Dataset Information

Artificial intelligence-based decision-making for age-related macular degeneration.

ABSTRACT: Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces. Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis. Results: Here, we present an AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD. Through deep learning process based on the analysis of preprocessed optical coherence tomography (OCT) imaging data, our AI-based system achieved the same image discrimination rate as that of retinal specialists in our hospital. The AI platform's detection accuracy was generally higher than 90% and was significantly superior (p < 0.001) to that of medical students (69.4% and 68.9%) and equal (p = 0.99) to that of retinal specialists (92.73% and 91.90%). Furthermore, it provided appropriate treatment recommendations comparable to those of retinal specialists. Conclusions: We therefore developed a website for realistic cloud computing based on this AI platform, available at https://www.ym.edu.tw/~AI-OCT/. Patients can upload their OCT images to the website to verify whether they have AMD and require treatment. Using an AI-based cloud service represents a real solution for medical imaging diagnostics and telemedicine.

SUBMITTER: Hwang DK

PROVIDER: S-EPMC6332801 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Artificial intelligence-based decision-making for age-related macular degeneration.

Hwang De-Kuang DK Hsu Chih-Chien CC Chang Kao-Jung KJ Chao Daniel D Sun Chuan-Hu CH Jheng Ying-Chun YC Yarmishyn Aliaksandr A AA Wu Jau-Ching JC Tsai Ching-Yao CY Wang Mong-Lien ML Peng Chi-Hsien CH Chien Ke-Hung KH Kao Chung-Lan CL Lin Tai-Chi TC Woung Lin-Chung LC Chen Shih-Jen SJ Chiou Shih-Hwa SH

Theranostics 20190101 1

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces. <b>Methods:</b> In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related ...[more]

