Facial analysis improves diagnosis
Researchers use artificial intelligence to detect rare diseases even more accurately

Date:
February 10, 2022
Source:
University of Bonn
Summary:
Rare genetic diseases can sometimes be recognized through facial
features, such as characteristically shaped brows, nose or cheeks.

Researchers have now trained software that uses portrait photos to
better diagnose such diseases. The improved version "GestaltMatcher"
can now also detect diseases that are not yet known to it. It
also manages to diagnose known diseases with very small numbers
of patients.



FULL STORY ==========================================================================
Many sufferers of rare diseases endure an odyssey until the correct
diagnosis is made. "The goal is to detect such diseases at an early
stage and initiate appropriate therapy as soon as possible," says
Prof. Dr. Peter Krawitz from the Institute for Genomic Statistics and Bioinformatics (IGSB) at the University Hospital Bonn (Germany). The
researcher is a member of the Cluster of Excellence ImmunoSensation2 and
the Transdisciplinary Research Area "Modelling" at the University of Bonn.


==========================================================================
The majority of rare diseases are genetic. The underlying hereditary
mutations often cause varying degrees of impairment in different areas of
the body. In most cases, these hereditary changes are also expressed by characteristic facial features: for example, because eyebrows, the base
of the nose or the cheeks are shaped in a distinctive way. However, this
varies from disease to disease. Artificial intelligence (AI) uses these
facial characteristics, calculates the similarities, and automatically
links them to clinical symptoms and genetic data of patients. "The face provides us with a starting point for diagnosis," says Tzung-Chien Hsieh
of Krawitz's team. "It is possible to calculate what the disease is with a
high degree of accuracy." "GestaltMatcher" requires only a few patients
The AI system "GestaltMatcher" described in the current publication is a continued development of "DeepGestalt," which the IGSB team trained with
other institutions a few years ago. While DeepGestalt still required
about ten non- related affected persons as a reference for training,
its successor "GestaltMatcher" requires significantly fewer patients
for feature matching.

This is a great advantage in the group of very rare diseases, where only
a few patients are reported worldwide. Furthermore, the new AI system also considers similarities with patients who have also not yet been diagnosed,
and thus combinations of characteristics that have not yet been described.

GestaltMatcher therefore also "recognizes" diseases that were previously unknown to it and suggests diagnoses based on this. "This means we can
now classify previously unknown diseases, search for other cases and
provide clues as to the molecular basis," says Krawitz.

The team used 17,560 patient photos, most of which came from digital
health company FDNA, which the research team worked with developing
the web service through which the AI can be used. Around 5,000 of the
photos and patient data were contributed by the research team at the
Institute of Human Genetics at the University of Bonn, along with nine
other university sites in Germany and abroad. The researchers focused
on disease patterns that were as diverse as possible. They were able to consider a total of 1,115 different rare diseases.

"This wide variation in appearance trained the AI so well that we can
now diagnose with relative confidence even with only two patients as
our baseline at best, if that's possible," Krawitz says.

"We are very happy to finally have a phenotype analysis solution for the
ultra- rare cases, which can help clinicians solve challenging cases, and researchers to progress rare disease understanding," says Aviram Bar-Haim
of FDNA Inc. in Boston, USA. In Germany, too, the application in doctors' offices, for example, is not far off, adds Krawitz. Doctors can already
use their smartphones to take a portrait photo of a patient and use AI to
make differential diagnoses, he says. "GestaltMatcher helps the physician
make an assessment and complements expert opinion." Peter Krawitz and
his team turned over the data they collected themselves to the non-profit Association for Genome Diagnostics (AGD), to provide researchers with
access. "The GestaltMatcher Database (GMDB) will improve the comparability
of algorithms and provide the basis for further development of artificial intelligence for rare diseases, including other medical image data such
as X- rays or retinal images from ophthalmology," Krawitz says.

Participating institutions and funding: In addition to the Institute for Genomic Statistics and Bioinformatics and the Institute of Human Genetics
of the University Hospital Bonn, the Charite'- Universita"tsmedizin
Berlin, the universities of Greifswald, Tu"bingen, Du"sseldorf, Lu"beck, Heidelberg, the Technical University of Munich as well as universities
from South Africa, France, the USA and Norway were involved. The study
was mainly funded by the German Research Foundation (DFG).

========================================================================== Story Source: Materials provided by University_of_Bonn. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Tzung-Chien Hsieh, Aviram Bar-Haim, Shahida Moosa, Nadja Ehmke,
Karen W.

Gripp, Jean Tori Pantel, Magdalena Danyel, Martin Atta Mensah,
Denise Horn, Stanislav Rosnev, Nicole Fleischer, Guilherme
Bonini, Alexander Hustinx, Alexander Schmid, Alexej Knaus,
Behnam Javanmardi, Hannah Klinkhammer, Hellen Lesmann, Sugirthan
Sivalingam, Tom Kamphans, Wolfgang Meiswinkel, Fre'de'ric Ebstein,
Elke Kru"ger, Se'bastien Ku"ry, Ste'phane Be'zieau, Axel Schmidt,
Sophia Peters, Hartmut Engels, Elisabeth Mangold, Martina Kreiss,
Kirsten Cremer, Claudia Perne, Regina C. Betz, Tim Bender, Kathrin
Grundmann-Hauser, Tobias B. Haack, Matias Wagner, Theresa Brunet,
Heidi Beate Bentzen, Luisa Averdunk, Kimberly Christine Coetzer,
Gholson J. Lyon, Malte Spielmann, Christian P. Schaaf, Stefan
Mundlos, Markus M. No"then, Peter M. Krawitz. GestaltMatcher
facilitates rare disease matching using facial phenotype
descriptors. Nature Genetics, 2022; DOI: 10.1038/s41588-021-01010-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220210114016.htm

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