RESUMEN
With increasing population aging, osteoporosis vertebral compression fractures (OVCFs), resulting in severe back pain and functional impairment, have become progressively common. Percutaneous vertebroplasty (PVP) and percutaneous kyphoplasty (PKP) as minimally invasive procedures have revolutionized OVCFs treatment. However, PVP- and PKP-related complications, such as symptomatic cement leakage and adjacent vertebral fractures, continue to plague physicians. Consequently, progressively more implants for OVCFs have been developed recently to overcome the shortcomings of traditional procedures. Therefore, we conducted a literature review on several new implants for OVCFs, including StaXx FX, Vertebral Body Stenting, Vesselplasty, Sky Bone Expander, Kiva, Spine Jack, Osseofix, Optimesh, Jack, and V-strut. Additionally, this review highlights the individualized applications of these implants for OVCFs. Nevertheless, current clinical studies on these innovative implants remain limited. Future prospective, randomized, and controlled studies are needed to elucidate the effectiveness and indications of these new implants for OVCFs.
RESUMEN
Background: Numerous studies have attempted to apply artificial intelligence (AI) in the dermatological field, mainly on the classification and segmentation of various dermatoses. However, researches under real clinical settings are scarce. Objectives: This study was aimed to construct a novel framework based on deep learning trained by a dataset that represented the real clinical environment in a tertiary class hospital in China, for better adaptation of the AI application in clinical practice among Asian patients. Methods: Our dataset was composed of 13,603 dermatologist-labeled dermoscopic images, containing 14 categories of diseases, namely lichen planus (LP), rosacea (Rosa), viral warts (VW), acne vulgaris (AV), keloid and hypertrophic scar (KAHS), eczema and dermatitis (EAD), dermatofibroma (DF), seborrheic dermatitis (SD), seborrheic keratosis (SK), melanocytic nevus (MN), hemangioma (Hem), psoriasis (Pso), port wine stain (PWS), and basal cell carcinoma (BCC). In this study, we applied Google's EfficientNet-b4 with pre-trained weights on ImageNet as the backbone of our CNN architecture. The final fully-connected classification layer was replaced with 14 output neurons. We added seven auxiliary classifiers to each of the intermediate layer groups. The modified model was retrained with our dataset and implemented using Pytorch. We constructed saliency maps to visualize our network's attention area of input images for its prediction. To explore the visual characteristics of different clinical classes, we also examined the internal image features learned by the proposed framework using t-SNE (t-distributed Stochastic Neighbor Embedding). Results: Test results showed that the proposed framework achieved a high level of classification performance with an overall accuracy of 0.948, a sensitivity of 0.934 and a specificity of 0.950. We also compared the performance of our algorithm with three most widely used CNN models which showed our model outperformed existing models with the highest area under curve (AUC) of 0.985. We further compared this model with 280 board-certificated dermatologists, and results showed a comparable performance level in an 8-class diagnostic task. Conclusions: The proposed framework retrained by the dataset that represented the real clinical environment in our department could accurately classify most common dermatoses that we encountered during outpatient practice including infectious and inflammatory dermatoses, benign and malignant cutaneous tumors.
RESUMEN
The repair of hair-occluded information is one of the key problems for the precise segmentation and analysis of the skin malignant melanoma image with hairs. Aimed at dermoscopy images of pigmented skin lesions, an unsupervised repair algorithm for the hair-occluded information is proposed in this paper. This algorithm includes three steps: first, the melanoma image with hairs are enhanced by morphologic closing-based top-hat operator and then segmented through statistic threshold; second, the hairs are extracted based on the elongate of connected region; third, the hair-occluded information is repaired by the PDE-based image inpainting. As a matter of fact, with the morphologic closing-based top-hat operator both strong and weak hairs can be enhanced simultaneously, and the elongate state of band-like connected region can be correctly described by the elongate function proposed in this paper so as to measure the hair effectively. Therefore, the unsupervised repair problem of the hair-occluded information can be resolved very well through combining the hair extracting with the image inpainting technology. The experiment results show that the repaired images can satisfy the requirement of medical diagnosis by the proposed algorithm and the segmentation veracity is effectively improved after repairing the hair-occluded information.
