Automatic intraocular lens segmentation and detection in optical coherence tomography images

Prediction of IOL decentration, tilt and axial position using anterior segment OCT data

BACKGROUND

Intraocular lenses (IOLs) require proper positioning in the eye to provide good imaging performance. This is especially important for premium IOLs. The purpose of this study was to develop prediction models for estimating IOL decentration, tilt and the axial IOL equator position (IOLEQ) based on preoperative biometric and tomographic measures.

METHODS

Based on a dataset (N = 250) containing preoperative IOLMaster 700 and pre-/postoperative Casia2 measurements from a cataractous population, we implemented shallow feedforward neural networks and multilinear regression models to predict the IOL decentration, tilt and IOLEQ from the preoperative biometric and tomography measures. After identifying the relevant predictors using a stepwise linear regression approach and training of the models (150 training and 50 validation data points), the performance was evaluated using an N = 50 subset of test data.

RESULTS

In general, all models performed well. Prediction of IOL decentration shows the lowest performance, whereas prediction of IOL tilt and especially IOLEQ showed superior performance. According to the 95% confidence intervals, decentration/tilt/IOLEQ could be predicted within 0.3 mm/1.5°/0.3 mm. The neural network performed slightly better compared to the regression, but without significance for decentration and tilt.

CONCLUSION

Neural network or linear regression-based prediction models for IOL decentration, tilt and axial lens position could be used for modern IOL power calculation schemes dealing with 'real' IOL positions and for indications for premium lenses, for which misplacement is known to induce photic effects and image distortion.

Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

PURPOSE

To develop and validate a deep learning model to automatically segment three structures using an anterior segment optical coherence tomography (AS-OCT): The intraocular lens (IOL), the retrolental space (IOL to the posterior lens capsule) and Berger's space (BS; posterior capsule to the anterior hyaloid membrane).

METHODS

An artificial intelligence (AI) approach based on a deep learning model to automatically segment the IOL, the retrolental space, and BS in AS-OCT, was trained using annotations from an experienced clinician. The training, validation and test set consisted of 92 cross-sectional OCT slices, acquired in 47 visits from 41 eyes. Annotations from a second experienced clinician in the test set were additionally evaluated to conduct an inter-reader variability analysis.

RESULTS

The AI model achieved a Precision/Recall/Dice score of 0.97/0.90/0.93 for IOL, 0.54/0.65/0.55 for retrolental space, and 0.72/0.58/0.59 for BS. For inter-reader variability, Precision/Recall/Dice values were 0.98/0.98/0.98 for IOL, 0.74/0.59/0.62 for retrolental space, and 0.58/0.57/0.57 for BS. No statistical differences were observed between the automated algorithm and the inter-reader variability for BS segmentation.

CONCLUSION

The deep learning model allows for fully automatic segmentation of all investigated structures, achieving human-level performance in BS segmentation. We, therefore, expect promising applications of the algorithm with particular interest in BS in automated big data analysis and real-time intra-operative support in ophthalmology, particularly in conjunction with primary posterior capsulotomy in femtosecond laser-assisted cataract surgery.

Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens

Background

Cataract surgery has been recently developed from sight rehabilitating surgery to accurate refractive surgery. The precise concentration of intraocular lens (IOL) is crucial for postoperative high visual quanlity. The three-dimentional (3D) images of ocular anterior segment captured by optial coherence tomography (OCT) make it possible to evaluate the IOL position in 3D space, which provide insights into factors relavant to the visual quanlity and better design of new functional IOL. The deep learning algorithm potentially quantify the IOL position in an objective and efficient way.

Methods

The region-based fully convolutional network (R-FCN) was used to recogonize and delineate the IOL configuration in 3D OCT images. Scleral spur was identified automatically. Then the tilt angle of the IOL relative to the scleral spur plane along with its decentration with respect to the pupil were calculated. Repeatability and reliability of the method was evaluated by the intraclass correlation coefficient.

Results

After improvement, the R-FCN network recognition efficiency of IOL configuration reached 0.910. The ICC of reliability and repeatability of the method is 0.867 and 0.901. The average tilt angle of the IOL relative to scleral spur is located in 1.65±1.00 degrees. The offsets dx and dy occurring in the early X and Y directions of the IOL are 0.29±0.22 and 0.33±0.24 mm, respectively. The IOL offset distance is 0.44±0.33 mm.

Conclusions

We proposed a practical method to quantify the IOL postion in 3D space based on OCT images and assisted by an algorithm.

Application of artificial intelligence in ophthalmology

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.

[Corneal power after descemet stripping automated endothelial keratoplasty (DSAEK) - Modeling and concept for calculation of intraocular lenses]

BACKGROUND AND PURPOSE

Descemet stripping automated endothelial keratoplasty (DSAEK) is an established treatment option for pathologies of the corneal endothelium. It is typically accompanied with a hyperopic shift in refraction. The purpose of this work is to predict corneal geometry after DSAEK based on model data and to present a concept how to determine corneal power, e.g. for intraocular power calculation to prevent a refractive surprise with a subsequent cataract surgery.

MATERIAL AND METHODS

Based on data of the Kooijman schematic model eye we simulated a microkeratome cut parallel to the corneal front surface for donor trephination to determine the radial thickness profile of the posterior corneal donor lamella. This donor lamella was tension-neutrally adapted to the back surface of the host and the profile of the cornea after DSAEK was derived and characterized by a quadric surface. Comparison with the curvature of the host without and with donor could resample hyperopic shift which was published in literature. A method was shown how to determine corneal power after DSAEK.

RESULTS

From the data of the Kooijman schematic model eye and the donor characteristics central / peripheral corneal thickness was increased by 150 / 250μm due to adaptation of the donor lamella. Geometry of corneal back surface showed a reduced radius of curvature (by about 0.9mm) and a change in conic constant (by about -0.13). Persistent clinically observed hyperopic shift correlates to the change in geometry of the cornea due to adaptation of the donor lamella, which reduces corneal power by 0.88 D.

CONCLUSION

DSAEK leads to a hyperopic shift in refraction, which can be explained by a change in corneal back surface geometry. In case of subsequent cataract surgery, the intraocular lens power should be calculated with consideration of both corneal surfaces rather than keratometry or corneal topography in order to minimize a systematic hyperopic shift due to misinterpretation of corneal power after DSAEK. In case of a Triple-DSAEK, a target refraction of -1.5 D should be chosen in order to safely prevent postoperative hyperopia.