Taiwan Journal of Ophthalmology最新文献

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.

Here, we report a case of bilateral cystoid macular edema (CME) in a woman with phakic eyes after 22 years of latanoprost use. Optical coherence tomography revealed multiple intraretinal cysts, and fluorescein angiography revealed characteristic petaloid dye leakage from the perifoveal capillaries. A cause-effect relationship was suspected when CME resolved after switching from latanoprost to dorzolamide, as confirmed by positive rechallenge and dechallenge tests. Since prostaglandin analog-induced CME has only been reported in patients with pseudophakic, aphakic, or phakic eyes with retinal conditions predisposing to macular edema, this seems to be the first reported case of latanoprost-induced CME in a patient with phakic eyes without retinal risk factors.

Choroidal ruptures occur in 5% to 10% closed-globe injuries with wide variation in visual prognosis, which depending on the visual acuity at presentation, the location of the rupture, and other associated ocular injuries. We reported a case of bilateral traumatic choroidal rupture with a large macular hole. We performed surgery in the right eye of microincisional vitrectomy, temporally inverted internal limiting membrane (ILM) flap, and C3F8 tamponade; then microincisional vitrectomy, fibrotic scar removal, double inverted ILM flap, and C3F8 tamponade in the left eye. After surgery, she achieved both good anatomical and visual acuity improvement in the right eye, but limited visual acuity improvement in the left eye due to subfoveal choroidal scar formation.

Papilledema is an optic disc swelling with increased intracranial pressure as the underlying cause. Diagnosis of papilledema is made based on ophthalmoscopy findings. Although important, ophthalmoscopy can be challenging for general physicians and nonophthalmic specialists. Meanwhile, artificial intelligence (AI) has the potential to be a useful tool for the detection of fundus abnormalities, including papilledema. Even more, AI might also be useful in grading papilledema. We aim to review the latest advancement in the diagnosis of papilledema using AI and explore its potential. This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was performed on four databases (PubMed, Cochrane, ProQuest, and Google Scholar) using the Keywords "AI" and "papilledema" including their synonyms. The literature search identified 372 articles, of which six met the eligibility criteria. Of the six articles included in this review, three articles assessed the use of AI for detecting papilledema, one article evaluated the use of AI for papilledema grading using Frisèn criteria, and two articles assessed the use of AI for both detection and grading. The models for both papilledema detection and grading had shown good diagnostic value, with high sensitivity (83.1%-99.82%), specificity (82.6%-98.65%), and accuracy (85.89%-99.89%). Even though studies regarding the use of AI in papilledema are still limited, AI has shown promising potential for papilledema detection and grading. Further studies will help provide more evidence to support the use of AI in clinical practice.