App surpasses the ‘gold standard’ of sensitivity, Baylor University researchers say
A Baylor University researcher’s prototype smartphone app — designed to help parents detect early signs of various eye diseases in their children such as retinoblastoma, an aggressive pediatric eye cancer — has passed its first big test.
The CRADLE app (ComputeR Assisted Detector LEukocoia) searches for traces of abnormal reflections from the retina called leukocoria or “white eye,” a primary symptom of retinoblastoma, as well as other common eye disorders. The study, published in the journal Science Advances, found the app is an effective tool to augment clinical leukocoria screenings, allowing parents to efficiently and effectively screen their children more often throughout their development.
CRADLE — developed by Baylor University researchers Bryan F. Shaw, Ph.D., associate professor of chemistry and biochemistry, along with Greg Hamerly, Ph.D., associate professor of computer science — searches through family photographs for signs of leukocoria.
According to the study’s first author, Baylor senior University Scholar Micheal Munson, researchers determined the sensitivity, specificity and accuracy of the prototype by analyzing more than 50,000 photographs of children taken prior to their diagnosis. For children with diagnosed eye disorders, CRADLE was able to detect leukocoria for 80 percent of the children. The app detected leukocoria in photos that were taken on average of 1.3 years prior to their official diagnosis.
The effectiveness of traditional screenings during a general physical exam is limited, with signs of retinoblastoma via the detection of leukocoria in only 8 percent of cases. CRADLE’s sensitivity for children age 2 and younger surpassed 80 percent. That 80 percent threshold is regarded by ophthalmologists as the ‘‘gold standard” of sensitivity for similar devices, Munson said.
Researchers found the CRADLE app to be more effective simply by the breadth and frequency of its sample sizes: everyday family photos, according to the study. Given the number of photos taken by family and friends and the variety of environments, there is a variety of opportunities for light to reflect off the ocular lesions regardless of its location in the eye.
As the app’s algorithm has become more sophisticated, its ability to detect even slight instances of leukocoria has improved.
“This is one of the most critical parts of building the app,” Shaw said. “We wanted to be able to detect all hues and intensities of leukocoria. As a parent of a child with retinoblastoma, I am especially interested in detecting the traces of leukocoria that appear as a ‘gray’ pupil and are difficult to detect with the naked eye.”
Initially, the CRADLE app was used primarily to identify retinoblastoma — a rare eye disease that is the most common form of eye cancer in children up to age 5. Shaw’s own experience as a parent of a child with retinoblastoma formed the genesis of the app.
Shaw and Hamerly created the app in 2014 for the iPhone and in 2015 for Android devices after Shaw’s son, Noah, lost his right eye, but his left eye was able to be salvaged. He is now 11.
“We suspected that the app would detect leukocoria associated with other more common disorders and some rare ones,” Shaw said. “We were right. So far parents, and some doctors, have used it to detect cataract, myelin retinal nerve fiber layer, refractive error, Coats’ disease, and of course retinoblastoma.”
Lead author Munson is a prime example of Baylor’s focus on meaningful undergraduate research experiences that take place alongside Baylor faculty. In the spring, he received the Goldwater Scholarship from the Barry M. Goldwater Scholarship and Excellence in Education Program, one of the top undergraduate scholarships given in the natural sciences, engineering and mathematics Munson is concentrating his studies in biochemistry and applied mathematics, and along with conducting research with Shaw, has spent three summers at Johns Hopkins School of Medicine, where he studied type II pulmonary arterial hypertension.
“Mike (Munson) just knocked on my door and asked for a job,” Shaw said. “After he had worked about six months on the project, I handed him the reins.
“Baylor really is in the Goldilocks zone of undergraduate research,” Shaw said. “We are not too big that they fall through the cracks and we’re not too small that they don’t have good projects to work on.”
“I just kept the goal in mind: saving the sight and potentially the lives of children throughout the world,” Munson said.
Shaw said they are retraining the algorithm with Baylor undergraduates currently tagging and sorting about 100,000 additional photos. He said they also are looking at additional features to cut down on false positive detections.
The app can be downloaded for free and can be found under the name “White Eye Detector.”
The Latest on: ComputeR Assisted Detector LEukocoia
via Google News
The Latest on: ComputeR Assisted Detector LEukocoia
- AI runs smack up against a big data problem in COVID-19 diagnosison April 4, 2020 at 10:51 am
Help the radiologists It's simple in theory to identify what a computer should look for. An X-ray or a CT scan will show formations ... That pipeline that integrated the AI with the radiologist's ...
- CDx Diagnostics Announces Contract with Blue Cross Blue Shield of Massachusetts for WATS3D Biopsyon April 2, 2020 at 5:00 am
Newly issued coverage statement on diagnostic platform for screening and surveillance of Barrett's esophagus - SUFFERN, N.Y., April 02, 2020 (GLOBE NEWSWIRE) -- Effective ...
- How to quickly and efficiently identify huge gene data sets to help coronavirus researchon March 31, 2020 at 6:24 am
The new method can also be used to detect hereditary diseases in humans or to determine genetic abnormalities, according to the same news release. "Over the next few years, we want to develop new ...
- Clinical Chemistry Analyzers Market To Reach USD 16.81 Billion By 2027on March 30, 2020 at 8:09 am
Technological advancements in clinical chemistry analyzers such as StaRRsed Inversa automated erythrocyte sedimentation rate (ESR) analyzer, computer-assisted interpretation, and artificial ...
- DIAGNOS Announces Stock Options Granton March 27, 2020 at 12:06 pm
Diagnos Inc. (“DIAGNOS” or “the Corporation”) (TSX Venture: ADK) (OTCQB: DGNOF), a leader in early detection of critic ...
- Role of mechanical cues and hypoxia on the growth of tumor cells in strong and weak confinement: A dual in vitro–in silico approachon March 25, 2020 at 12:00 pm
Growth kinetics is then monitored by time-lapse phase-contrast microscopy, and a home-made contour detection software is used to derive the average geometrical ... Earketyp 3D, France) using the ...
- Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materialson March 23, 2020 at 3:16 am
The recent advances in deep-learning technologies based on neural networks have led to the emergence of high-performance algorithms for interpreting images, such as object detection 1,2,3,4,5, ...
- Huawei launches an AI assisted Diagnostic service for faster Coronavirus detectionon March 18, 2020 at 3:51 am
Amidst the Coronavirus Pandemic, Huawei has recently developed a new AI assisted quantitative ... such as computer vision and medical image analysis to automatically detect the virus quickly ...
- Deep learning models for electrocardiograms are susceptible to adversarial attackon March 8, 2020 at 5:00 pm
Cardiologist-level arrhythmia detection and classification in ambulatory ... In International Conference on Medical Image Computing and Computer-Assisted Intervention 493–501 (Springer, 2018).
- Computer Assisted Coding (CAC) Systems Marketon March 6, 2020 at 4:00 pm
utm_source=openpr&utm_medium=Arshad Through the statistical analysis, the report describes the total global market of the computer assisted coding (CAC) systems sector, including efficiency ...
via Bing News