Literature DB >> 35774322

Automatic evaluation of graft orientation during Descemet membrane endothelial keratoplasty using intraoperative OCT.

Marc B Muijzer1,2, Friso G Heslinga3,2, Floor Couwenberg4, Herke-Jan Noordmans5, Abdelkarim Oahalou6, Josien P W Pluim3,7, Mitko Veta3,2, Robert P L Wisse1,2.   

Abstract

Correct Descemet Membrane Endothelial Keratoplasty (DMEK) graft orientation is imperative for success of DMEK surgery, but intraoperative evaluation can be challenging. We present a method for automatic evaluation of the graft orientation in intraoperative optical coherence tomography (iOCT), exploiting the natural rolling behavior of the graft. The method encompasses a deep learning model for graft segmentation, post-processing to obtain a smooth line representation, and curvature calculations to determine graft orientation. For an independent test set of 100 iOCT-frames, the automatic method correctly identified graft orientation in 78 frames and obtained an area under the receiver operating characteristic curve (AUC) of 0.84. When we replaced the automatic segmentation with the manual masks, the AUC increased to 0.92, corresponding to an accuracy of 86%. In comparison, two corneal specialists correctly identified graft orientation in 90% and 91% of the iOCT-frames.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

Entities:  

Year:  2022        PMID: 35774322      PMCID: PMC9203112          DOI: 10.1364/BOE.446519

Source DB:  PubMed          Journal:  Biomed Opt Express        ISSN: 2156-7085            Impact factor:   3.562


  35 in total

Review 1.  Receiver operating characteristic curve in diagnostic test assessment.

Authors:  Jayawant N Mandrekar
Journal:  J Thorac Oncol       Date:  2010-09       Impact factor: 15.609

Review 2.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 3.  Artificial Intelligence Transforms the Future of Health Care.

Authors:  Nariman Noorbakhsh-Sabet; Ramin Zand; Yanfei Zhang; Vida Abedi
Journal:  Am J Med       Date:  2019-01-31       Impact factor: 4.965

4.  Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study.

Authors:  Maximilian Treder; Jost Lennart Lauermann; Maged Alnawaiseh; Nicole Eter
Journal:  Cornea       Date:  2019-02       Impact factor: 2.651

5.  Clinical Decision Support in the Era of Artificial Intelligence.

Authors:  Edward H Shortliffe; Martin J Sepúlveda
Journal:  JAMA       Date:  2018-12-04       Impact factor: 56.272

6.  The role of novel DMEK graft shapes in facilitating intraoperative unscrolling.

Authors:  Milad Modabber; Julia C Talajic; Michèle Mabon; Mathieu Mercier; Samir Jabbour; Johanna Choremis
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2018-09-28       Impact factor: 3.117

7.  Intraoperative optical coherence tomography-assisted descemet membrane endothelial keratoplasty in the DISCOVER study.

Authors:  Brian Cost; Jeffrey M Goshe; Sunil Srivastava; Justis P Ehlers
Journal:  Am J Ophthalmol       Date:  2015-05-28       Impact factor: 5.258

8.  A survey on shape-constraint deep learning for medical image segmentation.

Authors:  Simon Bohlender; Ilkay Oksuz; Anirban Mukhopadhyay
Journal:  IEEE Rev Biomed Eng       Date:  2021-12-17

9.  How to Avoid an Upside-Down Orientation of the Graft during Descemet Membrane Endothelial Keratoplasty?

Authors:  Joanna Wasielica-Poslednik; Alexander K Schuster; Lilian Rauch; Jessica Glaner; Aytan Musayeva; Jana C Riedl; Norbert Pfeiffer; Adrian Gericke
Journal:  J Ophthalmol       Date:  2019-08-04       Impact factor: 1.909

10.  Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks.

Authors:  Friso G Heslinga; Mark Alberti; Josien P W Pluim; Javier Cabrerizo; Mitko Veta
Journal:  Transl Vis Sci Technol       Date:  2020-08-21       Impact factor: 3.283

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