A comparison of keratoconus progression following collagen cross-linkage using standard or personalised keratometry thresholds.

OBJECTIVE: To define how estimates of keratoconus progression following collagen cross-linking (CXL) vary according to the parameter selected to measure corneal shape. MATERIALS AND METHODS: We estimated progression following CXL in 1677 eyes. We compared standard definitions of keratoconus progression based on published thresholds for Kmax, front K2, or back K2, or progression of any two of these three parameters, with the option of an increased threshold for Kmax values ≥ 55D. As corneal thickness reduces unpredictably after CXL, it was excluded from the principal analysis. We then repeated the analysis using novel adaptive estimates of progression for Kmax, front K2, or back K2, developed separately using 6463 paired readings from keratoconus eyes, with a variation of the Bland-Altman method to determine the 95% regression-based limits of agreement (LoA). We created Kaplan-Meier survival plots for both standard and adaptive thresholds. The primary outcome was progression five years after a baseline visit 9-15 months following CXL. RESULTS: Progression rates were 8% with a standard (≥ 1.5D) threshold for K2 or 6% with the static multi-parameter definition. With a ≥ 1D threshold for Kmax, the progression was significantly higher at 29%. With adaptive Kmax or K2, the progression rates were similar (20%) but less than with the adaptive multi-parameter method (22%). CONCLUSIONS: Estimates of keratoconus progression following CXL vary widely according to the reference criteria. Using adaptive thresholds (LoA) to define the repeatability of keratometry gives estimates for progression that are markedly higher than with the standard multi-parameter method.

Corneal astigmatic outcomes after femtosecond laser-assisted cataract surgery combined with surface penetrating arcuate keratotomies.

AIM: To evaluate corneal astigmatic outcomes of femtosecond laser-assisted arcuate keratotomies (FAKs) combined with femtosecond-laser assisted cataract surgery (FLACS) over 12mo follow-up. METHODS: Totally 145 patients with bilateral cataracts and no ocular co-morbidities were recruited to a single-centre, single-masked, prospective randomized controlled trial (RCT) comparing two monofocal hydrophobic acrylic intraocular lenses. Eyes with corneal astigmatism (CA) of >0.8 dioptres (D) received unpaired, unopened, surface penetrating FAKs at the time of FLACS. Visual acuity, subjective refraction and Scheimpflug tomography were recorded at 1, 6, and 12mo. Alpins vectoral analyses were performed. RESULTS: Fifty-one patients (61 eyes), mean age 68.2±9.6y [standard deviation (SD)], received FAKs. Sixty eyes were available for analysis, except at 12mo when 59 attended. There were no complications due to FAKs. Mean pre-operative CA was 1.13±0.20 D. There was a reduction of astigmatism at all post-operative visits (residual CA 1mo: 0.85±0.42 D, P=0.0001; 6mo: 0.86±0.35 D, P=0001; and 12mo: 0.90±0.39, P=0.0001). Alpins indices remained stable over 12mo. Overall, the cohort was under-corrected at all time points. At 12mo, 61% of eyes were within ±15 degrees of pre-operative astigmatic meridian. CONCLUSION: Unpaired unopened penetrating FAKs combined with on-axis phacoemulsification are safe but minimally effective. CA is largely under-corrected in this cohort using an existing unmodified nomogram. The effect of arcuate keratotomies on CA remained stable over 12mo.

Early detection of visual impairment in young children using a smartphone-based deep learning system.

Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Although certain features of visually impaired children, such as facial appearance and ocular movements, can assist ophthalmic practice, applying these features to real-world screening remains challenging. Here, we present a mobile health (mHealth) system, the smartphone-based Apollo Infant Sight (AIS), which identifies visually impaired children with any of 16 ophthalmic disorders by recording and analyzing their gazing behaviors and facial features under visual stimuli. Videos from 3,652 children (≤48 months in age; 54.5% boys) were prospectively collected to develop and validate this system. For detecting visual impairment, AIS achieved an area under the receiver operating curve (AUC) of 0.940 in an internal validation set and an AUC of 0.843 in an external validation set collected in multiple ophthalmology clinics across China. In a further test of AIS for at-home implementation by untrained parents or caregivers using their smartphones, the system was able to adapt to different testing conditions and achieved an AUC of 0.859. This mHealth system has the potential to be used by healthcare professionals, parents and caregivers for identifying young children with visual impairment across a wide range of ophthalmic disorders.

