2024 Volume 51 Issue 2 Pages 236-241
The number of patients with chronic kidney disease in Japan is estimated to be around 14 million. As the disease progresses, patients not only require dialysis and kidney transplantation, but it is also related to major causes of death due to decreased immunity and progression of arteriosclerosis. In addition to renal biopsy, imaging tests such as ultrasound, MRI and CT are used for diagnosis, but in routine clinical practice, serum creatinine, proteinuria and blood pressure are the main assessment parameters.
This existing clinical data is used as a feature for AI diagnostics to predict the onset of acute kidney injury, life prognosis, and risk of progression of kidney disease. The introduction of AI techniques into the quantitative evaluation of kidney biopsies is also being explored, demonstrating high accuracy in tissue classification and lesion identification.
Advances in imaging diagnostics, represented by MRI, have made it possible to visualize renal pathologies that were previously unattainable. Innovative imaging techniques allow for the non-invasive acquisition of information related to renal ischemia, hypoxia, perfusion, and changes in microstructure and fibrosis. AI technology is heavily relied upon as a means to comprehensively and quantitatively process vast amounts of imaging information.
In the absence of effective treatments for chronic kidney disease, early diagnosis and appropriate therapeutic intervention are critical. The use of AI technology is expected to contribute to accurate diagnosis and evaluation of treatment effects for kidney disease, and to have a significant impact on the progress of clinical research.
