A multi-center study of prediction of macular hole status after vitrectomy and internal limiting membrane peeling by a deep learning model

Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases

OBJECTIVE

To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases.

BACKGROUND

Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and noninvasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize 3-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverage on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases.

CONCLUSION

This review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations

Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high fineness and stability of surgeons. The massive demand of the AI assist ophthalmic surgery will constitute an important factor in boosting accelerate precision medicine. In clinical practice, it is instrumental to update and review the considerable evidence of the current AI technologies utilized in the investigation of ophthalmic surgery involved in both the progression and innovation of precision medicine. Bibliographic databases including PubMed and Google Scholar were searched using keywords such as "ophthalmic surgery", "surgical selection", "candidate screening", and "robot-assisted surgery" to find articles about AI technology published from 2018 to 2023. In addition to the Editorials and letters to the editor, all types of approaches are considered. In this paper, we will provide an up-to-date review of artificial intelligence in eye surgery, with a specific focus on its application to candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance.

Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study

PURPOSE

To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.

METHODS

Six thousand six hundred and sixty-one digital images of RRD treated by vitrectomy and internal tamponade were collected from the British and Eire Association of Vitreoretinal Surgeons database. Each image was classified as a primary surgical success or a primary surgical failure. The synthetic minority over-sampling technique was used to address class imbalance. We adopted the state-of-the-art deep convolutional neural network architecture Inception v3 to train, validate, and test deep learning models to predict the anatomical outcome of RRD surgery. The area under the curve (AUC), sensitivity, and specificity for predicting the outcome of RRD surgery was calculated for the best predictive deep learning model.

RESULTS

The deep learning model was able to predict the anatomical outcome of RRD surgery with an AUC of 0.94, with a corresponding sensitivity of 73.3% and a specificity of 96%.

CONCLUSION

A deep learning model is capable of accurately predicting the anatomical outcome of RRD surgery. This fully automated model has potential application in surgical care of patients with RRD.

The Importance of Anatomic Configuration and Cystic Changes in Macular Hole: Predicting Surgical Success with a Different Approach

PURPOSE

This study aimed to define a novel metric for the area of the macular hole (MH) and cysts located around the hole using an optical coherence tomography (OCT) device.

METHODS

This study was conducted with 58 eyes of 56 patients. The patients were divided into two groups according to anatomic closure after surgery. Using the metrics of macular hole index (MHI), tractional hole index (THI), hole forming factor (HFF), macular hole area (HA), the cystoid space areas in the inner retinal layers (CA), and our novel metric, the cyst hole area index (CHAI) was calculated. The correlation of the CA, the HA, and the CHAI with other indexes were assessed. Receiver operating characteristic (ROC) curves and cut-off values were derived for indexes predicting type 1 or type 2 closures.

RESULTS

The CA showed a strong positive correlation with the base MH size and the maximum MH height (r = 0.624, p < 0.001; r = 0.722, p < 0.001, respectively). The HA showed a strong positive correlation with basal MH size and minimum MH size (r = 0.934, p < 0.001; r = 0.765, p < 0.001). The HA showed a moderate positive correlation with maximum MH height (r = 0.483, p < 0.001, respectively). CHAI showed a moderate positive correlation with minimum MH size (r = 0.297, p = 0.02). CHAI and HA showed a moderate negative correlation with post-operative BCVA (r = -0.39, p = 0.003; r = -0.357, p = 0.006; respectively). ROC curve analysis showed that MHI (0.823), THI (0.750), and HFF (0.722) predicted type 1 closure and that CHAI (0.769) and HA (0.709) predicted type 2 closures.

CONCLUSION

MHI and our novel index CHAI, which can be calculated without any additional software, could successfully predict type 1 and type 2 closures, respectively.

Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume

BACKGROUND AND OBJECTIVE

To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images.

PATIENTS AND METHODS

This was a proof-of-concept consecutive case series. Patients with an idiopathic full-thickness MH undergoing pars plana vitrectomy surgery with 1 year of follow-up were considered for inclusion. MHs were manually graded by a vitreoretinal surgeon from preoperative OCT images to delineate MH volume. This information was used to train a fully three-dimensional convolutional neural network for automatic segmentation. The main outcome was the correlation of manual MH volume to automated volume segmentation.

RESULTS

The correlation between manual and automated MH volume was R² = 0.94 (n = 24). Automated MH volume demonstrated a higher correlation to change in visual acuity from preoperative to the postoperative 1-year time point compared with the minimum linear diameter (volume: R² = 0.53; minimum linear diameter: R² = 0.39).

CONCLUSION

MH automated volume segmentation on OCT imaging demonstrated high correlation to manual MH volume measurements. [Ophthalmic Surg Lasers Imaging Retina. 2022;53(4):208-214.].

Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features

Purpose

The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA) improvement after macular hole (MH) surgery from a combined model using DL on high-definition optical coherence tomography (HD-OCT) B-scans and clinical features.

Methods

We trained a DL convolutional neural network (CNN) using pre-operative HD-OCT B-scans of the macula and combined with a logistic regression model of pre-operative clinical features to predict VA increase ≥15 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at 6 months post-vitrectomy in closed MHs. A total of 121 MHs with 242 HD-OCT B-scans and 484 clinical data points were used to train, validate, and test the model. Prediction of VA increase was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 scores. We also extracted the weight of each input feature in the hybrid model.

Results

All performances are reported on the held-out test set, matching results obtained with cross-validation. Using a regression on clinical features, the AUROC was 80.6, with an F1 score of 79.7. For the CNN, relying solely on the HD-OCT B-scans, the AUROC was 72.8 ± 14.6, with an F1 score of 61.5 ± 23.7. For our hybrid regression model using clinical features and CNN prediction, the AUROC was 81.9 ± 5.2, with an F1 score of 80.4 ± 7.7. In the hybrid model, the baseline VA was the most important feature (weight = 59.1 ± 6.9%), while the weight of HD-OCT prediction was 9.6 ± 4.2%.

Conclusions

Both the clinical data and HD-OCT models can predict postoperative VA improvement in patients undergoing vitrectomy for a MH with good discriminative performances. Combining them into a hybrid model did not significantly improve performance.

Translational Relevance

OCT-based DL models can predict postoperative VA improvement following vitrectomy for MH but fusing those models with clinical data might not provide improved predictive performance.