Dermoscopy is a useful tool for observing the vascular profile of port-wine stain (PWS) birthmarks. However, due to the complicity of the vascular profile, there is a lack of consensus on the classification of dermoscopic features of PWS vessels. This study investigated the potentials of deep learning-assisted methods in the classification of dermoscopy image-based of PWS vascular profiles. The classified images were used as training samples, and the RegNet network with better classification effect was selected to establish the migration learning method. The results showed that the accuracy of the RegNet network on the validation set was 82.63%. The preliminary study suggests that deep learning assisted PWS vascular contour type classification is feasible.
Hemporfin is asecond generation photosensitizerand has been used in combination with laser and LED to treat vascular and cancerousdiseases in China. In this study, four different light sources were evaluated for Hemoporfin PDT. Photobleaching experiments were carried out by exposing Hemoporfin solution to green diode laser, red diode laser, green LED, and red LED, respectively, under the same power density (30 mW/cm2) for different lengths of time. Hemoporfin fluorescence was measured before and after light exposure. Photobleaching kinetics showed the order of greenlaser > green LED > red laser> red LED. This study suggests that all tested light sources could be used for Hemoporfin PDTbut the power density and exposure time of different light sources need to be adjusted in order to achieve the same levels of photodynamic effect.
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