  Machine-learning algorithms can facilitate low-cost, user-guided visual
diagnostic platforms for addressing disparities in access to sexual health
services. We developed a clinical image dataset using original and augmented
images for five penile diseases: herpes eruption, syphilitic chancres, penile
candidiasis, penile cancer, and genital warts. We used a U-net architecture
model for semantic pixel segmentation into background or subject image, the
Inception-ResNet version 2 neural architecture to classify each pixel as
diseased or non-diseased, and a salience map using GradCAM++. We trained the
model on a random 91% sample of the image database using 150 epochs per image,
and evaluated the model on the remaining 9% of images, assessing recall (or
sensitivity), precision, specificity, and F1-score (accuracy). Of the 239
images in the validation dataset, 45 (18.8%) were of genital warts, 43 (18.0%)
were of HSV infection, 29 (12.1%) were of penile cancer, 40 (16.7%) were of
penile candidiasis, 37 (15.5%) were of syphilitic chancres, and 45 (18.8%) were
of non-diseased penises. The overall accuracy of the model for correctly
classifying the diseased image was 0.944. Between July 1st and October 1st
2023, there were 2,640 unique users of the mobile platform. Among a random
sample of submissions (n=437), 271 (62.0%) were from the United States, 64
(14.6%) from Singapore, 41 (9.4%) from Candia, 40 (9.2%) from the United
Kingdom, and 21 (4.8%) from Vietnam. The majority (n=277 [63.4%]) were between
18 and 30 years old. We report on the development of a machine-learning model
for classifying five penile diseases, which demonstrated excellent performance
on a validation dataset. That model is currently in use globally and has the
potential to improve access to diagnostic services for penile diseases.
