MTCD: Cataract detection via near infrared eye images

Tripathi, Pavani; Akhter, Yasmeena; Khurshid, Mahapara; Lakra, Aditya; Keshari, Rohit; Vatsa, Mayank; Singh, Richa

doi:10.1016/j.cviu.2021.103303

Cited by 11 publications

(1 citation statement)

References 25 publications

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“…In recent years, convolutional neural networks (CNN) have become the predominant driving engine in computerized surgical workflow analysis. Recent studies have showcased the capabilities of CNN-based frameworks in preoperative cataract diagnosis [61], [64], [74], and cataract type and severity classification [29]. The intra-operative deeplearning-based methods can be categorized into two primary areas (i) operation room planning and (ii) intra-operative surgical guidance.…”

Section: Related Workmentioning

confidence: 99%

Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning

Ghamsarian,

Putzgruber-Adamitsch,

Sarny

et al. 2024

IEEE Access

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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 pixelbased, 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 ttest 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.

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