64 Development of a Machine Learning Algorithm for at Home Curvature Assessment

ABSTRACT Introduction Peyronie's disease prevalence estimates suggest that 11-15% of men suffer from this disease, although only 1% seek evaluation. When patients do seek help, tools for penile curvature assessment are lacking. This represents a barrier for both patient engagement with a health care provider, and for proper assessment by their treating clinician. Objective We set out to develop a tool for patient and clinician led assessment of Peyronie's disease using a neural network system capable of plaque assessment of photographic images. Methods Men underdoing penile curvature assessment in a sexual medicine clinic were recruited to enroll in this study. Images were taken of the erect penis from the left, right and top sides using an Apple iPad™. Model training was conducted with a residual neural network (ResNet), a neural network-based framework used for image recognition, to identify male genitals. To assess basic curvature a mathematical formulation was created to calculate the penile curvature after fitting linear lines from genital tip to curvature point and from curvature point to the base of the penis. To validate the algorithms performance two urologic surgeons with extensive experience performing curvature assessments were asked to use the labelling software Rectlabel™ to place linear lines identifying the penile curvature on the same photographs used to train the model. Results Initial training of the image analysis model has allowed for identification of the presence of a curvature of a penis, with ongoing training for curvature quantification (Figure 1). We have trained the model to remove background images, isolating the genitals (Figure 1) to aid in patient privacy. When assessing the models ability to measure penile curvature the algorithm calculated the curvature with a median difference of 7.7 degrees between model and physician assessment across the photographic library. Conclusions This study presents an algorithm capable of performing accurate curvature assessment based on photographic images. With demonstration of curvature assessment via photographic images this will allow patients to perform an initial curvature assessment at home, improving motivation for patients to seek help. Furthermore, this technology can augment clinician led curvature assessment, providing an objective and easy means for documenting disease severity. Disclosure No

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