Descemet Membrane Endothelial Keratoplasty and Bowman Layer Transplantation: An Anatomic Review and Historical Survey

Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery

OBJECTIVES

In this study, we developed a machine learning approach for postoperative corneal endothelial cell images of patients who underwent Descemet's membrane keratoplasty (DMEK).

METHODS

An AlexNet model is proposed and validated throughout the study for endothelial cell segmentation and cell location determination. The 506 images of postoperative corneal endothelial cells were analyzed. Endothelial cell detection, segmentation, and determining of its polygonal structure were identified. The proposed model is based on the training of an R-CNN to locate endothelial cells. Next, by determining the ridges separating adjacent cells, the density and hexagonality rates of DMEK patients are calculated.

RESULTS

The proposed method reached accuracy and F1 score rates of 86.15 % and 0.857, respectively, which indicates that it can reliably replace the manual detection of cells in vivo confocal microscopy (IVCM). The AUC score of 0.764 from the proposed segmentation method suggests a satisfactory outcome.

CONCLUSIONS

A model focused on segmenting endothelial cells can be employed to assess the health of the endothelium in DMEK patients.

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

Objective

In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload.

Methods

A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.

Results

The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.

Conclusion

A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

Indications for keratoplasty in management of corneal ectasia

PURPOSE OF REVIEW

The current review aims to describe recent advancements in treatment of corneal ectasias and its effect on indications for corneal transplantation.

RECENT FINDINGS

The majority of patients affected by ectatic corneal disease use contact lenses to correct resulting astigmatism. Patients who are intolerant of contact lenses or cannot achieve acceptable vision through conservative measures could consider keratoplasty. However, continuing advancements in both nonsurgical and surgical treatments are either reducing or delaying the need for keratoplasty in patients affected by ectatic corneal disease.

SUMMARY

Corneal transplantation has been the mainstay of treatment for patients with advanced ectatic corneal disease. In the past decade, numerous improvements have been occurred to make contact lenses not only more effective for visual correction, but also more comfortable. Although corneal cross-linking is the only proven treatment known to prevent progression of disease, several other therapies show early potential for those in which cross-linking is contraindicated. Patients now have access to a wider range of therapies before considering keratoplasty.