3D-Printed Phantoms to Quantify Accuracy and Variability of Goniometric and Volumetric Assessment of Peyronie’s Disease Deformities - Beyond the Abstract

Peyronie’s disease (PD) is a fibrotic disease that is characterized by plaque accumulation in the tunica albuginea of the penis. Individuals with PD commonly report penile curvature, although other symptoms include hourglass deformities, penile length and girth loss, and erectile dysfunction. Current workflows use manual goniometry to assess angle curvature and disease severity. It remains a question how accurate manual measurements may be when erection quality may be influenced by a multitude of factors and inter-provider variability may exist in defining the point of maximum curvature. The study objectives were to identify which penile characteristics were most susceptible to variability between providers and to assess the use of 3D structured light scanning as a tool in the evaluation of this disease.


We used 3D printed penile models to simulate common deformities in angle, length, and volume. We compared the inter-provider variability and accuracy of 10 urology providers to pre-determined design parameters. We additionally assessed the accuracy of 3D light scanning as a technique to accurately measure penile deformities. Overall, we found volume and angle of curvature were most susceptible to inter-provider variability and had significant differences in measurement compared to the originally designed parameters. These differences were not seen in the 3D light scanned evaluations.

Our study had 3 major takeaways to improve the evaluation for Peyronie’s disease. First, we identified inaccuracies that may exist in the evaluation of this disease. Second, we propose the use of 3D light scanning as a method to accurately characterize penile deformities and to establish volume changes as a metric for Peyronie’s disease. And lastly, we identify a workflow to more accurately evaluate and monitor treatment progression for Peyronie’s disease that is subject to minimal inter-provider variability that may currently exist. These technologies can also help further answer questions about the efficacy of current treatments of this condition.

Written by: Tommy Jiang & Sriram Eleswarapu, MD, PhD, David Geffen School of Medicine at UCLA, Los Angeles, CA

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