Automated artificial intelligence‐based system for clinical follow‐up of patients with age‐related macular degeneration

Potapenko, Ivan; Thiesson, Bo; Kristensen, Mads; Hajari, Javad Nouri; Ilginis, Tomas; Fuchs, Josefine; Hamann, Steffen; Cour, Morten la

doi:10.1111/aos.15133

Cited by 6 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The high reliability of DR classification among the various graders in the Danish national screening programme could also have potential to be used for training of artificial intelligence‐based algorithms for DR grading. Such algorithms would often require access to tens of thousands of annotated retinal images, but the high agreement among observers in this study is likely to provide a sufficient ground truth grading for algorithm training, as it has also been demonstrated by Potapenko, Kristensen, et al (2022); Potapenko, Thiesson, et al (2022). in neovascular age‐related macular degeneration.…”

Section: Discussionsupporting

confidence: 55%

Inter‐grader reliability in the Danish screening programme for diabetic retinopathy

Thykjær

Andresen²,

Andersen³

et al. 2023

Acta Ophthalmologica

View full text Add to dashboard Cite

PurposeThe Danish Registry of Diabetic Retinopathy includes information from >200 000 patients who attends diabetic retinopathy (DR) screening in Denmark. Screening of patients with uncomplicated type 2 diabetes is often performed by practicing ophthalmologists, while patients with type 1 and complicated type 2 diabetes attends screening at hospitals. We performed a clinical reliability study of retinal images from Danish screening facilities to explore the inter‐grader agreement between the primary screening ophthalmologist and a blinded, certified grader.MethodsInvitations to participate were sent to screening facilities across Denmark. The primary grader uploaded fundus photographs with information on estimated level of DR (International Clinical Diabetic Retinopathy scale as 0 [no DR], 1–3 [mild, moderate or severe nonproliferative DR {NPDR}], or 4 [proliferative DR {PDR}]), region of screening, image style, and screening facility. Images were then regraded by a blinded, certified, secondary grader. Weighted kappa analysis was performed to evaluate agreement.ResultsFundus photographs from 230 patients (458 eyes) were received from practicing ophthalmologists (52.6%) and hospital‐based grading centres (47.4%) from all Danish regions. Reported levels of DR by the primary graders were 66.8%, 12.2%, 13.1%, 1.3% and 5.5% for DR levels 0–4. The overall agreement between primary and secondary graders was 93% (κ = 0.83). Based on screening facility agreement was 96% (κ = 0.89) and 90% (κ = 0.76) for practicing ophthalmologists and hospital‐based graders.ConclusionIn this nationwide study, we observed a high overall inter‐grader agreement and based on this, it is reasonable to assume that reported DR gradings in the screening programme in Denmark, accurately reflect the truth.

show abstract

Section: Discussionsupporting

confidence: 55%

Inter‐grader reliability in the Danish screening programme for diabetic retinopathy

Thykjær

Andresen²,

Andersen³

et al. 2023

Acta Ophthalmologica

View full text Add to dashboard Cite

show abstract

“…We anticipate that further training of the algorithm using larger image sets will improve the performance of the AI-based prediction of each fluid compartment status. Furthermore, if this improved AI algorithm can be combined with automated detection of each retinal fluid compartment 40 , it may contribute to the establishment of a better AI-based patient follow-up system 41 .…”

Section: Importance Of Predicting the Difference In Post-treatment Oc...mentioning

