Application of artificial intelligence in cataract management: current and future directions

Influencing factors and prediction model construction of posterior capsular opacification after intraocular lens implantation treated with Nd: YAG laser

OBJECTIVE

To explore the influencing factors of posterior capsular opacification (Posterior Capsular Opacification, PCO) after intraocular lens implantation treated with Nd: YAG(Neodymium: Yttrium-Aluminum-Garnet) laser and to establish a predictive model to evaluate its risk.

METHODS

From January 2018 to June 2023, the data of 312 patients with posterior capsule opacification and Nd: YAG laser treatment in our hospital were retrospectively analyzed. All patients were randomly divided into training group (218 cases) and verification group (94 cases) at the ratio of 7:3. In the training set, the independent risk factors of posterior capsule opacification before operation were identified by multivariate Logistic regression analysis, and the nomogram prediction model was constructed. By drawing ROC (receiver operating characteristic) curve and calibration curve, the prediction effectiveness of the model is evaluated, and the verification is carried out in the verification set, and its clinical application value is explored by Decision Curve Analysis (DCA).

RESULTS

Among 312 patients, 84 (22.92%) developed PCO. The logistic results showed that age ≥ 60 years, extracapsular excision surgery, multifocal intraocular lens, axial length ≥ 24 mm, preoperative visual acuity < 0.3, high laser energy, and large posterior capsule incision aperture were associated with the occurrence of PCO (P < 0.05). The C-index indexes of the nomograph model were 0.870 and 0.842 in the training set and verification set, respectively, and the average was absolute. In the Hosmer-Lemeshow test, the χ2 values of the training set and the verification set are 4.007(P = 0.856) and 2.841(P = 0.943), respectively. The ROC curve shows that the AUC(Area Under Curve) values of the training set and the verification set are 0.870 (95% CI: 0.810-0.929) and 0.843 (95% CI: 0.732-0.954) respectively, and the combination of sensitivity and specificity is 0.792, 0.810, 0.765 and 0.792 respectively.

CONCLUSION

The nomogram prediction model based on Nd: YAG laser treatment of PCO risk factors after intraocular lens implantation has high accuracy and calibration, which can provide a key reference for formulating preventive measures, help to reduce the incidence of PCO and improve the prognosis of patients.

Artificial Intelligence Applications in Ophthalmology

Ophthalmology is well suited for the integration of artificial intelligence (AI) owing to its reliance on various imaging modalities, such as anterior segment photography, fundus photography, and optical coherence tomography (OCT), which generate large volumes of high-resolution digital images. These images provide rich datasets for training AI algorithms, which enables precise diagnosis and monitoring of various ocular conditions. Retinal disease management heavily relies on image recognition. Limited access to ophthalmologists in underdeveloped areas and high image volumes in developed countries make AI a promising, cost-effective solution for screening and diagnosis. In corneal diseases, differential diagnosis is critical yet challenging because of the wide range of potential etiologies. AI and diagnostic technologies offer promise for improving the accuracy and speed of these diagnoses, including the differentiation between infectious and noninfectious conditions. Smartphone imaging coupled with AI technology can advance the diagnosis of anterior segment diseases, democratizing access to eye care and providing rapid and reliable diagnostic results. Other potential areas for AI applications include cataract and vitreous surgeries as well as the use of generative AI in training ophthalmologists. While AI offers substantial benefits, challenges remain, including the need for high-quality images, accurate manual annotations, patient heterogeneity considerations, and the "black-box phenomenon". Addressing these issues is crucial for enhancing the effectiveness of AI and ensuring its successful integration into clinical practice. AI is poised to transform ophthalmology by increasing diagnostic accuracy, optimizing treatment strategies, and improving patient care, particularly in high-risk or underserved populations.

