Taiwan Journal of Ophthalmology最新文献

This report describes a unique case of systemic diffuse large B-cell lymphoma (DLBCL) with initial ocular manifestations of bilateral optic disc edema and serous retinal detachment (SRD). A 29-year-old man presented with altered color vision in the left eye, mild fever, weakness, and headache, followed by bilaterally reduced visual acuity. Anterior segment and vitreous examinations showed no inflammation with sluggish response of light reflex. His fundus examination revealed bilateral multiple SRDs and optic disc swelling with choroidal thickening. On fluorescein angiography, pinpoint hyperfluorescence, associated dye pooling, and optic disc staining with leakage were found bilaterally. Laboratory studies revealed elevated C-reactive protein and mild leukocytosis with neutrophil predominance. He was provisionally diagnosed with probable Vogt-Koyanagi-Harada syndrome and received methylprednisolone pulse therapy. Five days later, his systemic condition deteriorated following initial ocular symptom improvement. Whole-body computerized tomography revealed clustered lymphadenopathies, which were interpreted as DLBCL after lymph node biopsy. His ocular condition improved after DLBCL chemotherapy. We hope to promote early recognition with appropriate workups through this case and literature review.

Citrobacter koseri is a rarely reported ocular pathogen. It may induce severe peripheral corneal inflammation and subsequent perforation by canaliculitis. Timely detection of the reservoir of this pathogen would halt its progression. The purpose of this study was to report a rare presentation of C. koseri chronic canaliculitis complicated with perforating peripheral ulcerative keratitis (PUK). A 71-year-old female who had several episodes of C. koseri conjunctivitis in the past 6 months was admitted to our infection ward under the impression of fever that was suspected to be related to urinary tract infection. She had concurrent copious mucopurulent discharge and blurred vision. Ocular examination disclosed hyperemic conjunctiva and an oval-shaped corneal infiltrate at 5-6 o'c periphery, which later rapidly progressed to PUK and corneal perforation. Despite aggressive treatment, the cornea continued to thin, and a second perforation occurred. After meticulous examination of the ocular adnexa, irrigation of inferior canaliculi revealed pustular discharge with profuse concretions indicating chronic canaliculitis. A cutaneous-lacrimal fistula was also found. Frequent antibiotic irrigation of the canaliculus finally halted the corneal melting and the cornea healed. Although rare, C. koseri may not only cause chronic canaliculitis but also induce peripheral corneal inflammation mimicking autoimmune-related PUK. Identification of C. koseri from conjunctival swab cultures should prompt the physicians to check chronic persistent canaliculus infections, which may help prevent rapidly progressive corneal inflammation and thus perforation. Management of C. koseri canaliculitis-induced PUK must also include antibiotic irrigation to eradicate canaliculitis infection at the reservoir and not just topical antibiotics.

The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

Purpose: To assess the additive potency of low-dose atropine combined with optical measures designed to decrease myopia progression.

Materials and methods: This retrospective study included 104 myopic children aged 5-12 over 4 years, divided into five groups: daily instillation of 0.01% atropine and distance single-vision spectacles (A), 0.01% atropine and progressive addition lenses (A + PAL), 0.01% atropine and soft contact lens with peripheral blur (A + CL). Two control groups were included, prescribed bifocal spectacles or single vision (SV) spectacles. Cycloplegic spherical equivalence refraction was measured biannually, including 1 year after cessation of treatment.

Results: A significant decrease in myopia progression was noted during the 2^nd and 3^rd years of atropine treatment: A -0.55 ± 0.55D, -0.15 ± 0.15, -0.12 ± 0.12D were 1^st, 2^nd, 3^rd years, respectively, A + PAL -0.47 ± 0.37D, -0.10 ± 0.25D, and -0.11 ± 0.25D were 1^st, 2^nd, 3^rd years, respectively, A + CL -0.36 ± 0.43D, -0.13 ± 0.29D, and -0.10 ± 0.27D were 1^st, 2^nd, 3^rd years, respectively. Myopia progression over 3 years, respectively, was -0.82 ± 0.50D, -0.70 ± 0.69D, -0.59 ± 0.66D in the bifocal group and -1.20 ± 1.28D, -0.72 ± 0.62D, -0.65 ± 0.47D in the SV group. One year after cessation of atropine treatment, myopia progression was - 0.32 ± 0.31D in A, -0.23 ± 0.28D in A + PAL, and -0.18 ± 0.35D in A + CL.

Conclusion: Atropine 0.01% presented as effective at decelerating myopia progression, more prominent in the 2^nd and 3^rd years of treatment. Combining atropine 0.01% with optical modalities exhibited a trend for added efficacy over monotherapy. A + CL exhibited the least rebound effect 1 year after cessation of treatment.

Purpose: The purpose is to study the 5-year results of aflibercept monotherapy using an individualized regimen in naïve patients with neovascular AMD (nAMD).

Materials and methods: This is a prospective observational study including naïve nAMD patients who underwent aflibercept injections with at least 5 years of follow-up. All of them received 3 monthly injections at the loading phase, followed by an observation period, then treated with an individualized treat-and-extend regimen. Visual acuity (VA) measurement and optical coherence tomography were performed at each visit.

Results: Forty-eight eyes were included. Of these, 30 were followed up for 5 years. The mean follow-up was 61.7 ± 2.3 months. The mean age was 81 ± 8 years. The visual gain was 7.3 ± 12.7 letters at 1 year, 6.5 ± 12.5 letters at 2 years, 5.2 ± 17 letters at 3 years, 6.2 ± 18.6 letters at 4 years, and 5.6 ± 20 letters at 5 years. At the last observation, 53% of eyes had VA > 70 letters. A complete fluid resolution was obtained in 53% of the eyes. At the 5-year endpoint, the total number of injections was 21.6 ± 13.4. Macular atrophy was observed in 18 eyes (60%) and subretinal fibrosis in 14 eyes (46%).