PMID: 30662564

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Project description:ObjectiveRecent developments in artificial intelligence (AI) have positioned it to transform several stages of the clinical trial process. In this study, we explore the role of AI in clinical trial recruitment of individuals with geographic atrophy (GA), an advanced stage of age-related macular degeneration, amidst numerous ongoing clinical trials for this condition.DesignCross-sectional study.SubjectsRetrospective dataset from the INSIGHT Health Data Research Hub at Moorfields Eye Hospital in London, United Kingdom, including 306 651 patients (602 826 eyes) with suspected retinal disease who underwent OCT imaging between January 1, 2008 and April 10, 2023.MethodsA deep learning model was trained on OCT scans to identify patients potentially eligible for GA trials, using AI-generated segmentations of retinal tissue. This method's efficacy was compared against a traditional keyword-based electronic health record (EHR) search. A clinical validation with fundus autofluorescence (FAF) images was performed to calculate the positive predictive value of this approach, by comparing AI predictions with expert assessments.Main outcome measuresThe primary outcomes included the positive predictive value of AI in identifying trial-eligible patients, and the secondary outcome was the intraclass correlation between GA areas segmented on FAF by experts and AI-segmented OCT scans.ResultsThe AI system shortlisted a larger number of eligible patients with greater precision (1139, positive predictive value: 63%; 95% confidence interval [CI]: 54%-71%) compared with the EHR search (693, positive predictive value: 40%; 95% CI: 39%-42%). A combined AI-EHR approach identified 604 eligible patients with a positive predictive value of 86% (95% CI: 79%-92%). Intraclass correlation of GA area segmented on FAF versus AI-segmented area on OCT was 0.77 (95% CI: 0.68-0.84) for cases meeting trial criteria. The AI also adjusts to the distinct imaging criteria from several clinical trials, generating tailored shortlists ranging from 438 to 1817 patients.ConclusionsThis study demonstrates the potential for AI in facilitating automated prescreening for clinical trials in GA, enabling site feasibility assessments, data-driven protocol design, and cost reduction. Once treatments are available, similar AI systems could also be used to identify individuals who may benefit from treatment.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Project description:PurposeTo evaluate the performance of a disease activity (DA) model developed to detect DA in participants with neovascular age-related macular degeneration (nAMD).DesignPost hoc analysis.ParticipantsPatient dataset from the phase III HAWK and HARRIER (H&H) studies.MethodsAn artificial intelligence (AI)-based DA model was developed to generate a DA score based on measurements of OCT images and other parameters collected from H&H study participants. Disease activity assessments were classified into 3 categories based on the extent of agreement between the DA model's scores and the H&H investigators' decisions: agreement ("easy"), disagreement ("noisy"), and close to the decision boundary ("difficult"). Then, a panel of 10 international retina specialists ("panelists") reviewed a sample of DA assessments of these 3 categories that contributed to the training of the final DA model. A panelists' majority vote on the reviewed cases was used to evaluate the accuracy, sensitivity, and specificity of the DA model.Main outcome measuresThe DA model's performance in detecting DA compared with the DA assessments made by the investigators and panelists' majority vote.ResultsA total of 4472 OCT DA assessments were used to develop the model; of these, panelists reviewed 425, categorized as "easy" (17.2%), "noisy" (20.5%), and "difficult" (62.4%). False-positive and false negative rates of the DA model's assessments decreased after changing the assessment in some cases reviewed by the panelists and retraining the DA model. Overall, the DA model achieved 80% accuracy. For "easy" cases, the DA model reached 96% accuracy and performed as well as the investigators (96% accuracy) and panelists (90% accuracy). For "noisy" cases, the DA model performed similarly to panelists and outperformed the investigators (84%, 86%, and 16% accuracies, respectively). The DA model also outperformed the investigators for "difficult" cases (74% and 53% accuracies, respectively) but underperformed the panelists (86% accuracy) owing to lower specificity. Subretinal and intraretinal fluids were the main clinical parameters driving the DA assessments made by the panelists.ConclusionsThese results demonstrate the potential of using an AI-based DA model to optimize treatment decisions in the clinical setting and in detecting and monitoring DA in patients with nAMD.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Project description:BackgroundAge-related macular degeneration (AMD) represents a significant clinical concern, particularly in aging populations, and recent advancements in artificial intelligence (AI) have catalyzed substantial research interest in this domain. Despite the growing body of literature, there remains a need for a comprehensive, quantitative analysis to delineate key trends and emerging areas in the field of AI applications in AMD. This bibliometric analysis sought to systematically evaluate the landscape of AI-focused research on AMD to illuminate publication patterns, influential contributors, and focal research trends.MethodsUsing the Web of Science Core Collection (WoSCC), a search was conducted to retrieve relevant publications from 1992 to 2023. This analysis involved an array of bibliometric indicators to map the evolution of AI research in AMD, assessing parameters such as publication volume, national/regional and institutional contributions, journal impact, author influence, and emerging research hotspots. Visualization tools, including Bibliometrix, CiteSpace and VOSviewer, were employed to generate comprehensive assessments of the data.ResultsA total of 1,721 publications were identified, with the USA leading in publication output and the University of Melbourne as the most prolific institution. The journal Investigative Ophthalmology & Visual Science published the highest number of articles, and Schmidt-Eerfurth emerged as the most active author. Keyword and clustering analyses, along with citation burst detection, revealed three distinct research stages within the field from 1992 to 2023. Presently, research efforts are concentrated on developing deep learning (DL) models for AMD diagnosis and progression prediction. Prominent emerging themes include early detection, risk stratification, and treatment efficacy prediction. The integration of large language models (LLMs) and vision-language models (VLMs) for enhanced image processing also represents a novel research frontier.ConclusionsThis bibliometric analysis provides a structured overview of prevailing research trends and emerging directions in AI applications for AMD. These findings furnish valuable insights to guide future research and foster collaborative advancements in this evolving field.

Dataset Information

Artificial intelligence-based decision-making for age-related macular degeneration.

Publications

Artificial intelligence-based decision-making for age-related macular degeneration.

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