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.

The fast development of imaging techniques in recent years has opened new diagnostic paths also in dermatology, where high-frequency ultrasound (HFUS) enables the visualization of superficial structures. At the same time, automated ultrasound image analysis algorithms have started to be widely described in the literature. Although the newest deep learning models can classify the images without the previous segmentation steps, they are often the first part of a computer-aided diagnosis framework that helps further measurements. For the clinical evaluation, the parameters of skin layers: entry echo, SLEB and dermis, are the most important for differential diagnosis and accurate evaluation of treatment process. The paper presents a novel neural network model combining contextual feature pyramid blocks with attention gates to segment skin layers accurately. In addition, a sequential model was tested that pre-segmented the entry echo layer as the most characteristic element in the skin ultrasound image. For the first time, we segmented three skin layers: the entry echo layer, SLEB, and dermis. The developed method is verified using two different HFUS image databases containing images acquired with different ultrasound machines and ultrasound probe frequencies. Measures of models' performance were proposed, assessing the percentage of cases where the model classified the whole image as background and two focusing on the SLEB layer: percentages of false positive and false negatives detections. The average Dice indexes, obtained on the dataset recorded for this study, were 0.95, 0.85 and 0.93, respectively for the entry echo, SLEB and dermis. For models trained without transfer learning, proposed architectures were the only ones that detected the skin correctly every time. Both models achieved the lowest false positive (0.35% and 0%) and false negative (4.48% and 3.66%) rates during the experiments. Contextual feature pyramid modules and attention gates allow more accurate detection and segmentation of skin layers. The results obtained are compared with other models described in the literature as efficient for HFUS image analysis, and low false positive and false negative rates speak in favor of our approach.

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.

Actinic Keratosis (AK) is a common skin condition, usually appearing on sun-exposed areas, whose progression is associated with characteristic dermatoscopic and structural changes. Early detection of AK is crucial, as cancer progression may occur in changed skin. This study aimed to develop a multimodal, machine-learning-based framework combining dermatoscopic and high-frequency ultrasound (HFUS) data to automatically stage AK and identify early lesions. A dataset containing 222 pairs of dermatoscopic and HFUS images was clinically evaluated using the 3-point Zalaudek scale. Dermatoscopic images underwent ROI selection, hair removal, and extensive feature extraction (color, erythema, pigmentation, vessels, scales, pixel intensities, GLCM/LBP texture). HFUS images were divided into entry echo, sub-epidermal low-echoic band (SLEB), and dermis using a deep neural network, and then features describing the morphology and structure of the skin for each layer were extracted. A pre-trained EfficientNet network was used for feature extraction. Logistic Regression, k-Nearest Neighbors, Random Forests, Support Vector Machines and Multilayer Perceptrons with Sequential Feature Selection using 5-fold patient-wise cross-validation were used for feature-based classification. Additionally, multimodal TwinCNN was evaluated, with various pre-trained models as feature extractors. Combining dermatoscopic and HFUS features consistently outperformed single-modality models. Depending on the defined task, the models achieved over 80% accuracy (healthy, AK1-AK3), 78% (AK1-AK3), and almost 90% in the case of early AK detection vs. healthy and advanced AK on multimodal features. The TwinCNN model performed worse than classical machine-learning approaches, likely due to the limited size of the dataset and class imbalance. A multimodal framework integrating dermatoscopic and HFUS imaging enables accurate AK classification, surpassing single-modality approaches. Future work should expand multicenter datasets, improve automation of pre-processing steps, and explore enhanced neural multimodal fusion architectures.