Blood Perfusion in Rotational Full-Thickness Lower Eyelid Flaps Measured by Laser Speckle Contrast Imaging.

PURPOSE: Large upper eyelid defects can be repaired by rotational full-thickness lower eyelid flaps. The aim was to measure the blood perfusion in such flaps, and how it is affected by the length of the flaps, and the degree of rotation and stretching. METHODS: Nine patients underwent the Quickert procedure for entropion repair in which a full-thickness eyelid flap of approximate width 0.5 cm and length 2 cm was dissected in the lower eyelid. This generates a full-thickness eyelid flap similar to that used to repair large upper eyelid defects. Perfusion was measured using laser speckle contrast imaging, before and after the flap was rotated 90° and 120°, and stretched using forces of 0.5, 1, and 2 N. RESULTS: Blood perfusion decreased gradually from the base to the tip of the flap; being 75% of the reference value 0.5 cm from the base, 63% at 1.0 cm, 55% at 1.5, 23% at 1.75 cm, and 4% at 2.0 cm. Rotating the flaps by 90° or 120° had little effect on the perfusion. Stretching reduced the perfusion from 63% to 32% at 2 N, when measured at 1 cm. The combination of stretching and rotation did not lead to any further decrease. CONCLUSIONS: Blood perfusion in lower eyelid rotational flaps seems to be more sensitive to stretching than to rotation. Provided the flap is no longer than 1.5 cm, the perfusion will be greater than 20%, even when rotated, which should be sufficient for adequate survival and healing.

Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification.

OBJECTIVES: An extended-wavelength diffuse reflectance spectroscopy (EWDRS) technique was evaluated for its ability to differentiate between and classify different skin and tissue types in an in vivo pig model. MATERIALS AND METHODS: EWDRS recordings (450-1550 nm) were made on skin with different degrees of pigmentation as well as on the pig snout and tongue. The recordings were used to train a support vector machine to identify and classify the different skin and tissue types. RESULTS: The resulting EWDRS curves for each skin and tissue type had a unique profile. The support vector machine was able to classify each skin and tissue type with an overall accuracy of 98.2%. The sensitivity and specificity were between 96.4 and 100.0% for all skin and tissue types. CONCLUSION: EWDRS can be used in vivo to differentiate between different skin and tissue types with good accuracy. Further development of the technique may potentially lead to a novel diagnostic tool for e.g. non-invasive tumor margin delineation.