Intelligent cataract surgery supervision and evaluation via deep learning.

PURPOSE: To assess the performance of a deep learning (DL) algorithm for evaluating and supervising cataract extraction using phacoemulsification with intraocular lens (IOL) implantation based on cataract surgery (CS) videos. MATERIALS AND METHODS: DeepSurgery was trained using 186 standard CS videos to recognize 12 CS steps and was validated in two datasets that contained 50 and 21 CS videos, respectively. A supervision test including 50 CS videos was used to assess the DeepSurgery guidance and alert function. In addition, a real-time test containing 54 CSs was used to compare the DeepSurgery grading performance to an expert panel and residents. RESULTS: DeepSurgery achieved stable performance for all 12 recognition steps, including the duration between two pairs of adjacent steps in internal validation with an ACC of 95.06% and external validations with ACCs of 88.77% and 88.34%. DeepSurgery also recognized the chronology of surgical steps and alerted surgeons to order of incorrect steps. Six main steps are automatically and simultaneously quantified during the evaluation process (centesimal system). In a real-time comparative test, the DeepSurgery step recognition performance was robust (ACC of 90.30%). In addition, DeepSurgery and an expert panel achieved comparable performance when assessing the surgical steps (kappa ranged from 0.58 to 0.77). CONCLUSIONS: DeepSurgery represents a potential approach to provide a real-time supervision and an objective surgical evaluation system for routine CS and to improve surgical outcomes.

Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data.

PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.

Bacterial keratitis: identifying the areas of clinical uncertainty.

Bacterial keratitis is a common corneal infection that is treated with topical antimicrobials. By the time of presentation there may already be severe visual loss from corneal ulceration and opacity, which may persist despite treatment. There are significant differences in the associated risk factors and the bacterial isolates between high income and low- or middle-income countries, so that general management guidelines may not be appropriate. Although the diagnosis of bacterial keratitis may seem intuitive there are multiple uncertainties about the criteria that are used, which impacts the interpretation of investigations and recruitment to clinical studies. Importantly, the concept that bacterial keratitis can only be confirmed by culture ignores the approximately 50% of cases clinically consistent with bacterial keratitis in which investigations are negative. The aetiology of these culture-negative cases is unknown. Currently, the estimation of bacterial susceptibility to antimicrobials is based on data from systemic administration and achievable serum or tissue concentrations, rather than relevant corneal concentrations and biological activity in the cornea. The provision to the clinician of minimum inhibitory concentrations of the antimicrobials for the isolated bacteria would be an important step forward. An increase in the prevalence of antimicrobial resistance is a concern, but the effect this has on disease outcomes is yet unclear. Virulence factors are not routinely assessed although they may affect the pathogenicity of bacteria within species and affect outcomes. New technologies have been developed to detect and kill bacteria, and their application to bacterial keratitis is discussed. In this review we present the multiple areas of clinical uncertainty that hamper research and the clinical management of bacterial keratitis, and we address some of the assumptions and dogma that have become established in the literature.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review.

BACKGROUND: Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements. OBJECTIVE: The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions. METHODS: For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. RESULTS: We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study. CONCLUSIONS: Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Digital Transformation in Ophthalmic Clinical Care During the COVID-19 Pandemic.

ABSTRACT: COVID-19 has placed unprecedented pressure on health systems globally, whereas simultaneously stimulating unprecedented levels of transformation. Here, we review digital adoption that has taken place during the pandemic to drive improvements in ophthalmic clinical care, with a specific focus on out-of-hospital triage and services, clinical assessment, patient management, and use of electronic health records. We show that although there have been some successes, shortcomings in technology infrastructure prepandemic became only more apparent and consequential as COVID-19 progressed. Through our review, we emphasize the need for clinicians to better grasp and harness key technology trends such as telecommunications and artificial intelligence, so that they can effectively and safely shape clinical practice using these tools going forward.

Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology.

ABSTRACT: Aging populations and worsening burden of chronic, treatable disease is increasingly creating a global shortfall in ophthalmic care provision. Remote and automated systems carry the promise to expand the scale and potential of health care interventions, and reduce strain on health care services through safe, personalized, efficient, and cost-effective services. However, significant challenges remain. Forward planning in service design is paramount to safeguard patient safety, trust in digital services, data privacy, medico-legal implications, and digital exclusion. We explore the impact and challenges facing patients and clinicians in integrating AI and telemedicine into ophthalmic care-and how these may influence its direction.

Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study.

BACKGROUND: Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted. METHODS: In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov, NCT04213430, and is currently closed. FINDINGS: The area under the receiver operating characteristic curve (AUC) in the internal validation set was 0·955 (SD 0·046). AUC values in the external test set were 0·965 (0·035) in tertiary hospitals, 0·983 (0·031) in community hospitals, and 0·953 (0·042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0·960, 95% CI 0·957-0·964 in referable diabetic retinopathy). INTERPRETATION: Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care. FUNDING: This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Safety of video-based telemedicine compared to in-person triage in emergency ophthalmology during COVID-19.

BACKGROUND: the need for social distancing midst the COVID-19 pandemic has forced ophthalmologists to innovate with telemedicine. The novel process of triaging emergency ophthalmology patients via videoconsultations should reduce hospital attendances. However, the safety profile of such services were unknown. METHODS: in this retrospective cohort study, we reviewed case notes of 404 adults who used our videoconsultation service from 20/04/2020 to 03/05/2020. We compared these to 451 patient who attended eye casualty in person at the same time who were deemed not to require same day ophthalmic examination. FINDINGS: patients seen by videoconsultations tended to be younger (Median = 43 years, Inter-quartile range = 27 vs Median= 49 years, Inter-quartile range = 28)'. More males used the face-to-face triage (55%) while more females used videoconsultation (54%)%. Fewer patients seen by videoconsultations required specialist review compared to face-face triage [X 2 (1, N = 854) = 128.02, p<0.001)]. 35.5% of the patients initially seen by videoconsultation had unplanned reattendance within 1 month, compared to 15.7% in the group initially seen in person. X 2 (1, N = 234) = 7.31, p = 0.007). The rate of actual harm was no different (at 0% for each method), with perfect inter-grader correlation when graded independently by two senior ophthalmologists. 97% of patients seen on the video platform surveyed were satisfied with their care. INTERPRETATION: we demonstrate comparable patient safety of videoconsultations at one-month follow-up to in person review. The service is acceptable to patients and reduces the risk of COVID-19 transmission. We propose that videoconsultations are effective and desirable as a tool for triage in ophthalmology. FUNDING: the research supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology who fund PT and DS's time to conduct research. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Characteristics of endothelial corneal transplant rejection following immunisation with SARS-CoV-2 messenger RNA vaccine.

AIM: We report two cases of endothelial corneal allograft rejection following immunisation with SARS-CoV-2 messenger RNA (mRNA) vaccine BNT162b2 and describe the implications for management of transplant recipients postvaccination for COVID-19. METHODS: A 66-year-old woman with Fuchs endothelial corneal dystrophy (FECD) and a unilateral Descemet's membrane endothelial keratoplasty (DMEK) transplant received COVID-19 mRNA vaccine BNT162b2 14 days post-transplant. Seven days later, she presented with symptoms and signs of endothelial graft rejection. An 83-year-old woman with bilateral DMEK transplants for FECD 3 and 6 years earlier developed simultaneous acute endothelial rejection in both eyes, 3 weeks post second dose of COVID-19 mRNA vaccine BNT162b2. Rejection in both cases was treated successfully with topical corticosteroids. CONCLUSIONS: We believe this is the first report of temporal association between corneal transplant rejection following immunisation against COVID-19 and the first report of DMEK rejection following any immunisation. We hypothesise that the allogeneic response may have been initiated by the host antibody response following vaccination. Clinicians and patients should be aware of the potential of corneal graft rejection associated with vaccine administration and may wish to consider vaccination in advance of planned non-urgent keratoplasties. Patients should be counselled on the symptoms and signs that require urgent review to allow early treatment of any confirmed rejection episode.