The role of artificial intelligence in macular hole management: A scoping review

We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We synthesize 25 studies, comprehensively reporting on each AI model's development strategy, validation, tasks, performance, strengths, and limitations. All models analyzed ophthalmic images, and 5 (20 %) also analyzed clinical features. Study objectives were categorized based on 3 stages of MH care: diagnosis, identification of MH characteristics, and postoperative predictions of hole closure and vision recovery. Twenty-two (88 %) AI models underwent supervised learning, and the models were most often deployed to determine a MH diagnosis. None of the articles applied AI to guiding treatment plans. AI model performance was compared to other algorithms and to human graders. Of the 10 studies comparing AI to human graders (i.e., retinal specialists, general ophthalmologists, and ophthalmology trainees), 5 (50 %) reported equivalent or higher performance. Overall, AI analysis of images and clinical characteristics in MH demonstrated high diagnostic and predictive accuracy. Convolutional neural networks comprised the majority of included AI models, including those which were high performing. Future research may consider validating algorithms to propose personalized treatment plans and explore clinical use of the aforementioned algorithms.

Refractive Outcomes After Cataract Surgery-The Impact of Preoperative Visual Acuity, the Intraocular Lens Model, and the Surgeon's Experience: An Empirical Analysis of Hungarian and Kosovan Patients

Background/Objectives: Phacoemulsification and intraocular lens (IOL) implantation comprise a standard procedure for cataract treatment. However, minimal refractive error remains a determinant of postoperative results. Our study aimed to evaluate the refractive outcomes and the impact of the surgeon's experience and the IOL model on Kosovan and Hungarian patients after cataract surgery. Methods: This study included the preoperative and postoperative data of 1417 patients scheduled to undergo cataract surgery with IOL implantation at two centers: the Ophthalmology Department of Semmelweis University, Budapest, Hungary, and the Ophthalmology Department of the University Clinical Center of Kosovo, Prishtina, Kosovo. STATA and SPSS were used for statistical analysis. Results: The data of 1001 Hungarian and 416 Kosovan patients were included in this study. There was a statistically significant difference between the groups in the 1-month postoperative best-corrected distance visual acuity (BCDVA) (p = 0.001); in the Hungarian patients, the 1-month BCDVA was 85.2%, while in the Kosovan patients, it was 49.6%. Of the 14 different IOLs implanted in the Hungarian patients, the AcrySof IQ toric SN6AT, FineVision HP (POD F GF), and 677MTY IOLs resulted in a statistically significant positive impact on the 1-month postoperative visual acuity (p < 0.05). The AcrySof SA60AT and Akreos ADAPT AO, implanted in the Kosovan patients, had a statistically significant positive impact on the 1-month postoperative visual acuity (p < 0.05). More extensive surgeon experience had a statistically significant positive impact on postoperative outcomes (p < 0.00). Conclusions: Multifocal and toric IOLs showed superiority in terms of postoperative outcomes in our study; therefore, we conclude that greater surgeon experience, the availability of premium IOLs, and appropriate IOL selection have a considerable impact on refractive outcomes after cataract surgery.

Artificial intelligence applications in cataract and refractive surgeries

PURPOSE OF REVIEW

This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field.

RECENT FINDINGS

Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring.

SUMMARY

The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.

Applications of Artificial Intelligence in Cataract Surgery: A Review

Cataract surgery is one of the most performed procedures worldwide, and cataracts are rising in prevalence in our aging population. With the increasing utilization of artificial intelligence (AI) in the medical field, we aimed to understand the extent of present AI applications in ophthalmic microsurgery, specifically cataract surgery. We conducted a literature search on PubMed and Google Scholar using keywords related to the application of AI in cataract surgery and included relevant articles published since 2010 in our review. The literature search yielded information on AI mechanisms such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) as they are being incorporated into pre-operative, intraoperative, and post-operative stages of cataract surgery. AI is currently integrated in the pre-operative stage of cataract surgery to calculate intraocular lens (IOL) power and diagnose cataracts with slit-lamp microscopy and retinal imaging. During the intraoperative stage, AI has been applied to risk calculation, tracking surgical workflow, multimodal imaging data analysis, and instrument location via the use of "smart instruments". AI is also involved in predicting post-operative complications, such as posterior capsular opacification and intraocular lens dislocation, and organizing follow-up patient care. Challenges such as limited imaging dataset availability, unstandardized deep learning analysis metrics, and lack of generalizability to novel datasets currently present obstacles to the enhanced application of AI in cataract surgery. Upon addressing these barriers in upcoming research, AI stands to improve cataract screening accessibility, junior physician training, and identification of surgical complications through future applications of AI in cataract surgery.

