View clinical trials related to Automatic Judgement.
Filter by:Detecting the cause of keratitis fast is the premise of providing targeted therapy for reducing vision loss and preventing severe complications. Due to overlapping inflammatory features, even expert cornea specialists have relatively poor performance in the identification of causative pathogen of infectious keraitis. In this project, the investigators aim to develop an automated and accurate deep learning system to discriminate among bacterial, fungal, viral, amebic and noninfectious keratitis based on slit-lamp images and evaluated this system using the datasets obtained from mutiple independent clinical centers across China.
The aim of the study is to develop a computer program which is able to classify different entities of colorectal polyps on the basis of optical polyp features. In the end, the computer program shall differentiate between (i) hypeplastic polyps, (ii) adenomas and (iii) serrated adenomas . In the first phase of the study a computer program will be established which aims to distinguish between the above mentions entities on the basis of optical features derived from still images. A machine learning apporach will be used for creating the program. Afterwards, in a second phase of the study, still images of 100 polyps (not used in the first phase) will be presented to the computer program. Quality of the computer program will be tested by calculating the accuracy for differentiating the three different polyp types. The gold standard for true polyp diagnoses will be based on histopathological diagnoses of the polyps. The same pictures of 100 polyps will also be presented to human experts. Experts will also predict histopathological diagnoses on the basis of optical polyps featurs. Accuracy of computer-decisions and human expert predictions will be compared. The establishment of a well- functioning computer program is the primary aim of the study.