Diabetes retinal screening through deep learning
Ubotica has developed a deep learning-based solution for detecting the presence of diabetic retinopathy indicators in retinal images.
Ubotica has developed a deep learning-based solution for detecting the presence of diabetic retinopathy indicators in retinal images.
Researchers have developed an AI platform that could one day be used in a system to assess vascular and eye diseases.
The advent of electronic medical records with large image databases, along with advances in AI with deep learning, is offering medical professionals new opportunities to improve image analysis and disease diagnostics.
Engineers and ophthalmologists have developed a robotic imaging tool that can automatically detect and scan a patient's eyes for markers of different eye diseases.
This overview introduces smart insulin delivery systems and more innovations that help patients and doctors guide decision-making in diabetes care.
Although some artificial intelligence software tested reasonably well, only one met the performance of human screeners.
Researchers created a novel deep learning method that makes automated screenings for eye diseases such as diabetic retinopathy more efficient.
Researchers show how they can make an AI show how it's working, as well as let it diagnose more like a doctor, thus making AI-systems more relevant to clinical practice.
Researchers developed wirelessly driven ‘smart contact lens’ technology that can detect diabetes and further treat diabetic retinopathy just by wearing them.
Physicians have been using automatic digital retinal screening, without assistance from an ophthalmologist, to detect diabetic retinal disease.
Organ-on-a-chip technology has the potential to revolutionize drug development. Researchers have succeeded in putting various types of tissue onto chips.
Researchers have developed a deep learning system that may help detecting diabetic eye diseases, which could make doctors’ work easier and reduce healthcare cost.
Biomedical engineers have developed a portable optical coherence tomography scanner that promises to bring the vision-saving technology to underserved regions.
Pairing a smartphone to capture retinal images with an AI may offer a solution for better screening for diabetic retinopathy.