Associations Between Regional Environment and Cornea-Related Morphology of the Eye in Young Adults: A Large-Scale Multicenter Cross-Sectional Study.

Purpose: To investigate environmental factors associated with corneal morphologic changes. Methods: A cross-sectional study was conducted, which enrolled adults of the Han ethnicity aged 18 to 44 years from 20 cities. The cornea-related morphology was measured using an ocular anterior segment analysis system. The geographic indexes of each city and meteorological indexes of daily city-level data from the past 40 years (1980-2019) were obtained. Correlation analyses at the city level and multilevel model analyses at the eye level were performed. Results: In total, 114,067 eyes were used for analysis. In the correlation analyses at the city level, the corneal thickness was positively correlated with the mean values of precipitation (highest r [correlation coefficient]: >0.700), temperature, and relative humidity (RH), as well as the amount of annual variation in precipitation (r: 0.548 to 0.721), and negatively correlated with the mean daily difference in the temperature (DIF T), duration of sunshine, and variance in RH (r: -0.694 to 0.495). In contrast, the anterior chamber (AC) volume was negatively correlated with the mean values of precipitation, temperature, RH, and the amount of annual variation in precipitation (r: -0.672 to -0.448), and positively associated with the mean DIF T (r = 0.570) and variance in temperature (r = 0.507). In total 19,988 eyes were analyzed at the eye level. After adjusting for age, precipitation was the major explanatory factor among the environmental factors for the variability in corneal thickness and AC volume. Conclusions: Individuals who were raised in warm and wet environments had thicker corneas and smaller AC volumes than those from cold and dry ambient environments. Our findings demonstrate the role of local environmental factors in corneal-related morphology.

Visual Acuity Apps for Rapid Integration in Teleconsultation Services in all Resource Settings: A Review.

PURPOSE: There is a need for a simple and accurate way to assess visual acuity in telemedicine consultations in ophthalmology and other related specialties. DESIGN: We surveyed visual acuity testing apps available that allow patients to measure their own acuity, focusing on freely accessible resources suitable for all resource settings. METHODS: A systematic search was performed for visual acuity testing apps on 2 major platforms: Google Play Store (Google, CA, USA) and Apple App Store (Apple, CA, USA). RESULTS: Sixteen apps (67%) tested near vision, 5 apps (21%) tested distance vision, and 3 apps (13%) offered options for both near and distance vision testing. Of the 24 apps, 5 (21%) offered a method of calibration of optotype size. Three apps (13%) demonstrated evidence of clinical validation. Only 3 apps fulfilled our criteria for suitability for clinical practice. CONCLUSIONS: We have recommended 3 apps that may be quickly integrated into clinical practice in both ophthalmic and non-ophthalmic all resource settings.

Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study.

