Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CDFL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.

Recovery course of persistent posterior subretinal fluid after successful repair of rhegmatogenous retinal detachment.

PURPOSE: To investigate best corrected visual acuity (BCVA), subretinal fluid (SRF) absorption time or ellipsoid zone (EZ) restoration time and various variables in patients with persistent SRF after successful primary repair of rhegmatogenous retinal detachment (RRD). METHODS: This retrospective multicenter study allowed independent analysis of the healing pattern by two observers based on composite of serial cross-sectional macular optical coherence tomography (OCT) scans. Univariate and multivariate analyses were implemented. RESULTS: One hundred and three cases had persistent SRF after pars plana vitrectomy, scleral buckling, or pneumatic retinopexy. By univariate analysis, SRF resolution time correlated positively with the number of retinal breaks (p < 0.001) and with increased myopia (p = 0.011). Using multivariate analysis, final BCVA (log MAR) correlated positively with age, duration of RRD, initial BCVA (OR = 3.28; [95%CI = 1.44-7.47]; p = 0.015), and SRF resolution time (OR = 0.46 [95%CI 0.21-1.05]; p = 0.049). EZ restoration time was longer with increasing number of retinal tears (OR = 0.67; [95%CI 0.29-1.52]; p = 0.030), worse final BCVA, and presence of macula-off RRD (OR = 0.26; [95%CI 0.08-0.88]; p = 0.056). SRF resolution time correlated marginally with prone position. CONCLUSIONS: Residual posterior SRF is more common in eyes with multiple breaks or in myopic eyes. Final BCVA is better in younger subjects and in eyes with shorter duration of RRD. Persistent SRF is a self-limited disorder with a mean resolution of 11.2 months with good visual prognosis improving from a mean baseline logMAR of 1.08 to 0.25 at one year.

Artificial intelligence for telemedicine diabetic retinopathy screening: a review.

PURPOSE: This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation. METHODS: The review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups. RESULTS: The literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation. CONCLUSIONS: Telemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.

Fairness and generalizability of OCT normative databases: a comparative analysis.

PURPOSE: In supervised Machine Learning algorithms, labels and reports are important in model development. To provide a normality assessment, the OCT has an in-built normative database that provides a color base scale from the measurement database comparison. This article aims to evaluate and compare normative databases of different OCT machines, analyzing patient demographic, contrast inclusion and exclusion criteria, diversity index, and statistical approach to assess their fairness and generalizability. METHODS: Data were retrieved from Cirrus, Avanti, Spectralis, and Triton's FDA-approval and equipment manual. The following variables were compared: number of eyes and patients, inclusion and exclusion criteria, statistical approach, sex, race and ethnicity, age, participant country, and diversity index. RESULTS: Avanti OCT has the largest normative database (640 eyes). In every database, the inclusion and exclusion criteria were similar, including adult patients and excluding pathological eyes. Spectralis has the largest White (79.7%) proportionately representation, Cirrus has the largest Asian (24%), and Triton has the largest Black (22%) patient representation. In all databases, the statistical analysis applied was Regression models. The sex diversity index is similar in all datasets, and comparable to the ten most populous contries. Avanti dataset has the highest diversity index in terms of race, followed by Cirrus, Triton, and Spectralis. CONCLUSION: In all analyzed databases, the data framework is static, with limited upgrade options and lacking normative databases for new modules. As a result, caution in OCT normality interpretation is warranted. To address these limitations, there is a need for more diverse, representative, and open-access datasets that take into account patient demographics, especially considering the development of supervised Machine Learning algorithms in healthcare.

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.

Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening.

AIMS: This study aims to compare the performance of a handheld fundus camera (Eyer) and standard tabletop fundus cameras (Visucam 500, Visucam 540, and Canon CR-2) for diabetic retinopathy and diabetic macular edema screening. METHODS: This was a multicenter, cross-sectional study that included images from 327 individuals with diabetes. The participants underwent pharmacological mydriasis and fundus photography in two fields (macula and optic disk centered) with both strategies. All images were acquired by trained healthcare professionals, de-identified, and graded independently by two masked ophthalmologists, with a third senior ophthalmologist adjudicating in discordant cases. The International Classification of Diabetic Retinopathy was used for grading, and demographic data, diabetic retinopathy classification, artifacts, and image quality were compared between devices. The tabletop senior ophthalmologist adjudication label was used as the ground truth for comparative analysis. A univariate and stepwise multivariate logistic regression was performed to determine the relationship of each independent factor in referable diabetic retinopathy. RESULTS: The mean age of participants was 57.03 years (SD 16.82, 9-90 years), and the mean duration of diabetes was 16.35 years (SD 9.69, 1-60 years). Age (P = .005), diabetes duration (P = .004), body mass index (P = .005), and hypertension (P < .001) were statistically different between referable and non-referable patients. Multivariate logistic regression analysis revealed a positive association between male sex (OR 1.687) and hypertension (OR 3.603) with referable diabetic retinopathy. The agreement between devices for diabetic retinopathy classification was 73.18%, with a weighted kappa of 0.808 (almost perfect). The agreement for macular edema was 88.48%, with a kappa of 0.809 (almost perfect). For referable diabetic retinopathy, the agreement was 85.88%, with a kappa of 0.716 (substantial), sensitivity of 0.906, and specificity of 0.808. As for image quality, 84.02% of tabletop fundus camera images were gradable and 85.31% of the Eyer images were gradable. CONCLUSIONS: Our study shows that the handheld retinal camera Eyer performed comparably to standard tabletop fundus cameras for diabetic retinopathy and macular edema screening. The high agreement with tabletop devices, portability, and low costs makes the handheld retinal camera a promising tool for increasing coverage of diabetic retinopathy screening programs, particularly in low-income countries. Early diagnosis and treatment have the potential to prevent avoidable blindness, and the present validation study brings evidence that supports its contribution to diabetic retinopathy early diagnosis and treatment.

TRANSPLANTATION OF SUBRETINAL STEM CELL-DERIVED RETINAL PIGMENT EPITHELIUM FOR STARGARDT DISEASE: A Phase I Clinical Trial.

PURPOSE: To assess the safety of injecting human embryonic stem cell retinal pigment epithelial cell dose to treat Stargardt disease. METHODS: In this prospective, Phase I clinical trial, human embryonic stem cell retinal pigment epithelial cells in suspension were injected into the subretinal space in eyes with the worse best-corrected visual acuity (BCVA). After vitrectomy/posterior hyaloid removal, a partial retinal detachment was created and the human embryonic stem cell retinal pigment epithelial cells were administered. Phacoemulsification with intraocular lens implantation was performed in eyes with lens opacity. All procedures were optical coherence tomography-guided. The 12-month follow-up included retinal imaging, optical coherence tomography, visual field/electrophysiologic testing, and systemic evaluation. The main outcome was the absence of ocular/systemic inflammation or rejection, tumor formation, or toxicity during follow-up. RESULTS: The mean baseline BCVAs in the phacoemulsification and no phacoemulsification groups were similar (1.950 ± 0.446 and 1.575 ± 0.303, respectively). One year postoperatively, treated eyes showed a nonsignificant increase in BCVA. No adverse effects occurred during follow-up. Intraoperative optical coherence tomography was important for guiding all procedures. CONCLUSION: This surgical procedure was feasible and safe without cellular migration, rejection, inflammation, or development of ocular or systemic tumors during follow-up.