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AIM: The aim of this study was to study the effect of neodymium-doped yttrium aluminum garnet (Nd:YAG) laser iridotomy on corneal endothelial cell count in patients with subacute angle closure using specular microscope. MATERIALS AND METHODS: In this prospective study, 50 cases of narrow-angle Grade 1 and Grade 2 (Shaffer gonioscopic grading) visiting the Regional Institute of Ophthalmology, Government Medical College, Amritsar underwent Nd:YAG laser peripheral iridotomy. After obtaining informed written consent, specular microscopy was performed before and after iridotomy at 1 week, 1 month, 3 rd month, and at 6 th -month follow-up visits. Central, nasal, and temporal endothelial cell counts were evaluated through noncontact specular microscopy. RESULTS: The mean participant age was 51.52 ± 7.9 years, and majority of the participants were females (76%). The mean IOP before the laser was 19.25 ± 1.914 mmHg and it varied from 18.50 ± 1.647 to 18.25 ± 1.699 mmHg (day 1, p = 0.06 and at 6 months, p = 0.04) following laser procedure. The mean corneal endothelial cell count at superotemporal site before laser peripheral iridotomy was 2844 ± 260, and this value decreased to 2807 ± 263, 2699 ± 267, 2656 ± 270, and 2591 ± 275 cells/mm 2 at postiridotomy, 1, 3, and 6 months’ follow-up visits, respectively; these differences were statistically significant ( p < 0.05). The mean total energy required to produce iridotomy was 14.88 ± 6.71 mJ, ranging from 5 to 37 mJ. The linear regression analysis indicated no statistical correlation between change in endothelial cell count at the treated site and total mean energy used. No significant difference was found between preiridotomy and postiridotomy corneal thickness at any site. CONCLUSION: This study demonstrated a significant endothelial cell loss at the treated site in 6 months’ follow-up and suggested that Nd:YAG laser iridotomy may pose hazard to the corneal endothelium, although corneal decompensation at the treated site or as a whole was not seen.

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The physiological biometrics like face is combined with behavioral biometrics like speech to achieve the robustness of fusion process of a multimodal system. The selection of the biometrics is dependent on the robustness and uniqueness of the biometric. That is why, the selection of these two biometrics is done in this work. Mel Frequency Cepstral Coefficients has been utilized for speech feature extraction and in addition to this fuzzy logic is also utilized for training purpose. Then, the optimized features values are reduced using genetic algorithm. In the end, fusion is achieved by combination of fuse values obtained from both 2 biometrics. The whole simulation is tested in MATLAB environment.