From a The Lancet online article (January 18, 2020):
Smartphone app-based platforms for urine testing could improve adherence to albumin creatinine ratio (ACR) testing. One study showed screening of at-risk patients almost doubled with a home urine test kit that uses a smartphone camera to easily and accurately quantify ACR from a user-performed urine dipstick. If independently validated in a large, diverse population, this low-cost strategy could change the often dim trajectory for individuals with declining kidney function.
In the outpatient setting, a Japanese team used machine learning and natural language processing to predict disease progression and need for dialysis over 6 months in patients with diabetic nephropathy. And while the increased risk of contrast-induced acute kidney injury has been long appreciated, a machine learning algorithm trained and tested on 3 million adults effectively quantified the degree of kidney injury on the basis of the volume of contrast used and individual patient-level characteristics.