Automatic detection of pupil reactions in cataract surgery videos

In the light of an increased use of premium intraocular lenses (IOL), such as EDOF IOLs, multifocal IOLs or toric IOLs even minor intraoperative complications such as decentrations or an IOL tilt, will hamper the visual performance of these IOLs. Thus, the post-operative analysis of cataract surgeri...

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Autores principales:	Natalia Sokolova, Klaus Schoeffmann, Mario Taschwer, Stephanie Sarny, Doris Putzgruber-Adamitsch, Yosuf El-Shabrawi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ceebae058984489f80e3216060c6f612
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spelling	oai:doaj.org-article:ceebae058984489f80e3216060c6f6122021-11-04T06:07:16ZAutomatic detection of pupil reactions in cataract surgery videos1932-6203https://doaj.org/article/ceebae058984489f80e3216060c6f6122021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8530330/?tool=EBIhttps://doaj.org/toc/1932-6203In the light of an increased use of premium intraocular lenses (IOL), such as EDOF IOLs, multifocal IOLs or toric IOLs even minor intraoperative complications such as decentrations or an IOL tilt, will hamper the visual performance of these IOLs. Thus, the post-operative analysis of cataract surgeries to detect even minor intraoperative deviations that might explain a lack of a post-operative success becomes more and more important. Up-to-now surgical videos are evaluated by just looking at a very limited number of intraoperative data sets, or as done in studies evaluating the pupil changes that occur during surgeries, in a small number intraoperative picture only. A continuous measurement of pupil changes over the whole surgery, that would achieve clinically more relevant data, has not yet been described. Therefore, the automatic retrieval of such events may be a great support for a post-operative analysis. This would be especially true if large data files could be evaluated automatically. In this work, we automatically detect pupil reactions in cataract surgery videos. We employ a Mask R-CNN architecture as a segmentation algorithm to segment the pupil and iris with pixel-based accuracy and then track their sizes across the entire video. We can detect pupil reactions with a harmonic mean (H) of Recall, Precision, and Ground Truth Coverage Rate (GTCR) of 60.9% and average prediction length (PL) of 18.93 seconds. However, we consider the best configuration for practical use the one with the H value of 59.4% and PL of 10.2 seconds, which is much shorter. We further investigate the generalization ability of this method on a slightly different dataset without retraining the model. In this evaluation, we achieve the H value of 49.3% with the PL of 18.15 seconds.Natalia SokolovaKlaus SchoeffmannMario TaschwerStephanie SarnyDoris Putzgruber-AdamitschYosuf El-ShabrawiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Natalia Sokolova Klaus Schoeffmann Mario Taschwer Stephanie Sarny Doris Putzgruber-Adamitsch Yosuf El-Shabrawi Automatic detection of pupil reactions in cataract surgery videos
description	In the light of an increased use of premium intraocular lenses (IOL), such as EDOF IOLs, multifocal IOLs or toric IOLs even minor intraoperative complications such as decentrations or an IOL tilt, will hamper the visual performance of these IOLs. Thus, the post-operative analysis of cataract surgeries to detect even minor intraoperative deviations that might explain a lack of a post-operative success becomes more and more important. Up-to-now surgical videos are evaluated by just looking at a very limited number of intraoperative data sets, or as done in studies evaluating the pupil changes that occur during surgeries, in a small number intraoperative picture only. A continuous measurement of pupil changes over the whole surgery, that would achieve clinically more relevant data, has not yet been described. Therefore, the automatic retrieval of such events may be a great support for a post-operative analysis. This would be especially true if large data files could be evaluated automatically. In this work, we automatically detect pupil reactions in cataract surgery videos. We employ a Mask R-CNN architecture as a segmentation algorithm to segment the pupil and iris with pixel-based accuracy and then track their sizes across the entire video. We can detect pupil reactions with a harmonic mean (H) of Recall, Precision, and Ground Truth Coverage Rate (GTCR) of 60.9% and average prediction length (PL) of 18.93 seconds. However, we consider the best configuration for practical use the one with the H value of 59.4% and PL of 10.2 seconds, which is much shorter. We further investigate the generalization ability of this method on a slightly different dataset without retraining the model. In this evaluation, we achieve the H value of 49.3% with the PL of 18.15 seconds.
format	article
author	Natalia Sokolova Klaus Schoeffmann Mario Taschwer Stephanie Sarny Doris Putzgruber-Adamitsch Yosuf El-Shabrawi
author_facet	Natalia Sokolova Klaus Schoeffmann Mario Taschwer Stephanie Sarny Doris Putzgruber-Adamitsch Yosuf El-Shabrawi
author_sort	Natalia Sokolova
title	Automatic detection of pupil reactions in cataract surgery videos
title_short	Automatic detection of pupil reactions in cataract surgery videos
title_full	Automatic detection of pupil reactions in cataract surgery videos
title_fullStr	Automatic detection of pupil reactions in cataract surgery videos
title_full_unstemmed	Automatic detection of pupil reactions in cataract surgery videos
title_sort	automatic detection of pupil reactions in cataract surgery videos
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ceebae058984489f80e3216060c6f612
work_keys_str_mv	AT nataliasokolova automaticdetectionofpupilreactionsincataractsurgeryvideos AT klausschoeffmann automaticdetectionofpupilreactionsincataractsurgeryvideos AT mariotaschwer automaticdetectionofpupilreactionsincataractsurgeryvideos AT stephaniesarny automaticdetectionofpupilreactionsincataractsurgeryvideos AT dorisputzgruberadamitsch automaticdetectionofpupilreactionsincataractsurgeryvideos AT yosufelshabrawi automaticdetectionofpupilreactionsincataractsurgeryvideos
_version_	1718445177856589824

Automatic detection of pupil reactions in cataract surgery videos

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