We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




Artificial Intelligence Helps Detect Rare Diseases

By HospiMedica International staff writers
Posted on 18 Jun 2019
Print article
A child with Kabuki Syndrome (Photo courtesy of Wikimedia).
A child with Kabuki Syndrome (Photo courtesy of Wikimedia).
A new study suggests that an artificial intelligence (AI) neural network can be used to automatically combine portrait photos and genetic data to diagnose rare diseases more efficiently.

The prioritization of exome data by image analysis (PEDIA) project, under development by the University of Bonn (Germany), GeneTalk (Bonn, Germany), Charité University Medicine (Charité; Berlin, Germany), and other institutions, is designed to interpret exome data by analyzing sequence variants in portrait photographs, and integrating the results using the DeepGestalt phenotyping tool, a product of FDNA (Herzliya, Israel), which was trained with around 30,000 portrait pictures of people affected by rare syndromal diseases.

In a proof of concept study, the researchers measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each separate case, frontal photos, clinical features, and the disease-causing variants were submitted. The results showed that computer-assisted analysis of frontal photos improved the top 1% accuracy rate by more than 20–89%, and the top 10% accuracy rate by more than 5–99% for the disease-causing gene. The study was published on June 5, 2019, in Nature Genetics in Medicine.

“In combination with facial analysis, it is possible to filter out the decisive genetic factors and prioritize genes. Merging data in the neuronal network reduces data analysis time and leads to a higher rate of diagnosis,” said senior author Professor Peter Krawitz, MD, PhD, director of the Institute for Genomic Statistics and Bioinformatics at the University of Bonn. “This is of great scientific interest to us and also enables us to find a cause in some unsolved cases.”

“PEDIA is a unique example of next-generation phenotyping technologies,” said Dekel Gelbman, CEO of FDNA. “Integrating an advanced AI and facial analysis framework such as DeepGestalt into the variant analysis workflow will result in a new paradigm for superior genetic testing.”

Many rare diseases cause characteristic abnormal facial features in those affected, such as Kabuki syndrome, which is reminiscent of the make-up of a traditional Japanese form of theatre. The eyebrows are arched, the eye-distance is wide and the spaces between the eyelids are long. Another example is mucopolysaccharidosis, which leads to bone deformation, stunted growth, and learning difficulties. Such phenotype information has so far only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.


Related Links:
University of Bonn
GeneTalk
Charité University Medicine
FDNA


Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Thyroid Shield
Standard Thyroid Shield

Print article

Channels

Critical Care

view channel
Image: Researchers have developed a novel risk score for cardiovascular complications after bone marrow transplant (Photo courtesy of 123RF)

Novel Tool Predicts Cardiovascular Risks after Bone Marrow Transplantation

Every year, thousands of people undergo bone marrow transplants to potentially cure serious diseases like leukemia, lymphoma, and immune deficiency disorders. While these transplants can be lifesaving,... Read more

Surgical Techniques

view channel
Image: The Early Bird Bleed Monitoring System provides visual and audible indicators of the onset and progression of bleeding events (Photo courtesy of Saranas)

Novel Technology Monitors and Lowers Bleeding Complications in Patients Undergoing Heart Procedures

Bleeding complications at the femoral access site can significantly hamper recovery, affecting the success of procedures, patient satisfaction, and overall healthcare costs. It is crucial for surgeons... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The new eye-safe laser technology can diagnose traumatic brain injury (Photo courtesy of 123RF)

Novel Diagnostic Hand-Held Device Detects Known Biomarkers for Traumatic Brain Injury

The growing need for prompt and efficient diagnosis of traumatic brain injury (TBI), a major cause of mortality globally, has spurred the development of innovative diagnostic technologies.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.