View clinical trials related to Ophthalmological Disorder.
Filter by:To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images. The system can analyze multimodal ocular fundus images, make diagnoses and generate corresponding reports.
Strabismus affects approximately 0.8%-6.8% of the world's population and appears by the age of 3 years in 65% of affected individuals. Manual measurement of deviation is often laborious and highly dependent on the experience of the specialist and the cooperation of the patients. Current strabismus evaluation technologies are heavily dependent on model eyes. Here, the investigators use deep learning to develop an artificial intelligence (AI) platform consisting of three deep learning (DL) systems to screen strabismus, evaluate deviation and propose a surgical plan based on corneal light-reflection photos. The investigator also conduct clinical trial to validate its versatility in clinical practice.
Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.