Dataset Information

Validation of the Use of Automated and Manual Quantitative Analysis of Corneal Nerve Plexus Following Refractive Surgery.

ABSTRACT: Following refractive surgery, the cornea is denervated and re-innervated, hence a reproducible tool to objectively quantify this change is warranted. This study aimed to determine the repeatability and reproducibility of corneal nerve quantification between automated (ACCMetrics) and manual software (CCMetrics) following refractive surgery. A total of 1007 in vivo confocal microscopy images from 20 post-small incision lenticule extraction (SMILE) or post-laser-assisted in situ keratomileusis (LASIK) patients were evaluated by two independent observers using CCMetrics for corneal nerve fibre density (CNFD), corneal nerve branch density (CNBD), and corneal nerve fibre length (CNFL). Intra-observer and inter-observer reproducibility and repeatability, as well as agreement and correlation between the measurements obtained by ACCMetrics and CCMetrics, were assessed. We found that CNFL demonstrated the best intra- and inter-observer agreement followed by CNFD (intra-class correlation coefficient (ICC) = 0.799 and 0.740, respectively for CNFL; 0.757 and 0.728 for CNFD). CNBD demonstrated poorest intra- and inter-observer ICC. There was an underestimation in ACCMetrics measurements compared to CCMetrics measurements, although the differences were not significant. Our data suggested that both automated and manual methods can be used as reliable tools for the evaluation of corneal nerve status following refractive surgery. However, the measurements obtained with different methods are not interchangeable.

SUBMITTER: Chin JY

PROVIDER: S-EPMC7400400 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Validation of the Use of Automated and Manual Quantitative Analysis of Corneal Nerve Plexus Following Refractive Surgery.

Chin Jia Ying JY Yang Lily Wei Yun LWY Ji Angel Jung Se AJS Nubile Mario M Mastropasqua Leonardo L Allen John Carson JC Mehta Jodhbir S JS Liu Yu-Chi YC

Diagnostics (Basel, Switzerland) 20200718 7

Following refractive surgery, the cornea is denervated and re-innervated, hence a reproducible tool to objectively quantify this change is warranted. This study aimed to determine the repeatability and reproducibility of corneal nerve quantification between automated (ACCMetrics) and manual software (CCMetrics) following refractive surgery. A total of 1007 in vivo confocal microscopy images from 20 post-small incision lenticule extraction (SMILE) or post-laser-assisted in situ keratomileusis (LA ...[more]

PMID: 32708510

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Project description:ImportanceEvaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans.ObjectiveTo examine the use of a deep learning model in the screening of candidates for refractive surgery.Design, setting, and participantsA diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, to June 28, 2019. Participants included 1385 patients; 6465 corneal tomographic images were used to generate the artificial intelligence (AI) model. The Pentacam HR system was used for data collection.InterventionsThe deidentified images were analyzed by ophthalmologists and the AI model.Main outcomes and measuresThe performance of the AI classification system.ResultsA classification system centered on the AI model Pentacam InceptionResNetV2 Screening System (PIRSS) was developed for screening potential candidates for refractive surgery. The model achieved an overall detection accuracy of 94.7% (95% CI, 93.3%-95.8%) on the validation data set. Moreover, on the independent test data set, the PIRSS model achieved an overall detection accuracy of 95% (95% CI, 88.8%-97.8%), which was comparable with that of senior ophthalmologists who are refractive surgeons (92.8%; 95% CI, 91.2%-94.4%) (P = .72). In distinguishing corneas with contraindications for refractive surgery, the PIRSS model performed better than the classifiers (95% vs 81%; P < .001) in the Pentacam HR system on an Asian patient database.Conclusions and relevancePIRSS appears to be useful in classifying images to provide corneal information and preliminarily identify at-risk corneas. PIRSS may provide guidance to refractive surgeons in screening candidates for refractive surgery as well as for generalized clinical application for Asian patients, but its use needs to be confirmed in other populations.

Dataset Information

Validation of the Use of Automated and Manual Quantitative Analysis of Corneal Nerve Plexus Following Refractive Surgery.

Publications

Validation of the Use of Automated and Manual Quantitative Analysis of Corneal Nerve Plexus Following Refractive Surgery.

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