Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy—Application of Sensor Arrays and Machine Learning
Georgina Faura,
Gerard Boix-Lemonche,
Anne Kristin Holmeide,
Rasa Verkauskiene,
Vallo Volke,
Jelizaveta Sokolovska,
Goran Petrovski
Affiliations
Georgina Faura
Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway
Gerard Boix-Lemonche
Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway
Anne Kristin Holmeide
Sharelab, Biozep AS, Oslo Science Park, Gaustadalleen 21, 0349 Oslo, Norway
Rasa Verkauskiene
Institute of Endocrinology, Medical Academy, Lithuanian University of Health Sciences, LT-50009 Kaunas, Lithuania
Vallo Volke
Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia
Jelizaveta Sokolovska
Faculty of Medicine, University of Latvia, Jelgavas Street 3, LV 1004 Riga, Latvia
Goran Petrovski
Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.