Self-supervised learning-enhanced deep learning method for identifying myopic maculopathy in high myopia patients

Accurate detection and timely care for patients with high myopia present significant challenges. We developed a deep learning (DL) system enhanced by a self-supervised learning (SSL) approach to improve the automatic diagnosis of myopic maculopathy (MM). Using a dataset of 7,906 images from the Shanghai High Myopia Screening Project and a public validation set of 1,391 images from MMAC2023, our method significantly outperformed conventional techniques. Internally, it achieved 96.8% accuracy, 83.1% sensitivity, and 95.6% specificity, with AUC values of 0.982 and 0.999. Externally, it maintained 89.0% accuracy, 71.7% sensitivity, and 87.8% specificity, with AUC values of 0.978 and 0.973. The model's Cohen's kappa values exceeded 0.8, indicating substantial agreement with retinal experts. Our SSL-enhanced DL approach offers high accuracy and potential to enhance large-scale myopia screenings, demonstrating broader significance in improving early detection and treatment of MM.

Lens Fragmentation with Picosecond Laser Pulses After Artificial Cataract Induction with Microwaves

Objectives: In this work we demonstrate the first laboratory study results of lens fragmentation with low-energy picosecond ultrashort laser pulses after artificial induction of cataract with microwave radiation on an ex vivo animal model. Background: This method will be evaluated with regard to the further development of lens fragmentation with novel ultrashort picosecond laser systems instead of ultrasonic phacoemulsification or the significantly more complex femtosecond laser fragmentation. Methods: As samples we used postmortem porcine eyes. The lenses were dissected and then irradiated in a microwave oven for artificial cataract induction. Subsequent computer-driven lens fragmentation was performed with a 12 ps, 1064 nm pulsed laser source with 100 µJ pulse energy, and 10 kHz pulse repetition rate. Results: Both the artificial cataract induction and the lens fragmentation were demonstrated. When inducing cataract, different degrees/stages of opaqueness and hardness could be achieved with different irradiation times and methods. The fragmentation with 12 ps pulses led to good results with regard to ablation depth and rate, especially for the softer lenses. Conclusions: As could be shown, low-energy picosecond ultrashort laser pulses are feasible for cataractous lens fragmentation on an ex vivo animal model with artificial cataract induction. Thus, this technique may influence future cataract surgeries by possibly being an alternative or extension to state-of-the-art methods. This will be evaluated with further tests and studies.

A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality

Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.

Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review

Artificial intelligence (AI) holds immense promise for transforming ophthalmic care through automated screening, precision diagnostics, and optimized treatment planning. This paper reviews recent advances and challenges in applying AI techniques such as machine learning and deep learning to major eye diseases. In diabetic retinopathy, AI algorithms analyze retinal images to accurately identify lesions, which helps clinicians in ophthalmology practice. Systems like IDx-DR (IDx Technologies Inc, USA) are FDA-approved for autonomous detection of referable diabetic retinopathy. For glaucoma, deep learning models assess optic nerve head morphology in fundus photographs to detect damage. In age-related macular degeneration, AI can quantify drusen and diagnose disease severity from both color fundus and optical coherence tomography images. AI has also been used in screening for retinopathy of prematurity, keratoconus, and dry eye disease. Beyond screening, AI can aid treatment decisions by forecasting disease progression and anti-VEGF response. However, potential limitations such as the quality and diversity of training data, lack of rigorous clinical validation, and challenges in regulatory approval and clinician trust must be addressed for the widespread adoption of AI. Two other significant hurdles include the integration of AI into existing clinical workflows and ensuring transparency in AI decision-making processes. With continued research to address these limitations, AI promises to enable earlier diagnosis, optimized resource allocation, personalized treatment, and improved patient outcomes. Besides, synergistic human-AI systems could set a new standard for evidence-based, precise ophthalmic care.

Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students

Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.

Systematic bibliometric analysis of research hotspots and trends on the application of premium IOLs in the past 2 decades

AIM

To analysis of research hotspots and trends on the application of premium intraocular lens (PIOLs) in the past 2 decades.

METHODS

The literature search was performed on the Web of Science and included PIOLs studies published between January 2000 and December 2022. The retrieved literature was collated and analyzed by R-tool's Bibliometrix package, CitNetExplorer, CiteSpace and other software.

RESULTS

A total of 1801 articles about PIOLs were obtained, most of which were published in Spain and the United States. The organization that published the most articles was the University of Valencia in Spain. Alió JL, and Montés-Micó R, from Spain were the most influential authors in this field. The Journal of Cataract and Refractive Surgery and Journal of Refractive Surgery were the core journals for this field; the top 10 cited articles mainly focus on postoperative satisfaction with multifocal intraocular lens (IOLs) and postoperative results of toric IOLs. Through the keyword analysis, we found that trifocal IOLs, astigmatism and extended depth of focus (EDoF) IOLs are the most discussed topics at present, and the importance of astigmatism and the clinical application of the new generation of PIOLs are the emerging research trends.

CONCLUSION

Bibliometric analysis can effectively help to identify multilevel concerns in PIOLs research and the prevailing research trends in the realm of PIOLs encompass the adoption of EDoF IOLs, trifocal IOLs, and their respective Toric models.

Artificial Intelligence in Hand Surgery - How Generative AI is Transforming the Hand Surgery Landscape

Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT by OpenAI in November 2022 marked a pivotal moment, showcasing the potential of generative AI in revolutionising patient care, diagnosis and treatment. Generative AI, unlike traditional AI systems, possesses the ability to generate new content by understanding patterns within datasets. This article explores the evolution of AI in healthcare, tracing its roots to the term coined by John McCarthy in 1955 and the contributions of pioneers like John Von Neumann and Alan Turing. Currently, generative AI, particularly Large Language Models, holds promise across three broad categories in healthcare: patient care, education and research. In patient care, it offers solutions in clinical document management, diagnostic support and operative planning. Notable advancements include Microsoft's collaboration with Epic for integrating AI into electronic medical records (EMRs), enhancing clinical data management and patient care. Furthermore, generative AI aids in surgical decision-making, as demonstrated in plastic, orthopaedic and hepatobiliary surgeries. However, challenges such as bias, hallucination and integration with EMR systems necessitate caution and ongoing evaluation. The article also presents insights from the implementation of NUHS Russell-GPT, a generative AI chatbot, in a hand surgery department, showcasing its utility in administrative tasks but highlighting challenges in surgical planning and EMR integration. The survey showed unanimous support for incorporating AI into clinical settings, with all respondents being open to its use. In conclusion, generative AI is poised to enhance patient care and ease physician workloads, starting with automating administrative tasks and evolving to inform diagnoses, tailored treatment plans, as well as aid in surgical planning. As healthcare systems navigate the complexities of integrating AI, the potential benefits for both physicians and patients remain significant, offering a glimpse into a future where AI transforms healthcare delivery. Level of Evidence: Level V (Diagnostic).

Smartphone Eye Examination: Artificial Intelligence and Telemedicine

Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.

