[Challenges and prospects in the application of artificial intelligence for ocular disease screening and diagnosis].

In recent years, artificial intelligence (AI) technologies have experienced substantial growth across various sectors, with significant strides made particularly in medical AI through advancements such as large models. The application of AI within the field of ophthalmology can enhance the accuracy of eye disease screening and diagnosis. However, the deployment of AI and its large models in ophthalmology still encounters numerous limitations and challenges. This article builds upon the transformative achievements in the medical AI sector and discusses the ongoing challenges faced by AI applications in ophthalmology. It provides forward-looking insights from an ophthalmic perspective regarding the era of large models and anticipates research trends in AI applications in ophthalmology, so as to foster the continuous advancement of AI technologies, thereby significantly promoting eye health.

Enhancing Meibography Image Analysis Through Artificial Intelligence-Driven Quantification and Standardization for Dry Eye Research.

Purpose: This study enhances Meibomian gland (MG) infrared image analysis in dry eye (DE) research through artificial intelligence (AI). It is comprised of two main stages: automated eyelid detection and tarsal plate segmentation to standardize meibography image analysis. The goal is to address limitations of existing assessment methods, bridge the curated and real-world dataset gap, and standardize MG image analysis. Methods: The approach involves a two-stage process: automated eyelid detection and tarsal plate segmentation. In the first stage, an AI model trained on curated data identifies relevant eyelid areas in non-curated datasets. The second stage refines the eyelid area in meibography images, enabling precise comparisons between normal and DE subjects. This approach also includes specular reflection removal and tarsal plate mask refinement. Results: The methodology achieved a promising instance-wise accuracy of 80.8% for distinguishing meibography images from 399 DE and 235 non-DE subjects. By integrating diverse datasets and refining the area of interest, this approach enhances meibography feature extraction accuracy. Dimension reduction through Uniform Manifold Approximation and Projection (UMAP) allows feature visualization, revealing distinct clusters for DE and non-DE phenotypes. Conclusions: The AI-driven methodology presented here quantifies and classifies meibography image features and standardizes the analysis process. By bootstrapping the model from curated datasets, this methodology addresses real-world dataset challenges to enhance the accuracy of meibography image feature extraction. Translational Relevance: The study presents a standardized method for meibography image analysis. This method could serve as a valuable tool in facilitating more targeted investigations into MG characteristics.

Pilot Study Assessing the Effect of Exam Room Length on the Measurement of Strabismus.

PURPOSE: Our study aims to investigate the effect of decreasing distance from the patient to the fixation target on the measurement of strabismus with a known distance-near disparity. METHODS: Strabismus measurements were taken by one pediatric ophthalmologist at our standard distance of 18 feet and compared to those taken at 16, 14, 12, and 10 feet from the fixation target. A clinically meaningful difference was defined as >2.5 prism diopters (PD), since a difference of that magnitude may alter surgical planning. RESULTS: Thirty-nine subjects, including 22 exotropes and 17 esotropes, were included in this study. Mean prism diopter difference (PDD) in the exotrope group at lengths of 16, 14, 12, and 10 feet compared to 18 feet were 1.3 (SD 1.9, range 0-6), 1.3 (SD 2.2, range 0-8), 1.7 (SD 3.2, range 0-14), and 2.8 (SD 4.4, range 0-14), respectively. Among esotropes, the mean PDD at the same distances were 1.1 (SD 1.9, range 0-7), 2.1 (SD 2.6, range 0-7), 3.9 (SD 4.9, range 0-19), and 4.3 (SD 5.1, range 0-19). The percentages of exotropes with a PDD of >2.5 at 16, 14, 12, and 10 feet compared to 18 feet were 13.6% (n = 3), 13.6% (n = 3), 18.2% (n = 4), and 27.3% (n = 6), respectively. In the esotrope group, 11.8% (n = 2), 35.3% (n = 6), 47.1% (n = 8), and 47.1% (n = 8) had a PDD of >2.5 at the same distances, respectively. CONCLUSION: This pilot study is the first to investigate the change in measured angle of strabismus at various non-mirrored distances from the patient to the fixation target. Our methodology defines a framework that could be used in a higher-powered study to further our understanding of the effect of room length on strabismus evaluation.

