Predicting Postoperative Intraocular Lens Dislocation in Cataract
  Surgery via Deep Learning
Negin Ghamsarian Doris Putzgruber-Adamitsch Stephanie Sarny Raphael Sznitman Klaus Schoeffmann Yosuf El-Shabrawi
A critical yet unpredictable complication following cataract surgery is
intraocular lens dislocation. Postoperative stability is imperative, as even a
tiny decentration of multifocal lenses or inadequate alignment of the torus in
toric lenses due to postoperative rotation can lead to a significant drop in
visual acuity. Investigating possible intraoperative indicators that can
predict post-surgical instabilities of intraocular lenses can help prevent this
complication. In this paper, we develop and evaluate the first fully-automatic
framework for the computation of lens unfolding delay, rotation, and
instability during surgery. Adopting a combination of three types of CNNs,
namely recurrent, region-based, and pixel-based, the proposed framework is
employed to assess the possibility of predicting post-operative lens
dislocation during cataract surgery. This is achieved via performing a
large-scale study on the statistical differences between the behavior of
different brands of intraocular lenses and aligning the results with expert
surgeons' hypotheses and observations about the lenses. We exploit a
large-scale dataset of cataract surgery videos featuring four intraocular lens
brands. Experimental results confirm the reliability of the proposed framework
in evaluating the lens' statistics during the surgery. The Pearson correlation
and t-test results reveal significant correlations between lens unfolding delay
and lens rotation and significant differences between the intra-operative
rotations stability of four groups of lenses. These results suggest that the
proposed framework can help surgeons select the lenses based on the patient's
eye conditions and predict post-surgical lens dislocation.