Recent developments in the application of lignocellulosic materials for oil spill removal are discussed in this review article. The types of lignocellulosic substrate material and their different chemical and physical modification strategies and basic preparation techniques are presented. The morphological features and the related separation mechanisms of the materials are summarized. The material types were classified into 3D-materials such as hydrophobic and oleophobic sponges and aerogels, or 2D-materials such as membranes, fabrics, films, and meshes. It was found that, particularly for 3D-materials, there is a clear correlation between the material properties, mainly porosity and density, and their absorption performance. Furthermore, it was shown that nanocellulosic precursors are not exclusively suitable to achieve competitive porosity and therefore absorption performance, but also bulk cellulose materials. This finding could lead to developments in cost- and energy-efficient production processes of future lignocellulosic oil spillage removal materials.

This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb Angle (CA) from Surface Topography (ST) scans in adolescents with Idiopathic Scoliosis (AIS). The aim is to minimize radiation exposure during critical growth periods by offering a reliable, non-invasive assessment tool. The efficacy of various CNN-based feature extractors-DenseNet121, EfficientNetB0, ResNet18, SqueezeNet, and a modified U-Net-was evaluated on a dataset of 654 ST scans using a regression analysis framework for accurate CA prediction. The dataset comprised 590 training and 64 testing scans. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and accuracy in classifying scoliosis severity (mild, moderate, severe) based on CA measurements. The EfficientNetB0 feature extractor outperformed other models, demonstrating strong performance on the training set (R=0.96, R=20.93) and achieving an MAE of 6.13^∘ and RMSE of 7.5^∘ on the test set. In terms of scoliosis severity classification, it achieved high precision (84.62%) and specificity (95.65% for mild cases and 82.98% for severe cases), highlighting its clinical applicability in AIS management. The regression-based approach using the EfficientNetB0 as a feature extractor presents a significant advancement for accurately determining CA from ST scans, offering a promising tool for improving scoliosis severity categorization and management in adolescents.

Background: Idiopathic scoliosis (IS) is a common spinal deformity affecting 0.5% to 5.2% of children worldwide, with a higher reported range in Spain (0.7–7.5%). Early detection through screening is crucial to prevent the progression of mild cases to severe deformities. Clinical methods such as the ADAM test and trunk rotation angle (TRA) are widely used, but the development of three-dimensional (3D) surface topography (ST) technologies has opened new avenues for non-invasive screening. The objectives of this study were (1) to perform clinical and ST-based scoliosis screening in a sample of healthy children involved in club sports, (2) to estimate the agreement between clinical and ST screening methods, (3) to describe the prevalence of scoliosis by sport, sex, and age, and (4) to evaluate the diagnostic performance of both screening approaches using available radiographs as a reference standard. Methods: A total of 343 children (58.7% males, 41.3% females; mean age 11.69 ± 2.05 years) were screened using both clinical and ST methods. Clinical screening included the ADAM test and TRA measurement, while ST screening was performed using BackSCNR^®, a markerless 3D scanning software. The children with positive screening results were recommended to obtain radiographs to confirm the diagnosis. Kappa agreement, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for both screening modalities using radiographic results as the gold standard. Results: The prevalence of scoliosis was 3.2% (n = 11) based on radiographic confirmation. The prevalence by sport was highest in swimming (17.6%), with minimal differences by sex (males 3.6%, females 2.5%). The clinical screening showed a sensitivity of 73%, specificity of 97%, PPV of 47%, NPV of 99%, and accuracy of 96%. The ST screening showed a sensitivity of 36%, specificity of 99%, PPV of 80%, NPV of 97%, and accuracy of 97%. The kappa values indicate a moderate influence of chance for both methods (clinical κ = 0.55; ST κ = 0.48). The balanced accuracy was 84% for the clinical screening and 68% for the ST screening. Conclusions: The clinical screening method showed superior sensitivity and balanced accuracy compared to ST screening. However, ST screening showed higher specificity and PPV, suggesting its potential as a complementary tool to reduce the high positive predictive value. These results highlight the importance of combining screening methods to improve the accuracy of the early detection of IS in physically active children, with the radiographic confirmation of the positive screened cases remaining essential for accurate diagnosis.