  Scanning probe microscopists generally do not rely on complete images to
assess the quality of data acquired during a scan. Instead, assessments of the
state of the tip apex, which not only determines the resolution in any scanning
probe technique but can also generate a wide array of frustrating artefacts,
are carried out in real time on the basis of a few lines of an image (and,
typically, their associated line profiles.) The very small number of machine
learning approaches to probe microscopy published to date, however, involve
classifications based on full images. Given that data acquisition is the most
time-consuming task during routine tip conditioning, automated methods are thus
currently extremely slow in comparison to the tried-and-trusted strategies and
heuristics used routinely by probe microscopists. Here, we explore various
strategies by which different STM image classes (arising from changes in the
tip state) can be correctly identified from partial scans. By employing a
secondary temporal network and a rolling window of a small group of individual
scanlines, we find that tip assessment is possible with a small fraction of a
complete image. We achieve this with little-to-no performance penalty -- or,
indeed, markedly improved performance in some cases -- and introduce a protocol
to detect the state of the tip apex in real time.