Diagnostic Protocol for Accommodative and Vergence Anomalies - A Review.

PURPOSE: To review the diagnostic protocols of non-strabismic binocular vision anomalies. METHODS: We carried out a literature search on published articles of non-strabismic accommodative and vergence anomalies in different international optometry and ophthalmology journals found in the Pubmed, ResearchGate, Google Scholar, and MEDLINE databases. RESULTS: The diagnostic criteria and normative data from the nine articles selected show discrepancies and variability in methodologies and techniques in the overall assessment of Non-Strabismic Binocular Vision Anomalies (NSBVA). Near point of convergence measurement is the most common assessment, whereas the vergence facility is the least commonly used assessment in terms of evaluating convergence insufficiency. Near point of convergence > 10 cm alone is the most sensitive sign to detect convergence insufficiency in a community set-up but high positive relative accommodation (>3.50D) is the most sensitive sign to diagnose accommodative excess. On the other hand, monocular accommodative facility < 7 CPM has the highest sensitivity to confirm the diagnosis of accommodative infacility. This review also indicates that the more clinical signs that are included in a set of diagnostic criteria, the lower the prevalence rate for that diagnosis. CONCLUSIONS: There is no standardized and diagnostically validated protocol for the assessment of NSBVAs. Variable cutoff values obtained using different methods and the selection of diagnostic criteria by various researchers have led to discrepancies that highlight the need for diagnostic validity of available protocols (combination of tests) for each anomaly. Clinical signs such as positive relative accommodation (PRA) for accommodative excess, near point of convergence (NPC) for convergence insufficiency and monocular accommodative facility (MAF) for accommodative infacility were found to be useful diagnostic signs of these anomalies. Studies should be carried out for accommodative and vergence dysfunctions using proper designs and methods to validate diagnostic criteria for all age groups. Standardization of assessment protocol and cutoff criteria will also aid in calculating prevalence for non-strabismic binocular vision anomalies.

Repeatability of Noninvasive Break-Up Time Measurements using Keratograph Oculus 3.

AIM: The primary aim of this study is to evaluate the repeatability of noninvasive break-up time (NIBUT) measurement by keratograph when it is determined from one, two or three partial measurements, and to recommend a suitable methodology for practice. Another goal is to verify that repeated measurements do not affect the measured value. MATERIAL AND METHODS: Thirty-eight healthy volunteers (30 women and 8 men) aged between 19 and 50 years old were included in the study, in which only one eye of each volunteer was measured. The study was designed as a prospective one. Each subject adapted to the local conditions of the laboratory for 15 minutes and subsequently underwent two series of NIBUT measurements (test, retest) on an OCULUS 3 Keratograph. The minimum time interval between the two series was 10 minutes, in which each series contained three partial measurements approximately 3 three measurements in the given series. Repeatability was assessed by a Bland-Altman analysis and expressed as a repeatability coefficient. In every case, only the time of the first break-up of the tear film was monitored. RESULTS: The statistical analysis did not show statistically significant differences both between partial measurements of NIBUT in the individual series (p = 0.92, p = 0.81) and when comparing all six measurements (p = 0.95). The mean values of the partial measurements ranged from 13.6 s to 14.4 s. The repeatability coefficients were found to be 15.0 s, 12.1 s and 10.0 s for methodologies A, B and C, respectively. A supplementary analysis for 12 eyes with low NIBUT (< 10 s) showed statistically significantly better repeatability in this group, with coefficients of 7.0 s (methodology A), 6.0 s (B) and 4.6 s (C). CONCLUSION: Determination of NIBUT from three consecutive measurements (with a sufficient interval of ideally a few minutes) significantly improves repeatability. Such repeated NIBUT measurements do not have a significant effect on the measured value. The mentioned methodology for measuring NIBUT on a keratograph can be recommended for use in practice.