A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence

PURPOSE

The proper selection of an intraocular lens power calculation formula is an essential aspect of cataract surgery. This study evaluated the accuracy of artificial intelligence-based formulas.

DESIGN

Systematic review.

METHODS

This review comprises articles evaluating the exactness of artificial intelligence-based formulas published from 2017 to July 2023. The papers were identified by a literature search of various databases (Pubmed/MEDLINE, Google Scholar, Crossref, Cochrane Library, Web of Science, and SciELO) using the terms "IOL formulas", "FullMonte", "Ladas", "Hill-RBF", "PEARL-DGS", "Kane", "Karmona", "Hoffer QST", and "Nallasamy". In total, 25 peer-reviewed articles in English with the maximum sample and the largest number of compared formulas were examined.

RESULTS

The scores of the mean absolute error and percentage of patients within ±0.5 D and ±1.0 D were used to estimate the exactness of the formulas. In most studies the Kane formula obtained the smallest mean absolute error and the highest percentage of patients within ±0.5 D and ±1.0 D. Second place was typically achieved by the PEARL DGS formula. The limitations of the studies were also discussed.

CONCLUSIONS

Kane seems to be the most accurate artificial intelligence-based formula. PEARL DGS also gives very good results. Hoffer QST, Karmona, and Nallasamy are the newest, and need further evaluation.

Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol

INTRODUCTION

Artificial intelligence (AI) development has led to improvements in many areas of medicine. Canada has workforce pressures in delivering cataract care. A potential solution is using AI technology that can automate care delivery, increase effectiveness and decrease burdens placed on patients and the healthcare system. This study assesses the use of 'Dora', an example of an AI assistant that is able to deliver a regulated autonomous, voice-based, natural-language consultation with patients over the telephone. Dora is used in routine practice in the UK, but this study seeks to assess the safety, usability, acceptability and cost-effectiveness of using the technology in Canada.

METHODS AND ANALYSIS

This is a two-phase prospective single-centred trial. An expected 250 patients will be recruited for each phase of the study. For Phase I of the study, Dora will phone patients at postoperative week 1 and for Phase II of the study, Dora will phone patients within 24hours of their cataract surgery and again at postoperative week 1. We will evaluate the agreement between Dora and a supervising clinician regarding the need for further review based on the patients' symptoms. A random sample of patients will undergo the System Usability Scale followed by an extended semi-structured interview. The primary outcome of agreement between Dora and the supervisor will be assessed using the kappa statistic. Qualitative data from the interviews will further gauge patient opinions about Dora's usability, appropriateness and level of satisfaction.

ETHICS AND DISSEMINATION

Research Ethics Board William Osler Health System (ID: 22-0044) has approved this study and will be conducted by guidelines of Declaration of Helsinki. Master-linking sheet will contain the patient chart identification (ID), full name, date of birth and study ID. Results will be shared through peer-reviewed journals and presentations at conferences.

Cataract surgery after corneal refractive surgery: preoperative considerations and management

PURPOSE OF REVIEW

Corneal refractive surgery (CRS) is one of the most popular eye procedures, with more than 40 million cases performed globally. As CRS-treated patients age and develop cataract, the number of cases that require additional preoperative considerations and management will increase around the world. Thus, we provide an up-to-date, concise overview of the considerations and outcomes of cataract surgery in eyes with previous CRS, including surface ablation, laser in-situ keratomileusis (LASIK), and small-incision lenticule extraction (SMILE).

RECENT FINDINGS

Challenges associated with accurate biometry in eyes with CRS have been mitigated recently through total keratometry, ray tracing, intraoperative aberrometry, and machine learning assisted intraocular lens (IOL) power calculation formulas to improve prediction. Emerging studies have highlighted the superior performance of ray tracing and/or total keratometry-based formulas for IOL power calculation in eyes with previous SMILE. Dry eye remains a common side effect after cataract surgery, especially in eyes with CRS, though the risk appears to be lower after SMILE than LASIK (in the short-term). Recent presbyopia-correcting IOL designs such as extended depth of focus (EDOF) IOLs may be suitable in carefully selected eyes with previous CRS.

