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Scanning Electronic Microscopy Evaluation of the Roughness of the Stromal Bed After Deep Corneal Cut with the LDV Femtosecond Laser (Z6) (Ziemer) and the ONE Microkeratome (Moria)

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Purpose : To compare the stromal bed surface quality and the accuracy of dissection depth after deep lamellar cuts using the Leonardo Da Vinci (LDV) femtosecond laser (Z6) and the ONE Microkeratome.

Description

Methods

Deep lamellar cuts were performed on nine human donor corneoscleral buttons: five with the LDV femtosecond (FS) laser (Z6) (Ziemer) and four with the ONE Microkeratome (MK) (Moria). Corneal thickness was measured with ultrasound pachymetry before and after the dissection. The Stromal bed quality was evaluated using light microscopy ( n = 4) and scanning electron microscopy (SEM) ( n = 9).

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The surface roughness on SEM images was graded on the scale of 1 (smoothest) to 5 (roughest) by four observers, blinded to the method used. Particle analysis on the SEM images was performed in order to have an objective measure of smoothness. Results : The achieved dissection depth using the FS laser was 496.4 ± 46.4 µm when attempting 500 µm and 474 ± 60 µm with the microkeratome when attempting 350 µm.

Histological evaluation of the corneoscleral buttons by both light and electron microscopy showed significantly smoother surface using the FS laser compared to the microkeratome. There were fewer and smaller particles observed in the SEM images of FS laser cut buttons ( p < 0.001).The average observer based score of anterior surface roughness (50×) was 2.2 for the FS laser and 3.9 for the microkeratome dissections ( p < 0.001).

Conclusions : The LDV femtosecond laser (Z6) platform is capable of creating deep corneal lamellar dissection with smoother surface quality and with more predictable cut depth as compared to the One Microkeratome.

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Medicine & Health
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Image 65% · Microscopy 75%

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Provenance · 1 source records, 16 field assertions
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DataCite10.6084/m9.figshare.225409914 d agoJSON v1
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concepts[field].local:field:medicine-healthmapping · DataCitevocabulary-mapper@1.0.0keywords['Medicine']
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concepts[modality].local:modality:microscopyenrichment · DataCitekeyword-concept-rules@1.0.0title+description (75%)
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