  We proposed a novel unsupervised methodology named Disarranged Zone Learning
(DZL) to automatically recognize stenosis in coronary angiography. The
methodology firstly disarranges the frames in a video, secondly it generates an
effective zone and lastly trains an encoder-decoder GRU model to learn the
capability to recover disarranged frames. The breakthrough of our study is to
discover and validate the Sequence Intensity (Recover Difficulty) is a measure
of Coronary Artery Stenosis Status. Hence, the prediction accuracy of DZL is
used as an approximator of coronary stenosis indicator. DZL is an unsupervised
methodology and no label engineering effort is needed, the sub GRU model in DZL
works as a self-supervised approach. So DZL could theoretically utilize
infinitely huge amounts of coronary angiographies to learn and improve
performance without laborious data labeling. There is no data preprocessing
precondition to run DZL as it dynamically utilizes the whole video, hence it is
easy to be implemented and generalized to overcome the data heterogeneity of
coronary angiography. The overall average precision score achieves 0.93, AUC
achieves 0.8 for this pure methodology. The highest segmented average precision
score is 0.98 and the highest segmented AUC is 0.87 for coronary occlusion
indicator. Finally, we developed a software demo to implement DZL methodology.
