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Optical 3D microscopy for more effective diagnosis of kidney diseases

Automatic image analysis of morphological changes will provide a faster, safer and quantitatively improved analysis of the development of kidney disease.

Via 3D microscopy, the filtration structure of the kidney, the 200–500 nanometre wide podocyte foot processes, are visualised with light microscopy (left image). This enables simple automatic analysis and quantification of the health condition of the kidney (images right), with possible application within clinical renal diagnostics.

More than half a million people are estimated to live with chronic kidney diseases in Sweden. The problem is exacerbated as people all over the world are increasingly developing diseases such as diabetes and high blood pressure, which in turn, affect the way kidneys function. Added to which, COVID-19 has often proven to cause acute damage to kidneys. With an early diagnosis of the development of kidney disease, one can stop or reverse the course of the disease, and prevent costly kidney dialysis or kidney transplantation.

Our method will deliver both 2D and 3D images of kidney biopsies and through image analysis ('machine learning', 'deep-learning', AI) we intend to develop automatic quantitative support for the diagnosis of common kidney diseases for improved precision health medicine. Our goal is to improve kidney pathology for more efficient and earlier detection of kidney diseases clinically.

The introduction of super-resolution optical pathology will make it possible to replace electron microscopy analysis, which takes place in extremely thin layers, to 3-D morphologically follow the development of kidney diseases in situ. With the help of automatic image analysis of morphological changes, this will provide a faster, safer and quantitatively improved analysis of the development of kidney diseases.

The project is a collaboration between pioneers in super-resolution optical microscopy at KTH and clinical researchers at Karolinska Institutet at Karolinska University Hospital and Danderyd Hospital.

During the start-up, the following researchers will collaborate in the project to develop the method for clinical kidney diagnosis:

Hans Blom , Research Leader, Associate Professor, KTH / Scilifelab
Sigrid Lundberg , Research Leader, MD, Renal Researcher/ Pathologist, KI / KS Danderyd
David Unnersjö-Jess, Tekn Dr / Postdoc, KTH / Univ Köln
Hjalmar Brismar, Professor, KTH / Scilifelab
Jaakko Patrakka, Associate Professor/ Professor, Renal researcher / nephrologist, KI / KS Huddinge
Annika Östman Wernerson, Professor, Renal and Transplantation Science/ Pathologist consultant KI / KS Huddinge
Hannes Olauson, Med Dr / MD, kidney researcher / pathologist, KI / KS Solna

Belongs to: MedTechLabs
Last changed: Dec 02, 2021