SUMMARY

Ophthalmologists will increasingly face challenges associated with the surgical management of cataract in patients with prior CRS. Careful preoperative assessment of the ocular surface, appropriate use of IOL power calculation formulas, and strategies for presbyopia correction are key to achieve good clinical and refractive outcomes and patient satisfaction. Recent advances in CRS techniques, such as SMILE, may pose new challenges for such eyes in the future.

Insights into artificial intelligence in myopia management: from a data perspective

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Impact of Single Constant Optimization on the Precision of IOL Power Calculation

Purpose

The primary objective of this research is to examine how precision in intraocular lens calculation formulas can be impacted by zeroing the mean error through adjustments in the effective lens position value. Additionally, the study aims to evaluate how this modification influences outcomes differently based on the source of the prediction error.

Methods

In order to analyze the impact of individual variables on the standard deviation, the study maintained all variables constant except for one at a time. Subsequently, variations were introduced to specific parameters, such as corneal curvature radius, keratometric refractive index, axial length, and predicted implant position.

Results

According to our findings, when zeroing the mean error is applied to correct for inaccuracies in corneal power estimation, it results in a significant and exponential rise in standard deviation, thus adversely affecting the formula's precision. However, when zeroing is employed to compensate for prediction errors stemming from axial length measurements or predicted implant position, the effect on precision is minimal or potentially beneficial.

Conclusions

The study highlights the potential risks associated with the indiscriminate but necessary zeroing of prediction errors in implant power calculation formulas. The impact on formula precision greatly depends on the source of the error, underscoring the importance of error source when analyzing variations in the standard deviation of the prediction error after zeroing.

Translational Relevance

Our study contributes to the ongoing effort to enhance the accuracy and reliability of these formulas, thereby improving the surgical outcomes for cataract patients.

Recent evidence of economic evaluation of artificial intelligence in ophthalmology

PURPOSE OF REVIEW

Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed.

RECENT FINDINGS

Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models.

SUMMARY

Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.

Accuracy of an artificial intelligence-based mobile application for detecting cataracts: Results from a field study

Purpose

To assess the accuracy of e-Paarvai, an artificial intelligence-based smartphone application (app) that detects and grades cataracts using images taken with a smartphone by comparing with slit lamp-based diagnoses by trained ophthalmologists.

Methods

In this prospective diagnostic study conducted between January and April 2022 at a large tertiary-care eye hospital in South India, two screeners were trained to use the app. Patients aged >40 years and with a best-corrected visual acuity <20/40 were recruited for the study. The app is intended to determine whether the eye has immature cataract, mature cataract, posterior chamber intra-ocular lens, or no cataract. The diagnosis of the app was compared with that of trained ophthalmologists based on slit-lamp examinations, the gold standard, and a receiver operating characteristic (ROC) curve was estimated. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed.

Results

The two screeners used the app to screen 2,619 eyes of 1,407 patients. In detecting cataracts, the app showed high sensitivity (96%) but low specificity (25%), an overall accuracy of 88%, a PPV of 92.3%, and an NPV of 57.8%. In terms of cataract grading, the accuracy of the app was high in detecting immature cataracts (1,875 eyes, 94.2%), but its accuracy was poor in detecting mature cataracts (73 eyes, 22%), posterior chamber intra-ocular lenses (55 eyes, 29.3%), and clear lenses (2 eyes, 2%). We found that the area under the curve in predicting ophthalmologists' cataract diagnosis could potentially be improved beyond the app's diagnosis based on using images only by incorporating information about patient sex and age (P < 0.0001) and best-corrected visual acuity (P < 0.0001).

