anterior segment parameters [including anterior chamber depth (ACD), width (ACW), area (ACA), and volume (ACV); (all p<0.001)], narrower anterior chamber angle parameters [including angle opening distance (AOD750), trabecular–iris space area (TISA750), and angle recess area (ARA); (all p<0.001)], higher iris curvature (ICURV) (p = 0.003), and larger lens vaults (LV) (p = 0.019). These anterior ocular biometric parameters were correlated with increasing age (p<0.01). Iris thickness (IT750) and iris area (IAREA) were associated with age, ACW, and pupil diameter (all p<0.05), while choroidal thickness (CT) was associated with age, gender, and axial length (all p<0.05). Univariate regression analysis showed that greater CT was significantly associated with smaller IAREA (p = 0.026).ConclusionCompared with male eyes, female eyes had narrower anterior biometric parameters that correlated with increasing age, which would be helpful in explaining the higher prevalence of angle closure rates in the female gender and in aging people. Increased CT might be associated with smaller iris area; however, this possibility needs to be investigated in future studies before this conclusion is made.

Background Common diseases are not satisfactorily managed under the current health-care system because of inadequate medical resources and limited accessibility. We aimed to establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios, and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel health-care facilities and capture modes. The datasets were labeled using a three-step strategy: capture mode recognition (modes: mydriatic-diffuse, mydriatic-slit lamp, non-mydriatic-diffuse, and nonmydriatic-slit lamp); cataract diagnosis as a normal lens, cataract, or a postoperative eye; and detection of referable cataracts with respect to cause and severity. Area under curve [AUC] was measured at each stage. We also integrated the above cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary health care, and specialised hospital services. The diagnostic accuracy, treatment referral, and ophthalmologist-topopulation service ratio were used to evaluate the performance and efficacy of the system. Findings The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in threestep tasks: capture mode recognition (AUC 99•28-99•71% for the four different capture modes), cataract diagnosis (AUC for mydriatic-slit lamp mode 99•82% [95%CI 98•93-100] for normal lens vs 99•96% [99•90-100] for cataract vs 99•93% [99•78-100] for postoperative eye, and AUCs >99% for other capture modes), and detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30•3% of people be referred to treatment, substantially increasing the ophthalmologist-to-population service ratio by 10•2-times compared with the traditional pattern. Interpretation The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

BackgroundThe existing literature contains no information regarding inflammatory cytokine expression in unilateral acute primary angle-closure (APAC) affected eyes and fellow eyes with primary angle closure suspect (PACS). To measure levels of various inflammatory cytokines in the aqueous humor (AH) of APAC affected eyes and fellow eyes with a diagnosis of PACS (18 unilateral APAC eyes and 18 fellow eyes with PACS), and determine the underlying correlation between them.MethodsThe total levels of 12 cytokines including granulocyte colony-stimulating factor (G-CSF), interleukin (IL)-6, IL-8, monocyte chemotactic protein (MCP)-1, MCP-3, macrophage-derived chemokine (MDC), macrophage inflammatory protein (MIP)-1β, and vascular endothelial growth factor (VEGF) etc. were assessed using the multiplex bead immunoassay technique. The level of cytokines in different groups was analyzed by a 2-related-samples nonparametric test. Data on patient demographics, preoperative intraocular pressure (IOP), number of glaucoma medications, as well as several ocular biological parameters were also collected for correlation analysis.ResultsThe APAC patients had significantly higher levels of G-CSF, IL-6, IL-8, MCP-1, MCP-3, MDC, MIP-1β, and VEGF in the AH samples from unilateral APAC affected eyes than in fellow eyes with PACS (all P < 0.05). The cytokines showed positive correlations between each other (P < 0.0071).ConclusionsCytokine networks in the AH may have critical roles in the progression of APAC. Thus, different cytokine expression in both eyes of the same patient may help us to understand the different pathology in APAC and PACS.

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