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Riken to resume retinal iPS transplantation in cooperation with Kyoto University

The Japan Times

KOBE – The Japanese government-affiliated research institute Riken said Monday that it will resume its clinical study in which retinal tissues developed from iPS cells will be transplanted in an eye disease patient, in cooperation with Kyoto University and other medical institutes. In 2014, the Riken Center for Developmental Biology, or CDB, successfully conducted a retinal transplant using induced pluripotent stem cells for the first time in the world. But its second trial was suspended due to a gene abnormality found in iPS cells. In the first trial, iPS cells were created from cells taken from the patient who underwent the transplant. Next time, the study team, led by Masayo Takahashi, project leader at the CDB, plans to use iPS cells created from mature cells of others, since the first operation proved using a patient's own cells is time-consuming and costly.


Novel system uses AI to detect abnormalities in fetal hearts

#artificialintelligence

A research group led by scientists from the RIKEN Center for Advanced Intelligence Project (AIP) have developed a novel system that can automatically detect abnormalities in fetal hearts in real-time using artificial intelligence (AI). This technology could help examiners to avoid missing severe and complex congenital heart abnormalities that require prompt treatments, leading to early diagnosis and well-planned treatment plans, and could contribute to the development of perinatal or neonatal medicine. Congenital heart problems -- which can involve abnormalities of the atrium, ventricle, valves or blood vessel connections -- can be very serious, and account for about 20% of all newborn deaths. Diagnosis of such problems before the baby is born, allowing for prompt treatment within a week after birth, is known to markedly improve the prognosis, so there have been many attempts to develop technology to enables accurate and rapid diagnosis. However, today, fetal diagnosis depends heavily on observations by experienced examiners using ultrasound imaging, so it is unfortunately not uncommon for children to be born without having been properly diagnosed.


Artificial Intelligence technology to detect diabetes retinopathy

#artificialintelligence

Dubai: In a bid to curb the rising incidence of diabetes, the Dubai Diabetes Centre (DDC) plans to introduce Artificial Intelligence to detect retinopathy, start tele-monitoring of patients who miss their appointments and also introduce obesity clinics in the emirate. Elaborating on the use of Artificial Intelligence Dr M Hamed Farooqi, director of the multidisciplinary centre said: "As per international diabetes standards, we need to have 14 retinal images per diabetic. The estimated number of diagnosed diabetics in the UAE exceeds one million. To interpret 14 million images per year, we need more than 50 eye specialists working full-time. Deep learning system (DLS) using Artificial Intelligence are capable of identifying diabetic retinopathy and related eye diseases using retinal images with a high degree of accuracy.


Retinal Vessel Segmentation Algorithm based on Residual Convolution Neural Network

#artificialintelligence

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal blood vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90% and 96.88%, and the average specificity is 98.85% and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.


Smartphone App And Deep Learning Help Detect Diabetes

#artificialintelligence

Diabetes is one of the world's top causes of disease and death, affecting more than 450 million people worldwide. While technology has come a long way in helping to detect and manage diabetes, it still typically involves blood draws and clinical tools. Moreover, around half of all people with diabetes aren't even aware that they have the disease. Researchers at UC San Francisco have now come up with a promising method of detecting diabetes using a smartphone camera and some deep learning, utilizing the publicly available Instant Heart Rate app from Azumio to capture photoplethysmography (PPG) measurements. When a user places his or her fingertip over the phone's flashlight and camera, the app measures PPG's by capturing color changes in the fingertip corresponding to each heartbeat. This data is reported back to the user as the instantaneous heart rate.