Raman spectroscopy assisted tear analysis: A label free, optical approach for noninvasive disease diagnostics.

In recent times, tear fluid analysis has garnered considerable attention in the field of biomarker-based diagnostics due to its noninvasive sample collection method. Tears encompass a reservoir of biomarkers that assist in diagnosing not only ocular disorders but also a diverse list of systemic diseases. This highlights the necessity for sensitive and dependable screening methods to employ tear fluid as a potential noninvasive diagnostic specimen in clinical environments. Considerable research has been conducted to investigate the potential of Raman spectroscopy-based investigations for tear analysis in various diagnostic applications. Raman Spectroscopy (RS) is a highly sensitive and label free spectroscopic technique which aids in investigating the molecular structure of samples by evaluating the vibrational frequencies of molecular bonds. Due to the distinct chemical compositions of different samples, it is possible to obtain a sample-specific spectral fingerprint. The distinctive spectral fingerprints obtained from Raman spectroscopy enable researchers to identify specific compounds or functional groups present in a sample, aiding in diverse biomedical applications. Its sensitivity to changes in molecular structure or environment provides invaluable insights into subtle alterations associated with various diseases. Thus, Raman Spectroscopy has the potential to assist in diagnosis and treatment as well as prognostic evaluation. Raman spectroscopy possesses several advantages, such as the non-destructive examination of samples, remarkable sensitivity to structural variations, minimal prerequisites for sample preparation, negligible interference from water, and the aptness for real-time investigation of tear samples. The purpose of this review is to highlight the potential of Raman spectroscopic technique in facilitating the clinical diagnosis of various ophthalmic and systemic disorders through non-invasive tear analysis. Additionally, the review delves into the advancements made in Raman spectroscopy with regards to paper-based sensing substrates and tear analysis methods integrated into contact lenses. Furthermore, the review also addresses the obstacles and future possibilities associated with implementing Raman spectroscopy as a routine diagnostic tool based on tear analysis in clinical settings.

Mechanisms for Positive Bielschowsky Head Tilt Testing in Horizontal Strabismus.

PURPOSE: To elucidate the induced effects of horizontal strabismus on the Bielschowsky Head Tilt Test (BHTT). DESIGN: Prospective clinical study. METHODS: Prospective analysis of BHTT testing in 85 patients with exotropia and 71 patients with esotropia who were examined in a strabismus clinic. RESULTS: Eighty-four of 85 patients with exotropia (98.82%) showed a positive BHTT with an induced hyperdeviation on the side of the tilt (to both sides in 67% and to one side in 32%). Fifty-seven of 71 patients with esotropia (80.2%) showed a positive BHTT with an induced hypodeviation on the side of the tilt (to both sides in 57.7% and to one side in 22.5%). These induced vertical deviations were greater in patients with larger horizontal deviations and in those with constant rather than intermittent deviations; however, they were not influenced by the presence or absence of associated primary oblique muscle overaction. CONCLUSIONS: Exotropia and esotropia produce hyperdeviations during BHTT testing, with a hyperdeviation on the side of the tilt observed in patients with exotropia, and hypotropia on the side of the tilt observed in patients with esotropia. These diametrical results are not attributable to any preexistent alteration of neurologic output inherent to these two forms of horizontal strabismus or to associated torsion. Rather, they arise directly from the altered anatomical positions of the two eyes, which cause the eyes to approximate their visual axes more closely to the vertical rectus muscles (in exotropia) and the oblique muscles (in esotropia), enabling the vertical actions of specific cyclovertical muscles to predominate in response to altered utricular output generated by the BHTT.

New disposable esthesiometer (KeraSenseⓇ) to improve diagnosis and management of neurotrophic keratitis.

