  With the advent of smartphone indirect ophthalmoscopy, teleophthalmology -
the use of specialist ophthalmology assets at a distance from the patient - has
experienced a breakthrough, promising enormous benefits especially for
healthcare in distant, inaccessible or opthalmologically underserved areas,
where specialists are either unavailable or too few in number. However,
accurate teleophthalmology requires high-quality ophthalmoscopic imagery. This
paper considers three feature families - statistical metrics, gradient-based
metrics and wavelet transform coefficient derived indicators - as possible
metrics to identify unsharp or blurry images. By using standard machine
learning techniques, the suitability of these features for image quality
assessment is confirmed, albeit on a rather small data set. With the increased
availability and decreasing cost of digital ophthalmoscopy on one hand and the
increased prevalence of diabetic retinopathy worldwide on the other, creating
tools that can determine whether an image is likely to be diagnostically
suitable can play a significant role in accelerating and streamlining the
teleophthalmology process. This paper highlights the need for more research in
this area, including the compilation of a diverse database of ophthalmoscopic
imagery, annotated with quality markers, to train the Point of Acquisition
error detection algorithms of the future.
