Long-acting (lasting extend) diquafosol ophthalmic solution 3% (DQSLX) is administered three times daily versus six times daily for the currently approved diquafosol ophthalmic solution (DQS). We investigated the efficacy and adherence of switching from DQS to DQSLX in patients with dry eye disease. We retrospectively enrolled 54 patients (17 men and 37 women) with eye drop prescription changes from DQS to DQSLX between December 2022 and March 2023. The number of eye drops, subjective symptoms, tear breakup time (TBUT), and fluorescein staining scores from baseline to 4 weeks after starting DQSLX were evaluated. Participants then chose between DQSLX and DQS. Patients administered DQSLX three times per day, as listed on the package insert, 88.9% of the time; significantly higher than the 5.6% of patients who used DQS six times per day, as instructed. The DQSLX group showed significant improvements in symptoms and fluorescein staining scores (23.3 ± 20.1 and 0.8 ± 1.7, respectively) compared with the baseline (37.8 ± 24.1 and 1.1 ± 1.5, p = 0.01 and <0.001, respectively). The TBUT in the DQSLX group (5.0 ± 2.5 s) did not significantly improve compared to the DQS group (4.5 ± 1.7 s) (p = 0.75). Fifty-one (94.4%) patients opted to continue DQSLX because of the pleasant feeling of the eye drops, long-lasting moisture, and less frequent administration. The efficacy and adherence of DQSLX was comparable to DQS.
We report a case of corneal melting through sterile infiltration presumably due to excessive use of nonsteroidal anti-inflammatory eye drops after corneal crosslinking (CXL). It was treated using steroids combined with amniotic membrane transplantation (AMT). A 33-year-old man with progressing keratoconus underwent left eye CXL. We prescribed betamethasone and levofloxacin eye drops 5 times daily and diclofenac sodium eye drops 3 times daily for 3 days. Three days after CXL, there was a persistent epithelial defect and the left corneal stroma was clouded. His visual acuity remained unchanged. We prescribed betamethasone hourly and 20 mg prednisolone daily. At 1 week after CXL, the corneal epithelial defect and infiltration were unchanged. He had not stopped using diclofenac at the prescribed time. On day 17, we performed AMT and his cornea and visual acuity improved. AMT may be effective against persistent epithelial defects and corneal melting after CXL.
The center of the fovea, termed the foveola, is the area of highest visual acuity, has the highest density of cone photoreceptors. We investigated the distance between the automatically-determined center of the foveal avascular zone (FAZ) and the manually-determined highest foveal bulge (FB) point using single swept-source optical coherence tomography angiography (OCTA) instrument. This cross-sectional study included 49 eyes of 49 individuals (34 women and 15 men; median age: 68 years) with no history of ocular disorders. The FAZ in the superficial capillary plexus was automatically determined using the Kanno–Saitama macro method, and the center of the FAZ was automatically determined using ellipse approximation. Another candidate foveal center, the highest FB point, was determined manually on the serial cross-sectional B-scan images. As a result, the foveal center was manually identified as the highest FB point on B-scan OCTA images. The center of the FAZ was more likely to be located inferior to the highest FB point (p = 0.031). In participants with a total (linear) distance of more than 50 μm between the center of the FAZ and the highest FB point, the displacement was significantly more in the horizontal direction than in the vertical direction (p = 0.017). These results can be applicable to further studies regarding the spatial relationships between the center of the FAZ and the highest FB point in various macular diseases or previously-treated eyes.
To evaluate the automated determination of the center of an idiopathic macular hole (MH) by using swept-source optical coherence tomography (OCT) images with new macro-based algorithms in ImageJ and to compare the difference between the MH center measurements obtained automatically and manually. This cross-sectional study included 39 eyes of 39 elderly individuals (22 women, 17 men) with stage 3 and 4 MH. The MH center was automatically determined using the ImageJ macro. The foveal center was also manually identified by two masked examiners using horizontal and vertical serial B-scan OCT angiography images. The mean age was 68.8 ± 8.3 years. After adjusting for the effect of magnification, the mean distance between the MH center determined manually by Examiner 1 and that determined automatically was 15.5 ± 9.9 µm. The mean distance between the two manually determined measurements of the MH center was 20.3 ± 19.7 µm. These two mean distance values did not differ significantly (Welch t-test, p = 0.27) and was non-inferior (p < 0.0001). The automated ImageJ-based method for determining the MH center was comparable to manual methods. This study showed that automated measurements were non-inferior to manual measurements, and demonstrated a substitutable usefulness, at least for use in clinical practice.
The extraction of the foveal avascular zone (FAZ) from optical coherence tomography angiography (OCTA) images has been used in many studies in recent years due to its association with various ophthalmic diseases. In this study, we investigated the utility of a dataset for deep learning created using Kanno Saitama Macro (KSM), a program that automatically extracts the FAZ using swept-source OCTA. The test data included 40 eyes of 20 healthy volunteers. For training and validation, we used 257 eyes from 257 patients. The FAZ of the retinal surface image was extracted using KSM, and a dataset for FAZ extraction was created. Based on that dataset, we conducted a training test using a typical U-Net. Two examiners manually extracted the FAZ of the test data, and the results were used as gold standards to compare the Jaccard coefficients between examiners, and between each examiner and the U-Net. The Jaccard coefficient was 0.931 between examiner 1 and examiner 2, 0.951 between examiner 1 and the U-Net, and 0.933 between examiner 2 and the U-Net. The Jaccard coefficients were significantly better between examiner 1 and the U-Net than between examiner 1 and examiner 2 (p < 0.001). These data indicated that the dataset generated by KSM was as good as, if not better than, the agreement between examiners using the manual method. KSM may contribute to reducing the burden of annotation in deep learning.
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