Conclusions

Although there is room for improvement, e-Paarvai app is a promising approach for diagnosing cataracts in difficult-to-reach populations. Integrating this with existing outreach programs can enhance the case detection rate.

Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory

PURPOSE

Estimating the surgery length has the potential to be utilized as skill assessment, surgical training, or efficient surgical facility utilization especially if it is done in real-time as a remaining surgery duration (RSD). Surgical length reflects a certain level of efficiency and mastery of the surgeon in a well-standardized surgery such as cataract surgery. In this paper, we design and develop a real-time RSD estimation method for cataract surgery that does not require manual labeling and is transferable with minimum fine-tuning.

METHODS

A regression method consisting of convolutional neural networks (CNNs) and long short-term memory (LSTM) is designed for RSD estimation. The model is firstly trained and evaluated for the single main surgeon with a large number of surgeries. Then, the fine-tuning strategy is used to transfer the model to the data of the other two surgeons. Mean Absolute Error (MAE in seconds) was used to evaluate the performance of the RSD estimation. The proposed method is compared with the naïve method which is based on the statistic of the historical data. A transferability experiment is also set to demonstrate the generalizability of the method.

RESULT

The mean surgical time for the sample videos was 318.7 s (s) (standard deviation 83.4 s) for the main surgeon for the initial training. In our experiments, the lowest MAE of 19.4 s (equal to about 6.4% of the mean surgical time) is achieved by our best-trained model for the independent test data of the main target surgeon. It reduces the MAE by 35.5 s (-10.2%) compared to the naïve method. The fine-tuning strategy transfers the model trained for the main target to the data of other surgeons with only a small number of training data (20% of the pre-training). The MAEs for the other two surgeons are 28.3 s and 30.6 s with the fine-tuning model, which decreased by -8.1 s and -7.5 s than the Per-surgeon model (average declining of -7.8 s and 1.3% of video duration). External validation study with Cataract-101 outperformed 3 reported methods of TimeLSTM, RSDNet, and CataNet.

CONCLUSION

An approach to build a pre-trained model for estimating RSD estimation based on a single surgeon and then transfer to other surgeons demonstrated both low prediction error and good transferability with minimum fine-tuning videos.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

Current Approach to the Pathogenesis of Diabetic Cataracts

Cataracts remain the first or second leading cause of blindness in all world regions. In the diabetic population, cataracts not only have a 3–5 times higher incidence than in the healthy population but also affect people at a younger age. In patients with type 1 diabetes, cataracts occur on average 20 years earlier than in the non-diabetic population. In addition, the risk of developing cataracts increases with the duration of diabetes and poor metabolic control. A better understanding of the mechanisms leading to the formation of diabetic cataracts enables more effective treatment and a holistic approach to the patient.

Big data in corneal diseases and cataract: Current applications and future directions

The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.

Artificial intelligence technology for myopia challenges: A review

Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.

Classification of dry and wet macular degeneration based on the ConvNeXT model

PURPOSE

To assess the value of an automated classification model for dry and wet macular degeneration based on the ConvNeXT model.

METHODS

A total of 672 fundus images of normal, dry, and wet macular degeneration were collected from the Affiliated Eye Hospital of Nanjing Medical University and the fundus images of dry macular degeneration were expanded. The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models. A total of 289 fundus images were used to test the models, and the classification results of the models on different datasets were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), accuracy, and kappa.

RESULTS

Using 289 fundus images, three-category models trained on the original and expanded datasets were assessed. The ConvNeXT model trained on the expanded dataset was the most effective, with a diagnostic accuracy of 96.89%, kappa value of 94.99%, and high diagnostic consistency. The sensitivity, specificity, F1-score, and AUC values for normal fundus images were 100.00, 99.41, 99.59, and 99.80%, respectively. The sensitivity, specificity, F1-score, and AUC values for dry macular degeneration diagnosis were 87.50, 98.76, 90.32, and 97.10%, respectively. The sensitivity, specificity, F1-score, and AUC values for wet macular degeneration diagnosis were 97.52, 97.02, 96.72, and 99.10%, respectively.