confidence: 99%

Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network

Moon

Lee

Hwang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

To develop an artificial intelligence (AI) model that predicts anti-vascular endothelial growth factor (VEGF) agent-specific anatomical treatment outcomes in neovascular age-related macular degeneration (AMD), thereby assisting clinicians in selecting the most suitable anti-VEGF agent for each patient. This retrospective study included patients diagnosed with neovascular AMD who received three loading injections of either ranibizumab or aflibercept. Training was performed using optical coherence tomography (OCT) images with an attention generative adversarial network (GAN) model. To test the performance of the AI model, the sensitivity and specificity to predict the presence of retinal fluid after treatment were calculated for the AI model, an experienced (Examiner 1), and a less experienced (Examiner 2) human examiners. A total of 1684 OCT images from 842 patients (419 treated with ranibizumab and 423 treated with aflibercept) were used as the training set. Testing was performed using images from 98 patients. In patients treated with ranibizumab, the sensitivity and specificity, respectively, were 0.615 and 0.667 for the AI model, 0.385 and 0.861 for Examiner 1, and 0.231 and 0.806 for Examiner 2. In patients treated with aflibercept, the sensitivity and specificity, respectively, were 0.857 and 0.881 for the AI model, 0.429 and 0.976 for Examiner 1, and 0.429 and 0.857 for Examiner 2. In 18.5% of cases, the fluid status of synthetic posttreatment images differed between ranibizumab and aflibercept. The AI model using GAN might predict anti-VEGF agent-specific short-term treatment outcomes with relatively higher sensitivity than human examiners. Additionally, there was a difference in the efficacy in fluid resolution between the anti-VEGF agents. These results suggest the potential of AI in personalized medicine for patients with neovascular AMD.

show abstract

“…Artificial intelligence (AI) models have shown equal or better performance in disease diagnosis and management based on the medical image, such as diabetic retinopathy ( Ruamviboonsuk et al, 2022 ), glaucoma ( Medeiros et al, 2021 ; Ibrahim et al, 2022 ), age-related macular degeneration ( Yan et al, 2021 ; Potapenko et al, 2022 ), congenital cataract ( Lin et al, 2019 ), central serous chorioretinopathy ( Xu et al, 2021 ; Jin and Ye, 2022 ), and papilledema ( Milea et al, 2020 ). AI-based teaching can improve students’ or junior residents’ performance and satisfaction during ophthalmology clerkship, especially showing an advantage in deepening understanding of signs and morphological features ( Wu et al, 2020 ; Han et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program

Fang

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Background: Artificial intelligence (AI) has been successfully applied to the screening tasks of fundus diseases. However, few studies focused on the potential of AI to aid medical teaching in the residency training program. This study aimed to evaluate the effectiveness of the AI-based pathologic myopia (PM) identification system in the ophthalmology residency training program and assess the residents’ feedback on this system.Materials and Methods: Ninety residents in the ophthalmology department at the Second Affiliated Hospital of Zhejiang University were randomly assigned to three groups. In group A, residents learned PM through an AI-based PM identification system. In group B and group C, residents learned PM through a traditional lecture given by two senior specialists independently. The improvement in resident performance was evaluated by comparing the pre-and post-lecture scores of a specifically designed test using a paired t-test. The difference among the three groups was evaluated by one-way ANOVA. Residents’ evaluations of the AI-based PM identification system were measured by a 17-item questionnaire.Results: The post-lecture scores were significantly higher than the pre-lecture scores in group A (p < 0.0001). However, there was no difference between pre-and post-lecture scores in group B (p = 0.628) and group C (p = 0.158). Overall, all participants were satisfied and agreed that the AI-based PM identification system was effective and helpful to acquire PM identification, myopic maculopathy (MM) classification, and “Plus” lesion localization.Conclusion: It is still difficult for ophthalmic residents to promptly grasp the knowledge of identification of PM through a single traditional lecture, while the AI-based PM identification system effectively improved residents’ performance in PM identification and received satisfactory feedback from residents. The application of the AI-based PM identification system showed advantages in promoting the efficiency of the ophthalmology residency training program.

show abstract

Automated artificial intelligence‐based system for clinical follow‐up of patients with age‐related macular degeneration

Cited by 6 publications

References 33 publications

Inter‐grader reliability in the Danish screening programme for diabetic retinopathy

Inter‐grader reliability in the Danish screening programme for diabetic retinopathy

Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program

Contact Info

Product

Resources

About