View clinical trials related to Colposcopy.
Filter by:To investigate the effect of coughing as an intervention to reduce pain in colposcopy guided biopsy.
The application of artificial intelligence in image recognition of cervical lesions diagnosis has become a research hotspot in recent years. The analysis and interpretation of colposcopy images play an important role in the diagnosis，prevention and treatment of cervical precancerous lesions and cervical cancer. At present, the accuracy of colposcopy detection is still affected by many factors. The research on the diagnosis system of cervical lesions based on multimodal deep learning of colposcopy images is a new and significant research topic. Based on the large database of cervical lesions diagnosis images and non-images, the research group established a multi-source heterogeneous cervical lesion diagnosis big data platform of non-image and image data. Research the lesions segmentation and classification model of colposcopy image based on convolutional neural network, explore the relevant medical data fusion network model that affects the diagnosis of cervical lesions, and realize a multi-modal self-learning artificial intelligence cervical lesion diagnosis system based on colposcopy images. The application efficiency of the artificial intelligence system in the real world was explored through the cohort, and the intelligent teaching model and method of cervical lesion diagnosis were further established based on the above intelligent system.
This randomized controlled trial evaluates the learning effectiveness of three different types of Colposcopy simulators objectively with Objective Structured Assessment of Technical Skill (OSATS) and subjectively with self-reported confidence survey. A total of 60 participants, randomly assigned into 3 groups, will learn from Hefler's, Reeve's or authors' simulators. The investigators hypothesize that the proposed simulator affords better learning objectively and subjectively with improved functional fidelity.