  Image quality is a crucial factor in the effectiveness and efficiency of
teledermatological consultations. However, up to 50% of images sent by patients
have quality issues, thus increasing the time to diagnosis and treatment. An
automated, easily deployable, explainable method for assessing image quality is
necessary to improve the current teledermatological consultation flow. We
introduce ImageQX, a convolutional neural network for image quality assessment
with a learning mechanism for identifying the most common poor image quality
explanations: bad framing, bad lighting, blur, low resolution, and distance
issues. ImageQX was trained on 26,635 photographs and validated on 9,874
photographs, each annotated with image quality labels and poor image quality
explanations by up to 12 board-certified dermatologists. The photographic
images were taken between 2017 and 2019 using a mobile skin disease tracking
application accessible worldwide. Our method achieves expert-level performance
for both image quality assessment and poor image quality explanation. For image
quality assessment, ImageQX obtains a macro F1-score of 0.73 +- 0.01, which
places it within standard deviation of the pairwise inter-rater F1-score of
0.77 +- 0.07. For poor image quality explanations, our method obtains F1-scores
of between 0.37 +- 0.01 and 0.70 +- 0.01, similar to the inter-rater pairwise
F1-score of between 0.24 +- 0.15 and 0.83 +- 0.06. Moreover, with a size of
only 15 MB, ImageQX is easily deployable on mobile devices. With an image
quality detection performance similar to that of dermatologists, incorporating
ImageQX into the teledermatology flow can enable a better, faster flow for
remote consultations.