PURPOSE: To validate the use, repeatability, and reproducibility of a new, cost-effective, disposable, sterile device (KeraSenseⓇ, Dompè farmaceutici SpA, Milan Italy) compared to Cochet-Bonnet (CB) esthesiometer. Secondly, to identify a simple, safe, rapid, and low-cost test to diagnose neurotrophic keratitis (NK). METHODS: 16 patients with diagnosis of NK stage I, 25 patients with diabetes mellitus (DM), and 26 healthy subjects were included in the study. Corneal sensitivity (CS) was assessed by CB and KeraSenseⓇ. Repeatability, accuracy, and reproducibility of the novel disposable aesthesiometer were assessed. Specificity, sensitivity, and cut-off value for NK diagnosis were calculated by ROC curve analysis. RESULTS: All NK patients showed a CS ≤ 40 mm, while none of the healthy patients showed a CS value < 50 mm. Significant agreement was found between CB measurements and the single use esthesiometer evaluations of CS (p < 0.001). Repeatability evaluations of the single use esthesiometer showed 100% agreement between different measurements (p < 0.001). Reproducibility evaluations showed 99.6% concordance between different operators (p < 0.001). A 55 mm value of the single use esthesiometer was adequate to exclude an NK diagnosis, while all NK patients showed a value ≤ 35 mm. CONCLUSIONS: Corneal hypo/anaesthesia is considered the hallmark of NK. The use of the novel single-use esthesiometer will allow for a diagnostic improvement in NK, sparing time and guaranteeing patients' safety. Diabetic patients despite normal corneal findings may show impairment of CS, suggesting a preclinical stage of NK, requiring a close follow-up.

Pupil size measurements with a multifunctional aberrometer/coherence interferometer/tomographer and two infrared-based pupillometers.

PURPOSE: To compare precision of pupil size measurements of a multifunctional device (Pentacam AXL Wave [Pentacam]) and 2 infrared-based pupillometers (PupilX, Colvard) and to compare repeatability of Pentacam and PupilX. SETTING: Department of Ophthalmology, Goethe-University, Frankfurt am Main, Germany. DESIGN: Prospective, comparative trial. METHODS: Pupil diameter of healthy eyes was measured with Colvard once and Pentacam without glare (WO) and with glare (WG), PupilX in 0, 1, and 16 lux 3 times each. In a second series, measurements with Pentacam WO and PupilX in 0.06 and 0.12 lux were assessed. RESULTS: 36 eyes of participants aged 21 to 63 years were included. Mean pupil diameter was 6.05 mm with Colvard, 5.79 mm (first series), 5.50 mm (second series) with Pentacam WO, 3.42 mm WG, 7.26 mm PupilX in 0, 4.67 mm 1, 3.66 mm 16, 6.82 mm in 0.06, and 6.39 mm in 0.12 lux. Measurements with Pentacam WO were significantly different to PupilX in 0, 0.06, 0.12, and 1 lux (all P < .001), but not to Colvard ( P = .086). Pupil size measured with Pentacam WG and PupilX in 16 lux was not significantly different ( P = .647). Consecutive measurements with Pentacam WO and WG had mean SD of 0.23 mm and 0.20 mm, respectively, and with PupilX 0.11 in 0, 0.24 mm 1, and 0.20 mm in 16 lux. CONCLUSIONS: Pentacam provided good assessment of pupil size but was not equivalent to PupilX in low lighting conditions. Repeatability was more favorable for Pentacam.

SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.

Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised image Transformer (SSiT) is proposed for Diabetic Retinopathy (DR) grading from fundus images. We novelly introduce saliency maps into SSL, with a goal of guiding self-supervised pre-training with domain-specific prior knowledge. Specifically, two saliency-guided learning tasks are employed in SSiT: 1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder. Thus, the key encoder is constrained to provide target representations focusing on salient regions, guiding the query encoder to capture salient features. 2) The query encoder is trained to predict the saliency segmentation, encouraging the preservation of fine-grained information in the learned representations. To assess our proposed method, four publicly-accessible fundus image datasets are adopted. One dataset is employed for pre-training, while the three others are used to evaluate the pre-trained models' performance on downstream DR grading. The proposed SSiT significantly outperforms other representative state-of-the-art SSL methods on all downstream datasets and under various evaluation settings. For example, SSiT achieves a Kappa score of 81.88% on the DDR dataset under fine-tuning evaluation, outperforming all other ViT-based SSL methods by at least 9.48%.

