Dataset Information

Use of a rich internet application solution to present medical images.

ABSTRACT: Browser with Rich Internet Application (RIA) Web pages could be a powerful user interface for handling sophisticated data and applications. Then the RIA solutions would be a potential method for viewing and manipulating the most data generated in clinical processes, which can accomplish the main functionalities as general picture archiving and communication system (PACS) viewing systems. The aim of this study is to apply the RIA technology to present medical images. Both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM data can be handled by our RIA solutions. Some clinical data that are especially difficult to present using PACS viewing systems, such as ECG waveform, pathology virtual slide microscopic image, and radiotherapy plan, are as well demonstrated. Consequently, clinicians can use browser as a unique interface for acquiring all the clinical data located in different departments and information systems. And the data could be presented appropriately and processed freely by adopting the RIA technologies.

SUBMITTER: Hsiao CH

PROVIDER: S-EPMC3222552 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Use of a rich internet application solution to present medical images.

Hsiao Chia-Hung CH Shiau Cheng-Ying CY Liu Yu-Ming YM Chao Max Min MM Lien Chung-Yueh CY Chen Chi-Hsien CH Yen Sang-Hue SH Tang Shih-Tsang ST

Journal of digital imaging 20111201 6

Browser with Rich Internet Application (RIA) Web pages could be a powerful user interface for handling sophisticated data and applications. Then the RIA solutions would be a potential method for viewing and manipulating the most data generated in clinical processes, which can accomplish the main functionalities as general picture archiving and communication system (PACS) viewing systems. The aim of this study is to apply the RIA technology to present medical images. Both Digital Imaging and Comm ...[more]

PMID: 21424328

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Project description:PurposeDeveloping robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-chosen data that can prove challenging to collect because of privacy concerns, disease rarity, or diagnostic label quality. Collecting image-based datasets for retinopathy of prematurity (ROP), a potentially blinding disease, suffers from these challenges. Progressively growing generative adversarial networks (PGANs) may help, because they can synthesize highly realistic images that may increase both the size and diversity of medical datasets.DesignDiagnostic validation study of convolutional neural networks (CNNs) for plus disease detection, a component of severe ROP, using synthetic data.ParticipantsFive thousand eight hundred forty-two retinal fundus images (RFIs) collected from 963 preterm infants.MethodsRetinal vessel maps (RVMs) were segmented from RFIs. PGANs were trained to synthesize RVMs with normal, pre-plus, or plus disease vasculature. Convolutional neural networks were trained, using real or synthetic RVMs, to detect plus disease from 2 real RVM test datasets.Main outcome measuresFeatures of real and synthetic RVMs were evaluated using uniform manifold approximation and projection (UMAP). Similarities were evaluated at the dataset and feature level using Fréchet inception distance and Euclidean distance, respectively. CNN performance was assessed via area under the receiver operating characteristic curve (AUC); AUCs were compared via bootstrapping and Delong's test for correlated receiver operating characteristic curves. Confusion matrices were compared using McNemar's chi-square test and Cohen's κ value.ResultsThe CNN trained on synthetic RVMs showed a significantly higher AUC (0.971; P = 0.006 and P = 0.004) and classified plus disease more similarly to a set of 8 international experts (κ = 0.922) than the CNN trained on real RVMs (AUC = 0.934; κ = 0.701). Real and synthetic RVMs overlapped, by plus disease diagnosis, on the UMAP manifold, showing that synthetic images spanned the disease severity spectrum. Fréchet inception distance and Euclidean distances suggested that real and synthetic RVMs were more dissimilar to one another than real RVMs were to one another, further suggesting that synthetic RVMs were distinct from the training data with respect to privacy considerations.ConclusionsSynthetic datasets may be useful for training robust medical AI models. Furthermore, PGANs may be able to synthesize realistic data for use without protected health information concerns.

Dataset Information

Use of a rich internet application solution to present medical images.

Publications

Use of a rich internet application solution to present medical images.

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