Predicting postoperative intraocular lens dislocation in cataract surgery via deep learning

Ghamsarian, Negin; Putzgruber-Adamitsch, Doris; Sarny, Stephanie; Sznitman, Raphael; Schoeffmann, Klaus; El-Shabrawi, Yosuf (2024). Predicting postoperative intraocular lens dislocation in cataract surgery via deep learning. IEEE Access IEEE 10.1109/ACCESS.2024.3361042

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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 postoperative 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.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

UniBE Contributor:

Ghamsarian, Negin, Sznitman, Raphael

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems

ISSN:

2169-3536

Publisher:

IEEE

Language:

English

Submitter:

Negin Ghamsarian

Date Deposited:

17 Jul 2024 14:10

Last Modified:

17 Jul 2024 14:20

Publisher DOI:

10.1109/ACCESS.2024.3361042

BORIS DOI:

10.48350/199061

URI:

https://boris.unibe.ch/id/eprint/199061

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