Insights into the diagnosis and management of sarcoid uveitis: A review.

Sarcoidosis is a leading cause of non-infectious uveitis that commonly affects middle-aged individuals and has a female preponderance. The disease demonstrates age, sex and ethnic differences in clinical manifestations. A diagnosis of sarcoidosis is made based on a compatible clinical presentation, supporting investigations and histologic evidence of non-caseating granulomas, although biopsy is not always possible. Multimodal imaging with widefield fundus photography, optical coherence tomography and angiography can help in the diagnosis of sarcoid uveitis and in the monitoring of treatment response. Corticosteroid remains the mainstay of treatment; chronic inflammation requires steroid-sparing immunosuppression. Features on multimodal imaging such as vascular leakage may provide prognostic indicators of outcome. Female gender, prolonged and severe uveitis, and posterior involving uveitis are associated with poorer visual outcomes.

High Dynamic Range Image Processing for Retinal Color Fundus Photography.

BACKGROUND AND OBJECTIVE: Color fundus photography is an important imaging modality that is currently limited by a narrow dynamic range. We describe a post-image processing technique to generate high dynamic range (HDR) retinal images with enhanced detail. PATIENTS AND METHODS: This was a retrospective, observational case series evaluating fundus photographs of patients with macular pathology. Photographs were acquired with three or more exposure values using a commercially available camera (Topcon 50-DX). Images were aligned and imported into HDR processing software (Photomatix Pro). Fundus detail was compared between HDR and raw photographs. RESULTS: Sixteen eyes from 10 patients (5 male, 5 female; mean age 59.4 years) were analyzed. Clinician graders preferred the HDR image 91.7% of the time (44/48 image comparisons), with good grader agreement (81.3%, 13/16 eyes). CONCLUSIONS: HDR fundus imaging is feasible using images from existing fundus cameras and may be useful for enhanced visualization of retinal detail in a variety of pathologic states. [Ophthalmic Surg Lasers Imaging Retina 2024;55:263-269.].

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Comparison of quantification of intraretinal hard exudates between optical coherence tomography en face image versus fundus photography.

PURPOSE: To compare the quantification of intraretinal hard exudate (HE) using en face optical coherence tomography (OCT) and fundus photography. METHODS: Consecutive en face images and corresponding fundus photographs from 13 eyes of 10 patients with macular edema associated with diabetic retinopathy or Coats' disease were analyzed using the machine-learning-based image analysis tool, "ilastik." RESULTS: The overall measured HE area was greater with en face images than with fundus photos (en face: 0.49 ± 0.35 mm2 vs. fundus photo: 0.34 ± 0.34 mm2, P < 0.001). However, there was an excellent correlation between the two measurements (intraclass correlation coefficient [ICC] = 0.844). There was a negative correlation between HE area and central macular thickness (CMT) (r = -0.292, P = 0.001). However, HE area showed a positive correlation with CMT in the previous several months, especially in eyes treated with anti-vascular endothelial growth factor (VEGF) therapy (CMT 3 months before: r = 0.349, P = 0.001; CMT 4 months before: r = 0.287, P = 0.012). CONCLUSION: Intraretinal HE can be reliably quantified from either en face OCT images or fundus photography with the aid of an interactive machine learning-based image analysis tool. HE area changes lagged several months behind CMT changes, especially in eyes treated with anti-VEGF injections.

Diagnostic capabilities of ChatGPT in ophthalmology.

