Researchers at the University of Bonn have trained software to improve our ability to diagnose rare genetic diseases. The program uses a patient’s portrait photograph and analyzes their facial features — such as characteristically shaped brows, nose, or cheeks — to judge how at risk a certain individual is of these ailments.

Dubbed “GestaltMatcher”, the program has successfully diagnosed known diseases in a trial with a very small number of patients.

Automated diagnosis

“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, corresponding author of the paper.

“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, co-author of the paper, in a press release. “GestaltMatcher helps the physician make an assessment and complements expert opinion.”

The way we perform diagnosis in healthcare will undoubtedly be revolutionized by AI. And, judging from the results of a new study, that revolution is already upon us.

A large number of very rare diseases are rooted in genetic factors. The same hereditary mutations that encode these diseases, however, ale also expressed phenotypically (in the body’s features) in characteristic ways, for example, in the particular shape of the nose, cheeks, or brows. Obviously, these characteristics vary from one disease to another and can be quite subtle, making them a poor diagnosis element — for human doctors, that is.

AI can however pick up on these subtle features and link them to a known disease. The new software analyzes an individual’s facial features from their profile picture, calculates how similar they are to a known set of characteristics, and uses this to estimate the probability that the person in question bears the genes associated with various conditions. The individual’s clinical symptoms and any available genetic data are also factored into the analysis.

The system is a further development of “DeepGestalt”, which the IGSB team trained with other institutions a few years ago. The team worked to improve its ability to learn using a small sample of patients — and the new program is much better in this regard than its predecessor — which is a key feature for software used to diagnose rare diseases, where sample sizes are very limited. Another key improvement is GestaltMatcher’s ability to consider data from patients who have not yet been diagnosed, allowing it to take into account combinations of features that have not yet been described. This, the team explains, allows it to recognize diseases that were previously unknown, and suggest diagnoses based on data available to it.

The program was trained using 17,560 patient photos, most of which came from digital health company FDNA. Around 5,000 of those photographs were contributed by the Institute of Human Genetics at the University of Bonn, along with nine other university sites in Germany and abroad. All in all, these covered 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.

The data was turned over to the non-profit Association for Genome Diagnostics (AGD), to allow researchers around the world free access to it.

The application is not far off from being available in doctors’ offices in certain countries such as Germany, the team adds. Doctors can simply take portraits of their patients with a smartphone and use the AI to help them in a diagnosis.