Conclusion: Patients with exudative AMD can maintain their visual function at 5 years with aflibercept using an individualized treatment. The loss of visual gain beyond 2 years could be related to the natural progression of the disease than the direct effect of anti-vascular endothelial growth injections.

The development of artificial intelligence (AI) and deep learning provided precise image recognition and classification in the medical field. Ophthalmology is an exceptional department to translate AI applications since noninvasive imaging is routinely used for the diagnosis and monitoring. In recent years, AI-based image interpretation of optical coherence tomography and fundus photograph in retinal diseases has been extended to diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. The rapid development of portable ocular monitoring devices coupled with AI-informed interpretations allows possible home monitoring or remote monitoring of retinal diseases and patients to gain autonomy and responsibility for their conditions. This review discusses the current research and application of AI, telemedicine, and home monitoring devices on retinal disease. Furthermore, we propose a future model of how AI and digital technology could be implemented in retinal diseases.

Purpose: To characterize the epidemiology, associated complications, and risk factors of orbital floor fractures in a nationwide longitudinal health insurance database.

Materials and methods: Claims data from a million randomly selected registered residents from the Taiwan National Health Insurance Research Database were analyzed between 2001 and 2011 as part of a retrospective cohort review. Patients were identified using the International Classification of Disease-9 diagnosis codes for orbital floor fracture (closed: 802.6; open: 802.7). The cases were categorized as surgical or nonsurgical based on the procedure codes and compared statistically.

Results: From 2001 to 2011, 663 patients were diagnosed with orbital floor fractures out of a total population at risk of 9,836,431 person-years (average incidence: 6.78 persons/100,000/year) with overall increasing incidence. Surgical treatments were performed in 213 (32%) patients. Patients who received surgical treatment were younger than those who did not (mean age 25.3 ± 13.6 years vs. 34.2 ± 18.6 years, P < 0.001). The diagnosis with diplopia was a significantly associated factor for surgical treatment (2.2% in nonsurgery group vs. 6.6% in surgery group, P = 0.007). Male gender (adjusted hazard ratios [aHR] = 2.1, 95% confidence interval [CI]: 1.79-2.49) and low monthly income (aHR = 1.76, 95% CI: 1.16-2.67) were the risk factors for orbital floor fracture.

Conclusion: The incidence of orbital floor fractures increased in the Taiwanese population between 2001 and 2011. Men and low income patients were at increased risk of orbital floor fracture. More research is necessary to clarify what factors are driving the escalating incidence of orbital fractures in this national population.

Cardiovascular disease (CVD) is a major cause of mortality and morbidity worldwide and imposes significant socioeconomic burdens, especially with late diagnoses. There is growing evidence of strong correlations between ocular images, which are information-dense, and CVD progression. The accelerating development of deep learning algorithms (DLAs) is a promising avenue for research into CVD biomarker discovery, early CVD diagnosis, and CVD prognostication. We review a selection of 17 recent DLAs on the less-explored realm of DL as applied to ocular images to produce CVD outcomes, potential challenges in their clinical deployment, and the path forward. The evidence for CVD manifestations in ocular images is well documented. Most of the reviewed DLAs analyze retinal fundus photographs to predict CV risk factors, in particular hypertension. DLAs can predict age, sex, smoking status, alcohol status, body mass index, mortality, myocardial infarction, stroke, chronic kidney disease, and hematological disease with significant accuracy. While the cardio-oculomics intersection is now burgeoning, very much remain to be explored. The increasing availability of big data, computational power, technological literacy, and acceptance all prime this subfield for rapid growth. We pinpoint the specific areas of improvement toward ubiquitous clinical deployment: increased generalizability, external validation, and universal benchmarking. DLAs capable of predicting CVD outcomes from ocular inputs are of great interest and promise to individualized precision medicine and efficiency in the provision of health care with yet undetermined real-world efficacy with impactful initial results.

Purpose: The aim of this study is to compare twelve intraocular lens power calculation formulas for eyes longer than 25.0 mm in terms of absolute error (AE), the percentage of postoperative emmetropia, and agreement interval in Bland-Altman analysis.

Materials and methods: Data of myopic patients who underwent uneventful phacoemulsification between January 2016 and July 2021 was reviewed. Intraocular lens power was calculated using Holladay 1, SRK/T, Hoffer Q, Holladay 2, Haigis, Barrett Universal II, Hill-RBF, Ladas, Kane, EVO, Pearl-DGS, and K6 formulas. Three months after phacoemulsification, refraction was measured, and mean AE was calculated. The percentage of patients with full visual acuity (VA) without any correction, with ± 0.25D, ±0.5D, ±0.75D, and limits of agreement for each formula were established.

Results: Ninety-one patients, whose ocular axial length ranged between 25.03 mm and 28.91 mm, were included in the study. The Barrett Universal II formula achieved the lowest mean AE of 0.11 ± 0.11 (P < 0.001) just before Kane (0.13 ± 0.09; P < 0.001 except vs. Haigis and Holladay 2) and SRK/T formulas (0.18 ± 0.12). In addition, the Barrett Universal II formula had the highest percentage of patients with full VA without any correction (72.5%) followed by Kane and Holladay 2 formulas (56.0% and 49.5%, respectively). Finally, Barrett Universal II, Kane, and Haigis formulas obtained the lowest agreement interval (0.5725, 0.6088, and 0.8307, respectively).

Conclusion: The Barrett Universal II formula is recommended for intraocular lens power calculation for eyeballs with the axial length longer than 25.0 mm. The Kane formula also gives very promising results in regarding the accuracy of intraocular lens power for myopic eyes.