Artificial intelligence for the diagnosis of rare diseases related to collagen VI

Researchers at the Institute of Robotics and Industrial Informatics -a joint center of the CSIC and the Polytechnic University of Catalonia-, and at the Sant Joan de Déu Hospital in Barcelona, have developed a system for helping in the diagnoses of rare diseases related to deficiencies in the structure of collagen VI.

Fibroblast cultures seen through a confocal microscope, where collagen network (green) and  fibroblast nuclei (blue) are visible. Left: a control sample. Center: sample of a patient with Bethlem myopathy. Right: Sample of a patient with Ullrich muscular dystrophy. The system makes diagnosis from images obtained with a confocal microscope and it is based on artificial learning techniques. These techniques learn from cases previously diagnosed by hospital specialists to generate a fully automatic diagnostic system. The developed system achieves a reliability in the diagnosis superior to 95%. It could become a valuable tool to objectively assess any new therapy to treat these diseases.

Deficiencies in the structure of collagen VI are a common cause of neuromuscular diseases with serious manifestations ranging from Bethlem's myopathy to severe Ullrich congenital muscular dystrophy. The symptoms of such diseases include proximal and axial muscle weakness, distal hyperlaxity, joint contractures and critical respiratory failure that requires assisted ventilation, what dramatically reduces life expectancy.

Structural defects of collagen VI are related to mutations of the COL6A1, COL6A2 and COL6A3 genes. However, despite current genetic sequencing technologies, diagnosis remains difficult. This happens in general in diseases caused by dominant mutations, where there is no complete absence of a major protein, and when the effect of a genetic variant on the protein structure may not be evident. Therefore, before any genetic analysis, the standard technique for the diagnosis of dystrophies related to collagen VI is the analysis of fibroblast culture images.

The specialists take into account several aspects of the images, such as the coherence in the orientation of the collagen fibres, the distribution of the collagen network and the arrangement of the cells in the said network to identify potential patients. However, this evaluation is only qualitative, and regulatory agencies will not approve any treatment (such as genetic editing using CRISPR technology) without an objective methodology to assess its effectiveness.

Therefore, there is an urgent need for precise methodologies to quantitatively monitor the effects of any possible new therapy. The proposed system responds to this need. Such a system is advantageous since it solves the problem of the lack of data for typical learning in rare diseases, points out the possibly problematic areas in the consultation images and provides a general quantitative assessment of the condition of the patients.

This work originates from a JAE grant for research introduction, funded by the CSIC granted to the first author of the reference article.

Reference article:

A. Bazaga, M. Roldán, C. Badosa, C. Jiménez-Mallebrera, J. M. Porta, A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI-related Muscular Dystrophies, Applied Soft Computing, Vol. 85, December 2019, 105772, 2019. https://doi.org/10.1016/j.asoc.2019.105772

Via: Institute of Robotics and Industrial Informatics (IRI)