Objectives: Actinic keratoses (AKs) are one of the most common reasons for consultation in the elderly population. This study aimed to assess the efficacy of 5-ALA PDT in AK treatment using high-frequency ultrasonography (HFUS) to evaluate skin layer changes during therapy. Methods: In our study, we included 44 AK patients aged 53 to 89 years. All patients had lesions clinically evaluated with the Olsen and AKASI scale. HFUS imaging was performed on seemingly healthy skin and lesions before and at 4, 8, and 12 weeks of therapy. Ultrasound markers such as skin thickness, echogenicity, and pixel intensity were measured. 5-ALA was applied under occlusion for 3 h. After removing the occlusive dressing, 5-ALA was removed with a saline solution and a directed therapy with a BF-200 lamp. Full follow-ups of 56 markers of suitable quality were selected. Results: The thickness of SLEB significantly decreased in the following weeks compared to the pre-therapy results, reaching its lowest values after 12 weeks. The average pixel intensity significantly increased in each skin layer after therapy (p < 0.01). For SLEB, there were statistically significant differences in LEP, MEP and contrast. The AKASI score before and after treatment was determined for the 39 patients who underwent follow-up at week 12. The median AKASI score was 3.2 (1.2–8.6) before treatment and 0.6 (0–2.8) after. Conclusions: According to the literature data, this is the first study describing the ALA-PDT treatment efficacy in different AK severities evaluated in HFUS. HFUS provides a valuable non-invasive tool for monitoring the efficacy of PDT in AK treatment, showing significant improvements in skin texture and structure.
Background: Actinic keratoses (AK) usually occur on sun-exposed areas in elderly patients with Fitzpatrick I–II skin types. Dermatoscopy and ultrasonography are two non-invasive tools helpful in examining clinically suspicious lesions. This study presents the usefulness of image-processing algorithms in AK staging based on dermatoscopic and ultrasonographic images. Methods: In 54 patients treated at the Department of Dermatology of Poznan University of Medical Sciences, clinical, dermatoscopic, and ultrasound examinations were performed. The clinico-dermoscopic AK classification was based on three-point Zalaudek scale. The ultrasound images were recorded with DermaScan C, Cortex Technology device, 20 MHz. The dataset consisted of 162 image pairs. The developed algorithm includes automated segmentation of ultrasound data utilizing a CFPNet-M model followed by handcrafted feature extraction. The dermatoscopic image analysis includes both handcrafted and convolutional neural network features, which, combined with ultrasound descriptors, are used in support vector machine-based classification. The network models were trained on public datasets. The influence of each modality on the final classification was evaluated. Results: The most promising results were obtained for the dermatoscopic analysis with the use of neural network model (accuracy 81%) and its combination with ultrasound scans (accuracy 79%). Conclusions: The application of machine learning-based algorithms in dermatoscopic and ultrasound image analysis machine learning in the staging of AKs may be beneficial in clinical practice in terms of predicting the risk of progression. Further experiments are warranted, as incorporating more images is likely to improve classification accuracy of the system.
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