CONCLUSION

The ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.

Correlations of Immediate Corneal Tomography Changes with Preoperative and the Elapsed Phaco Parameters

Purpose

The ability to predict corneal edema and understand its relationship with imaging parameters enables optimization of decision-making in terms of cataract surgery. Therefore, we aimed to elucidate the immediate tomographic alterations after phacoemulsification.

Patients and Methods

In this prospective study, we evaluated clinical and corneal tomographic data of 30 patients with cataracts, obtained using a rotating Scheimpflug tomographic system before and after cataract surgery with a phacoemulsification system. Corneal thickness and volume were measured, and Pentacam Nucleus Staging, keratometry, and specular microscopy were performed preoperatively and immediately postoperatively. The Wilcoxon signed-rank test was used to compare pre-and postoperative values. We calculated the correlations between the changes in these values and multiple parameters related to phacodynamics, including “ultrasound (US) elapsed” (phaco time), “US average” (average power used), and “US absolute” (energy effectively dissipated, a product of the other two parameters).

Results

There were increases in corneal volume (p<0.0001) and pachymetry (p<0.0001), and a decrease in endothelial cell count (p<0.0001) after surgery. The mean differences in pre- and postoperative specular microscopy, corneal volume, and pachymetry were −335.13±236.21 cells/mm³, 1.33±0.56 mm³, and 61.33±23.73 microns, respectively. The difference in pre-and postoperative corneal volume in patients with US elapsed ≥40 s was 0.75 mm³ greater than that in patients with US elapsed <40 s (95% confidence interval [CI]: 0.24–1.25; p=0.005); that of pachymetry in patients with US elapsed ≥40 s was 31.76 microns greater than that in patients with US elapsed <40 s (95% CI: 9.55–53.97; p=0.007). Spearman correlation revealed that, for every 1% increase in cataract density, the US average value increased by 0.31% (coef.: 0.3110; 95% CI: 0.0741–0.5490; p=0.012).

Conclusion

Knowledge of Pentacam Nucleus Staging and the effect of US elapsed on differences in corneal volume and pachymetry before and after cataract surgery should be of particular value for surgeons who routinely encounter patients with hard cataracts.

Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification

OBJECTIVE

To train and validate a code-free deep learning system (CFDLS) on classifying high-resolution digital retroillumination images of posterior capsule opacification (PCO) and to discriminate between clinically significant and non-significant PCOs.

METHODS AND ANALYSIS

For this retrospective registry study, three expert observers graded two independent datasets of 279 images three separate times with no PCO to severe PCO, providing binary labels for clinical significance. The CFDLS was trained and internally validated using 179 images of a training dataset and externally validated with 100 images. Model development was through Google Cloud AutoML Vision. Intraobserver and interobserver variabilities were assessed using Fleiss kappa (κ) coefficients and model performance through sensitivity, specificity and area under the curve (AUC).

RESULTS

Intraobserver variability κ values for observers 1, 2 and 3 were 0.90 (95% CI 0.86 to 0.95), 0.94 (95% CI 0.90 to 0.97) and 0.88 (95% CI 0.82 to 0.93). Interobserver agreement was high, ranging from 0.85 (95% CI 0.79 to 0.90) between observers 1 and 2 to 0.90 (95% CI 0.85 to 0.94) for observers 1 and 3. On internal validation, the AUC of the CFDLS was 0.99 (95% CI 0.92 to 1.0); sensitivity was 0.89 at a specificity of 1. On external validation, the AUC was 0.97 (95% CI 0.93 to 0.99); sensitivity was 0.84 and specificity was 0.92.

CONCLUSION

This CFDLS provides highly accurate discrimination between clinically significant and non-significant PCO equivalent to human expert graders. The clinical value as a potential decision support tool in different models of care warrants further research.