Deep Learning Helps Provide Accurate Kidney Volume Measurements

An AI model accurately performs total kidney volume measurements from MRI and reduces contouring time, potentially boosting treatment for people with a common type of kidney disease, according to research published in Radiology.

Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by the formation of numerous cysts and scarring of the kidneys. It is the most common inherited disorder of the kidneys, affecting more than 12 million people worldwide. The disease progresses over time, with many patients experiencing kidney failure in their fifties and sixties.

Measurements of total kidney volume help clinicians evaluate ADPKD progression and determine eligibility for treatment with tolvaptan, a drug that slows disease progression.

“When patients’ kidneys increase in size, that has prognostic implications and correlates with worsening renal function and ultimately the need for dialysis,” said study lead author Akshay Goel, MD, diagnostic radiologist and associate clinical research scientist at Weill Cornell Medicine in New York City. “Therefore, tracking volume is important.”

Accurate Automated Segmentation Time Reduced Expert Contouring Time

Dr. Goel and his colleagues at Weill Cornell Medicine developed an open source deep learning model and deployed it to automatically segment kidneys using MRI for determining total kidney volume in patients with ADPKD.

Kidney segmentation with MRI directly correlates to total kidney volume, Dr. Goel explained, as each voxel maps directly to volume measurement.

“Thus, calculating the clinical metric of total kidney volume from the 3D segmentation, a computer vision metric, is straightforward and only requires the voxel volume and number of pixels within the segmentation mask,” he said.

Previously, operator exhaustion from the tedious contouring task may naturally have led to human error, particularly given the precision required at contour borders. In this study, the researchers used model-assisted annotation to modify the contouring task, with the aim of reducing operator fatigue and enabling radiologists to produce more accurate contours of kidneys in ADPKD.

The model was developed using 213 abdominal MRI studies in 129 patients with ADPKD.

Researchers randomly divided the patients into training, validation and test sets for model development. External validation in 20 patients from outside institutions and prospective validation in 53 patients demonstrated accurate automated segmentation for total kidney volume estimation of polycystic kidneys and reduced expert contouring time. Evaluation on the first 50% of prospective cases showed a 51% mean reduction in contouring time, from almost 29 minutes to only 12 minutes.

“The model performance can be characterized as excellent and robust, with minimal mistakes in the segmentation task on prospective studies at Cornell and exams taken from external institutions,” Dr. Goel said. “Quantitatively, volume estimates are nearly perfect, with an average difference of 2% to 3% from carefully labeled radiologist examples.”