Researchers Develop Computer-aided Facial Method for Diagnosis

Researchers-Develop-Computer-aided-Facial-Method-for-Diagnosis

Researchers created a new method to diagnose diseases using artificial intelligence, according to a new study published in journal Genome Medicine on January 16, 2018.

A team of researchers from the University Hospital Bonn and the Charité- Universitätsmedizin Berlin, have made use of genetic material, cell surface texture, and typical facial features to simulate disease models.

In rare diseases, the computer-aided image analysis of patient portraits can facilitate and significantly improve diagnosis. Mabry syndrome, a rare disease that causes mental retardation, is triggered by a change in a single gene. “This disease belongs to a group that we describe as GPI anchor deficiencies and which includes more than 30 genes,” explains physician and physicist Prof. Dr. Peter Krawitz GPI anchors attach specific proteins to the cell membrane. If this does not work properly due to a gene mutation, signal transmission and further steps in the cell-cell communication are impaired.

The disease exhibits distinctive facial features. In Mabry syndrome for example, a narrow, tent-shaped upper lip, broad bridge of the nose, and wide-set eyes with long palpebral fissures are among the classic features, but these may be more or less pronounced. The diagnosis of this rare disease is thus complicated. The elevated alkaline phosphatase (AP) levels in the blood cannot be detected in every patient. “The result is that many patients and their relatives often suffer many years of uncertainty until the correct diagnosis is made,” says Krawitz.

The researchers applied artificial intelligence in image analysis, using photographs of the faces of a total of 91 patients. Cell surface changes for GPI anchor deficiencies were detected in some of the participants. Genetic analysis also revealed gene mutations that are typical for this rare group of diseases.

“The artificial modeling of gene-typical faces that we achieved with these datasets clearly shows that the computer-aided evaluation of patients’ portraits can facilitate and improve the diagnosis of GPI anchor deficiencies, which is significant progress,” says lead author Dr. Knaus.

The combined data from the laboratory and the computer is expected to create a better understanding of the molecular processes involved in diseases. In this manner, artificial intelligence employed in diagnostics of diseases can initiate better treatment, as elaborated in artificial intelligence in healthcare market report published by Coherent Market Insights.

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