BACKGROUND: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed to engineer deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images. METHODS: We did a multicentre, prospective study to develop models using slit-lamp or retinal fundus images from participants in three hepatobiliary departments and two medical examination centres. Included participants were older than 18 years and had complete clinical information; participants diagnosed with acute hepatobiliary diseases were excluded. We trained seven slit-lamp models and seven fundus models (with or without hepatobiliary disease [screening model] or one specific disease type within six categories [identifying model]) using a development dataset, and we tested the models with an external test dataset. Additionally, we did a visual explanation and occlusion test. Model performances were evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and F1* score. FINDINGS: Between Dec 16, 2018, and July 31, 2019, we collected data from 1252 participants (from the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University, the Department of Infectious Diseases of the Affiliated Huadu Hospital of Southern Medical University, and the Nantian Medical Centre of Aikang Health Care [Guangzhou, China]) for the development dataset; between Aug 14, 2019, and Jan 31, 2020, we collected data from 537 participants (from the Department of Infectious Diseases of the Third Affiliated Hospital of Sun Yat-sen University and the Huanshidong Medical Centre of Aikang Health Care [Guangzhou, China]) for the test dataset. The AUROC for screening for hepatobiliary diseases of the slit-lamp model was 0·74 (95% CI 0·71-0·76), whereas that of the fundus model was 0·68 (0·65-0·71). For the identification of hepatobiliary diseases, the AUROCs were 0·93 (0·91-0·94; slit-lamp) and 0·84 (0·81-0·86; fundus) for liver cancer, 0·90 (0·88-0·91; slit-lamp) and 0·83 (0·81-0·86; fundus) for liver cirrhosis, and ranged 0·58-0·69 (0·55-0·71; slit-lamp) and 0·62-0·70 (0·58-0·73; fundus) for other hepatobiliary diseases, including chronic viral hepatitis, non-alcoholic fatty liver disease, cholelithiasis, and hepatic cyst. In addition to the conjunctiva and sclera, our deep learning model revealed that the structures of the iris and fundus also contributed to the classification. INTERPRETATION: Our study established qualitative associations between ocular features and major hepatobiliary diseases, providing a non-invasive, convenient, and complementary method for hepatobiliary disease screening and identification, which could be applied as an opportunistic screening tool. FUNDING: Science and Technology Planning Projects of Guangdong Province; National Key R&D Program of China; Guangzhou Key Laboratory Project; National Natural Science Foundation of China.

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

A randomised controlled trial comparing femtosecond laser-assisted cataract surgery versus conventional phacoemulsification surgery: 12-month results.

AIMS: To report 12-month outcomes of randomised controlled trial comparing conventional phacoemulsification surgery (CPS) with femtosecond laser-assisted cataract surgery (FLACS). METHODS: This was a single-centre, prospective single-masked randomised case-controlled trial. Four hundred patients were randomised to CPS or FLACS with the LenSx platform (Alcon Laboratories Inc.). Visual acuity, refraction, central corneal thickness, endothelial cell loss (ECL), adverse events and quality of life outcomes, using EuroQOL 5-dimensions (EQ-5D-3 L) and cataract surgery patient-reported outcome measures (PROMs) questionnaires (Cat-PROM5), were recorded. RESULTS: Two hundred and thirty four patients (58.5%) attended 12-month follow-up (116 FLACS, 118 CPS). Mean LogMAR unaided distance visual acuity) (±SD) was 0.12 (0.18) with FLACS and 0.13 (0.19) with CPS (p=0.68; 95% Confidence Interval [CI]-0.06,0.04). Mean spherical equivalent (SE) refraction was -0.1±0.6 diopters (D) with FLACS and -0.2±0.6 D with CPS (p=0.44; 95% CI -0.09, 0.21). Mean corrected distance visual acuity (±SD) was -0.01 (0.1) with FLACS and 0(0.1) with CPS (p=0.45; 95% CI -0.04,0.02). Two patients per group underwent YAG laser capsulotomy for posterior capsular opacification (p=1). Mean ECL (per mm2±SD) was 301±320 with FLACS and 228±303 with CPS (p=0.07; 95% CI -7.26, 153.26). Mean Cat-PROM scores (±SD) were -5.5 (2.6) with FLACS and -5.8 (2.5) with CPS (p=0.3; 95% CI 0.31,1.01). EQ5-3DL mean index score (±SD) was 0.92 (0.13) with FLACS and 0.89 (0.14) with CPS (p=0.1; 95% CI -0.1, 0.01). Vector analysis comparing manual limbal relaxing incisions (LRIs) and intrastromal femtosecond laser-assisted astigmatic keratotomies (iFAKs) showed a greater correction index (p=0.02; 95% CI 0.06 to 0.60) and smaller difference vector (p=0.046; 95% CI -0.54, -0.01) with iFAK. CONCLUSIONS: There were no differences in vision, refraction, adverse postoperative events or PROMs between FLACS and CPS groups at 12 months. iFAKs may provide more effective astigmatic correction compared to LRIs, 12 months postoperatively.