PURPOSE: The purpose of this study is to assess the diagnostic accuracy of ChatGPT in the field of ophthalmology. METHODS: This is a retrospective cohort study conducted in one academic tertiary medical center. We reviewed data of patients admitted to the ophthalmology department from 06/2022 to 01/2023. We then created two clinical cases for each patient. The first case is according to the medical history alone (Hx). The second case includes an addition of the clinical examination (Hx and Ex). For each case, we asked for the three most likely diagnoses from ChatGPT, residents, and attendings. Then, we compared the accuracy rates (at least one correct diagnosis) of all groups. Additionally, we evaluated the total duration for completing the assignment between the groups. RESULTS: ChatGPT, residents, and attendings evaluated 126 cases from 63 patients (history only or history and exam findings for each patient). ChatGPT achieved a significantly lower accurate diagnosis rate (54%) in the Hx, as compared to the residents (75%; p < 0.01) and attendings (71%; p < 0.01). After adding the clinical examination findings, the diagnosis rate of ChatGPT was 68%, whereas for the residents and the attendings, it increased to 94% (p < 0.01) and 86% (p < 0.01), respectively. ChatGPT was 4 to 5 times faster than the attendings and residents. CONCLUSIONS AND RELEVANCE: ChatGPT showed low diagnostic rates in ophthalmology cases compared to residents and attendings based on patient history alone or with additional clinical examination findings. However, ChatGPT completed the task faster than the physicians.

Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.

BACKGROUND: The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become increasingly important. Despite the high prevalence of ERM, few deep learning studies have investigated ERM detection in the color fundus photography (CFP) domain. In this study, we built a generative model to enhance ERM detection performance in the CFP. METHODS: This deep learning study retrospectively collected 302 ERM and 1,250 healthy CFP data points from a healthcare center. The generative model using StyleGAN2 was trained using single-center data. EfficientNetB0 with StyleGAN2-based augmentation was validated using independent internal single-center data and external datasets. We randomly assigned healthcare center data to the development (80%) and internal validation (20%) datasets. Data from two publicly accessible sources were used as external validation datasets. RESULTS: StyleGAN2 facilitated realistic CFP synthesis with the characteristic cellophane reflex features of the ERM. The proposed method with StyleGAN2-based augmentation outperformed the typical transfer learning without a generative adversarial network. The proposed model achieved an area under the receiver operating characteristic (AUC) curve of 0.926 for internal validation. AUCs of 0.951 and 0.914 were obtained for the two external validation datasets. Compared with the deep learning model without augmentation, StyleGAN2-based augmentation improved the detection performance and contributed to the focus on the location of the ERM. CONCLUSIONS: We proposed an ERM detection model by synthesizing realistic CFP images with the pathological features of ERM through generative deep learning. We believe that our deep learning framework will help achieve a more accurate detection of ERM in a limited data setting.

Correction Method for Optical Scaling of Fundoscopy Images: Development, Validation, and First Implementation.

Purpose: Although fundus photography is extensively used in ophthalmology, refraction prevents accurate distance measurement on fundus images, as the resulting scaling differs between subjects due to varying ocular anatomy. We propose a PARaxial Optical fundus Scaling (PAROS) method to correct for this variation using commonly available clinical data. Methods: The complete optics of the eye and fundus camera were modeled using ray transfer matrix formalism to obtain fundus image magnification. The subject's ocular geometry was personalized using biometry, spherical equivalent of refraction (RSE), keratometry, and/or corneal topography data. The PAROS method was validated using 41 different eye phantoms and subsequently evaluated in 44 healthy phakic subjects (of whom 11 had phakic intraocular lenses [pIOLs]), 29 pseudophakic subjects, and 21 patients with uveal melanoma. Results: Validation of the PAROS method showed small differences between model and actual image magnification (maximum 3.3%). Relative to the average eye, large differences in fundus magnification were observed, ranging from 0.79 to 1.48. Magnification was strongly inversely related to RSE (R2 = 0.67). In phakic subjects, magnification was directly proportional to axial length (R2 = 0.34). The inverse relation was seen in pIOL (R2 = 0.79) and pseudophakic (R2 = 0.12) subjects. RSE was a strong contributor to magnification differences (1%-83%). As this effect is not considered in the commonly used Bennett-Littmann method, statistically significant differences up to 40% (mean absolute 9%) were observed compared to the PAROS method (P < 0.001). Conclusions: The significant differences in fundus image scaling observed among subjects can be accurately accounted for with the PAROS method, enabling more accurate quantitative assessment of fundus photography.