Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning

• URL: http://arxiv.org/abs/2312.03401v1
• Date: Wed, 6 Dec 2023 10:27:15 GMT
• Title: Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning
• Authors: Negin Ghamsarian, Doris Putzgruber-Adamitsch, Stephanie Sarny, Raphael Sznitman, Klaus Schoeffmann, Yosuf El-Shabrawi
• Abstract summary: A critical yet unpredictable complication following cataract surgery is intraocular lens dislocation. We develop and evaluate the first fully-automatic framework for the computation of lens unfolding delay, rotation, and instability during surgery. We exploit a large-scale dataset of cataract surgery videos featuring four intraocular lens brands.
• Score: 5.40411016117853
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery [3.8909273404657556]
  We develop a method that permits direct 2D-to-3D registration of the view microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively.
  arXiv  Detail & Related papers  (2024-03-12T00:26:08Z)
• An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models [8.516340459721484]
  We propose a first vision-based approach to update the preoperative 3D anatomical model. Results show a decrease in error during surgical progression as opposed to increasing when no update is employed.
  arXiv  Detail & Related papers  (2024-02-19T05:06:52Z)
• Action Recognition in Video Recordings from Gynecologic Laparoscopy [4.002010889177872]
  Action recognition is a prerequisite for many applications in laparoscopic video analysis. In this study, we design and evaluate a CNN-RNN architecture as well as a customized training-inference framework.
  arXiv  Detail & Related papers  (2023-11-30T16:15:46Z)
• EyeLS: Shadow-Guided Instrument Landing System for Intraocular Target Approaching in Robotic Eye Surgery [51.05595735405451]
  Robotic ophthalmic surgery is an emerging technology to facilitate high-precision interventions such as retina penetration in subretinal injection and removal of floating tissues in retinal detachment. Current image-based methods cannot effectively estimate the needle tip's trajectory towards both retinal and floating targets. We propose to use the shadow positions of the target and the instrument tip to estimate their relative depth position. Our method succeeds target approaching on a retina model, and achieves an average depth error of 0.0127 mm and 0.3473 mm for floating and retinal targets respectively in the surgical simulator.
  arXiv  Detail & Related papers  (2023-11-15T09:11:37Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• Learning-Based Keypoint Registration for Fetoscopic Mosaicking [65.02392513942533]
  In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. We propose a learning-based framework for in-vivo fetoscopy frame registration for field-of-view expansion.
  arXiv  Detail & Related papers  (2022-07-26T21:21:12Z)
• MTCD: Cataract Detection via Near Infrared Eye Images [69.62768493464053]
  cataract is a common eye disease and one of the leading causes of blindness and vision impairment. We present a novel algorithm for cataract detection using near-infrared eye images. Deep learning-based eye segmentation and multitask network classification networks are presented.
  arXiv  Detail & Related papers  (2021-10-06T08:10:28Z)
• LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos [6.743968799949719]
  A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. We propose an end-to-end recurrent neural network to recognize the lens-implantation phase and a novel semantic segmentation network to segment the lens and pupil after the implantation phase.
  arXiv  Detail & Related papers  (2021-07-02T07:27:29Z)
• Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization [7.235239641693831]
  In cataract surgery, the operation is performed with the help of a microscope. Since the microscope enables watching real-time surgery by up to two people only, a major part of surgical training is conducted using the recorded videos. To optimize the training procedure with the video content, the surgeons require an automatic relevance detection approach. In this paper, a three- module framework is proposed to detect and classify the relevant phase segments in cataract videos.
  arXiv  Detail & Related papers  (2021-04-29T12:01:08Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.