Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023)

BACKGROUND AND OBJECTIVES

Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases.

METHOD

The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review.

RESULTS

During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.

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.

Prediction of vitreomacular traction syndrome outcomes with deep learning: A pilot study

PURPOSE

To investigate the potential of an Optical Coherence Tomography (OCT) based Deep-Learning (DL) model in the prediction of Vitreomacular Traction (VMT) syndrome outcomes.

DESIGN

A single-centre retrospective review.

METHODS

Records of consecutive adult patients attending the Royal Adelaide Hospital vitreoretinal clinic with evidence of spontaneous VMT were reviewed from January 2019 until May 2022. All patients with evidence of causes of cystoid macular oedema or secondary causes of VMT were excluded. OCT scans and outcome data obtained from patient records was used to train, test and then validate the models.

RESULTS

For the deep learning model, ninety-five patient files were identified from the OCT (SPECTRALIS system; Heidelberg Engineering, Heidelberg, Germany) records. 25% of the patients spontaneously improved, 48% remained stable and 27% had progression of their disease, approximately. The final longitudinal model was able to predict 'improved' or 'stable' disease with a positive predictive value of 0.72 and 0.79, respectively. The accuracy of the model was greater than 50%.

CONCLUSIONS

Deep-learning models may be utilised in real-world settings to predict outcomes of VMT. This approach requires further investigation as it may improve patient outcomes by aiding ophthalmologists in cross-checking management decisions and reduce the need for unnecessary interventions or delays.

Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

This study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperative swept-source OCT data were collected from 150 patients with successfully closed FTMH using 6 × 6 mm2 macular volume scan datasets. Randomly selected and augmented 120,000 training and 5000 validation pairs of OCT images were used to train the GDLM. We assessed the accuracy and F1 score of concordance for neurosensory retinal areas, performed Bland-Altman analysis of foveolar height (FH) and mean foveal thickness (MFT), and predicted postoperative external limiting membrane (ELM) and ellipsoid zone (EZ) restoration accuracy between artificial intelligence (AI)-OCT and ground truth (GT)-OCT images. Accuracy and F1 scores were 94.7% and 0.891, respectively. Average FH (228.2 vs. 233.4 μm, P = 0.587) and MFT (271.4 vs. 273.3 μm, P = 0.819) were similar between AI- and GT-OCT images, within 30.0% differences of 95% limits of agreement. ELM and EZ recovery prediction accuracy was 88.0% and 92.0%, respectively. The proposed GDLM accurately predicted macular OCT images following FTMH surgery, aiding patient and surgeon understanding of postoperative macular features.

Novel Artificial Intelligence-Based Assessment of Imaging Biomarkers in Full-Thickness Macular Holes: Preliminary Data from a Pivotal Trial

Artificial intelligence (AI)- and deep learning (DL)-based systems have shown significant progress in the field of macular disorders, demonstrating high performance in detecting retinal fluid and assessing anatomical changes during disease progression. This study aimed to validate an AI algorithm for identifying and quantifying prognostic factors in visual recovery after macular hole (MH) surgery by analyzing major optical coherence tomography (OCT) biomarkers. This study included 20 patients who underwent vitrectomy for a full-thickness macular hole (FTMH). The mean diameter of the FTMH was measured at 285.36 ± 97.4 μm. The preoperative best-corrected visual acuity (BCVA) was 0.76 ± 0.06 logMAR, improving to 0.38 ± 0.16 postoperatively, with a statistically significant difference (p = 0.001). AI software was utilized to assess biomarkers, such as intraretinal fluid (IRF) and subretinal fluid (SRF) volume, external limiting membrane (ELM) and ellipsoid zone (EZ) integrity, and retinal hyperreflective foci (HRF). The AI analysis showed a significant decrease in IRF volume, from 0.08 ± 0.12 mm3 preoperatively to 0.01 ± 0.01 mm3 postoperatively. ELM interruption improved from 79% ± 18% to 34% ± 37% after surgery (p = 0.006), whereas EZ interruption improved from 80% ± 22% to 40% ± 36% (p = 0.007) postoperatively. Additionally, the study revealed a negative correlation between preoperative IRF volume and postoperative BCVA recovery, suggesting that greater preoperative fluid volumes may hinder visual improvement. The integrity of the ELM and EZ was found to be essential for postoperative visual acuity improvement, with their disruption negatively impacting visual recovery. The study highlights the potential of AI in quantifying OCT biomarkers for managing MHs and improving patient care.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images

AIM

To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages, limitations, and possible solutions common to all tasks.

METHODS

We searched three academic databases, including PubMed, Web of Science, and Ovid, with the date of August 2022. We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords, of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.

RESULTS

Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance, including diabetic retinopathy, glaucoma, age-related macular degeneration, retinal vein occlusions, retinal detachment, and other peripheral retinal diseases. Compared to fundus images, the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200° in a single exposure, which can observe more areas of the retina.

CONCLUSION

The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

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.

Inter- and intra-observer agreement in the measurement of macular holes by optical coherence tomography

BACKGROUND AND OBJECTIVE

A full-thickness macular hole ("FTMH") is a foveal lesion caused by a defect in the full thickness of the neurosensory retina. Its diagnosis and the indication for surgical treatment take into account the measurement of the hole according to the tool provided by the OCT. This measurement can be performed by several ophthalmologists during the follow-up of a patient. The aim of this study is to find out whether there is intra-individual and inter-individual variability in these measurements.

MATERIAL AND METHODS

Retrospective review of OCT b-scan images with a diagnosis of FTMH. Measurements of the minimum diameter of the FTMH were performed using the hand-held tool available on the DRI-Triton (Topcon, Japan) at 1:1 and 1:2 scales, on different days, by 2 retina specialists and 2 residents. These measurements were compared to assess inter-observer and intra-observer correspondence.

RESULTS

Thirty-four images were analysed. For intra-observer variability, a correlation index higher than 0.98 was obtained in all cases. For inter-observer variability, the intra-class correlation coefficient was 0.94 (95% CI: 0.91-0.97) for the 1:1 scale, and 0.94 (95% CI: 0.91-0.97) for the 1:2 scale.

CONCLUSIONS

OCT-measured AMEC size values are reproducible between ophthalmic specialists and residents and are independent of the imaging scale at which the measurement is made.

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.