Purpose: The aim of this study was to evaluate and compare night vision and low-luminance contrast sensitivity (CS) in patients undergoing implantation of phakic collamer lenses or wavefront-optimized laser-assisted in situ keratomileusis (LASIK).
Patients And Methods: This is a nonrandomized, prospective study, in which 48 military personnel were recruited. Rabin Super Vision Test was used to compare the visual acuity and CS of Visian implantable collamer lens (ICL) and LASIK groups under normal and low light conditions, using a filter for simulated vision through night vision goggles.
Results: Preoperative mean spherical equivalent was -6.10 D in the ICL group and -6.04 D in the LASIK group (P=0.863). Three months postoperatively, super vision acuity (SVa), super vision acuity with (low-luminance) goggles (SVaG), super vision contrast (SVc), and super vision contrast with (low luminance) goggles (SVcG) significantly improved in the ICL and LASIK groups (P<0.001). Mean improvement in SVaG at 3 months postoperatively was statistically significantly greater in the ICL group than in the LASIK group (mean change [logarithm of the minimum angle of resolution, LogMAR]: ICL =-0.134, LASIK =-0.085; P=0.032). Mean improvements in SVc and SVcG were also statistically significantly greater in the ICL group than in the LASIK group (SVc mean change [logarithm of the CS, LogCS]: ICL =0.356, LASIK =0.209; P=0.018 and SVcG mean change [LogCS]: ICL =0.390, LASIK =0.259; P=0.024). Mean improvement in SVa at 3 months was comparable in both groups (P=0.154).
Conclusion: Simulated night vision improved with both ICL implantation and wavefront-optimized LASIK, but improvements were significantly greater with ICLs. These differences may be important in a military setting and may also affect satisfaction with civilian vision correction.
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http://dx.doi.org/10.2147/OPTH.S106120 | DOI Listing |
Sci Rep
December 2024
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December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
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December 2024
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October 2024
State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong, China.
Super-resolution (SR) of magnetic resonance imaging (MRI) is gaining increasing attention for being able to provide detailed anatomical information. However, current SR methods often use the complex convolutional network for feature extraction, which is difficult to train and not suitable for limited computation resources in the medical scenario. To tackle these bottlenecks, we propose a multi-distillation residual network (MDRN) for more differential feature refinement, which has a superior trade-off between reconstruction accuracy and computation cost.
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