Dataset Information

Impact of Microscope-Integrated OCT on Ophthalmology Resident Performance of Anterior Segment Surgical Maneuvers in Model Eyes.

ABSTRACT:

Purpose

The integration of swept-source optical coherence tomography (SS-OCT) into the operating microscope enables real-time, tissue-level three-dimensional (3D) imaging to aid in ophthalmic microsurgery. In this prospective randomized controlled study, we evaluated the impact of SS microscope-integrated OCT (MI-OCT) on ophthalmology residents' performance of ophthalmic microsurgical maneuvers.

Methods

Fourteen ophthalmology residents from a single institution were stratified by year of training and randomized to perform four anterior segment surgical maneuvers on porcine eyes with (MI-OCT+) or without (MI-OCT-) direct intraoperative OCT guidance. Subsequently, both groups repeated the same maneuvers without MI-OCT feedback to test whether initial MI-OCT experience affected subsequent surgical performance. Finally, the MI-OCT- group was crossed over and allowed to repeat the same maneuvers with direct MI-OCT guidance. Each resident completed a survey at the completion of the study.

Results

With direct MI-OCT feedback, residents demonstrated enhanced performance in depth-based anterior segment maneuvers (corneal suture passes at 50% and 90% depth and corneal laceration repair) compared with the residents operating without MI-OCT. Microscope-integrated OCT+ residents continued to outperform the controls when both groups subsequently operated without MI-OCT. For clear corneal wound geometry, there was no statistically significant effect of MI-OCT as applied in this study. Overall, the resident surgeons rated their subjective experience of using MI-OCT very favorably.

Conclusions

Microscope-integrated OCT feedback enhances performance of ophthalmology residents in select anterior segment surgical maneuvers. Microscope-integrated OCT represents a valuable tool in the surgical education of ophthalmology residents.

SUBMITTER: Todorich B

PROVIDER: S-EPMC4968783 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Publications

Impact of Microscope-Integrated OCT on Ophthalmology Resident Performance of Anterior Segment Surgical Maneuvers in Model Eyes.

Todorich Bozho B Shieh Christine C DeSouza Philip J PJ Carrasco-Zevallos Oscar M OM Cunefare David L DL Stinnett Sandra S SS Izatt Joseph A JA Farsiu Sina S Mruthyunjaya Privthi P Kuo Anthony N AN Toth Cynthia A CA

Investigative ophthalmology & visual science 20160701 9

<h4>Purpose</h4>The integration of swept-source optical coherence tomography (SS-OCT) into the operating microscope enables real-time, tissue-level three-dimensional (3D) imaging to aid in ophthalmic microsurgery. In this prospective randomized controlled study, we evaluated the impact of SS microscope-integrated OCT (MI-OCT) on ophthalmology residents' performance of ophthalmic microsurgical maneuvers.<h4>Methods</h4>Fourteen ophthalmology residents from a single institution were stratified by ...[more]

PMID: 27409466

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Project description:ObjectiveTo develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans.DesignCross-sectional study.SubjectsData from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation.MethodImage contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth).Main outcome measuresWe measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland-Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements.ResultsFor SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland-Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71-0.87 for angle width measurements, 0.54 for IT750, 0.83-0.85 for other iris measurements, and 0.89-0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program.ConclusionWe developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements.Financial disclosuresThe author(s) have no proprietary or commercial interest in any materials discussed in this article.

Project description:PurposeTo assess the effects of age and refractive status on anterior segment anatomical structures, including the ciliary body, using a new swept-source anterior segment optical coherence tomography (AS-OCT) device.MethodsThis prospective observational study included 63 healthy volunteers (mean age: 44.2 years). Images of the anterior segment were obtained using a new swept-source AS-OCT (ANTERION, Heidelberg Engineering GmbH, Heidelberg, Germany) with tracking and image averaging from the right eye of all participants. Repeatability as well as inter- and intra-observer reliability of biometric measurements were evaluated. The impact of image tracking and averaging on ciliary muscle measurements was tested. Univariate and multivariable statistical models were developed to evaluate the relationship of age and refractive status on anterior segment biometric measurements.ResultsFor all test-retest repeatability and inter- and intra-observer reproducibility of swept-source AS-OCT measurements, high intraclass correlation (ICC) was noted (0.88-1.00). The nasal maximum ciliary muscle thickness (CMTMAX) and distance between scleral spur to the thickest point of the ciliary muscle (SSMAX) were larger than those on the temporal side (p<0.001 and p = 0.006, respectively). Nasal and temporal CMTMAX (p = 0.004 and p<0.001, respectively) and lens thickness (p<0.01) increased with age. Nasal and temporal SSMAX decreased with older age and increasing hyperopia (p = 0.01 and p<0.001, respectively). Image averaging resulted in improved ciliary muscle measurements (p = 0.008 to 0.02). Lens vault increased with older age and increased hyperopia (p<0.01). OCT measurements of the angle decreased with older age and increased hyperopia (p<0.001 to 0.03). Aqueous depth decreased with older age and increased hyperopia (p<0.01). Pupil diameter decreased with older age (p<0.01).ConclusionsRepeatability and reproducibility of biometric measurements using the ANTERION AS-OCT were excellent. Image averaging improved the accuracy of ciliary muscle measurements. The device produced measurements of biometric parameters that described superficial and deep structures including the ciliary body and full lens thickness from a single image.

Dataset Information

Impact of Microscope-Integrated OCT on Ophthalmology Resident Performance of Anterior Segment Surgical Maneuvers in Model Eyes.

Purpose

Methods

Results

Conclusions

Publications

Impact of Microscope-Integrated OCT on Ophthalmology Resident Performance of Anterior Segment Surgical Maneuvers in Model Eyes.

Similar Datasets

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