Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

Accurate preoperative lymph node (LN) assessment is crucial for patients with intrahepatic cholangiocarcinoma (iCCA) and perihilar cholangiocarcinoma (pCCA) because presence of LN metastases significantly reduces survival rates and can contraindicate surgical resection. Endoscopic ultrasound (EUS) provides a reliable method for LN assessment with the advantage of enabling tissue acquisition for pathological confirmation. This study aimed to assess interobserver agreement among endosonographers in evaluating LN characteristics in patients with iCCA and pCCA.A cross-sectional survey study was conducted among 24 endosonographers. Participants reviewed 42 EUS images from iCCA and pCCA patients, classifying LNs based on six characteristics (demarcation, shape, echogenicity, homogeneity, suspiciousness, and need to retrieve tissue). Interobserver agreement was determined using Light’s kappa statistics. Accuracy, sensitivity, and specificity in identifying malignant LNs were calculated.Overall kappa values indicated moderate to fair agreement on LN characteristics, with Kappa values of 0.24 for demarcation, 0.45 for shape, 0.38 for echogenicity, 0.52 for homogeneity, and 0.36 for suspiciousness. Overall accuracy of endosonographers in correctly identifying malignant LNs was 62%, with individual accuracy ranging from 44 to 75%. Sensitivity was 60% (range: 29%–90%) and specificity was 64% (range: 28%–89%).Endosonographic assessment of LN morphology and characterization demonstrates considerable variability among endosonographers. Thus, there is a clear need for standardization in preoperative LN evaluation, including establishing consensus about when to perform tissue acquisition, based on objective criteria such as short-axis diameter. Further research is required to refine and optimize these guidelines.