Towards effective data sharing in ophthalmology: data standardization and data privacy

Halfpenny, William; Baxter, Sally L.

doi:10.1097/icu.0000000000000878

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…One data model, the Observation Medical Outcomes Partnership Common Data Model, provides a standard for merging and unifying data from different EHR systems 72,73 . In the future, developing and implementing standards for various types of data will be essential for accelerating AI techniques in ophthalmology 55 …”

Section: Resultsmentioning

confidence: 99%

“…51 Proposed solutions include standardizing images from various imaging platforms and training models with large data sets consisting of annotated real-world data, a wide range of image quality, and different types of imaging data. 45,51,52 The call for data standardization in the ophthalmology community across multiple data modalities has become more prominent in recent years [53][54][55] and broad-based efforts, such as through DICOM Working Group 9, the American Academy of Ophthalmology, the National Eye Institute, and the Observation Health Data Sciences and Informatics organization, are ongoing to advance standardization and enable improved interoperability and data harmonization across different data sources.…”

Section: Importance Of Data Source Diversity and Data Standardization...mentioning

confidence: 99%

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Review of Visualization Approaches in Deep Learning Models of Glaucoma

Sidhu

Weinreb

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.

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Section: Resultsmentioning

confidence: 99%

Section: Importance Of Data Source Diversity and Data Standardization...mentioning

confidence: 99%

Review of Visualization Approaches in Deep Learning Models of Glaucoma

Sidhu

Weinreb

et al. 2023

Asia-Pacific Journal of Ophthalmology

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“…Most machine learning/deep network models assume the data used for training and testing are independent and identically distributed with samples from a reference probability distribution, which can pose a certain level of limitation on the model's generalization. It is well noted that the performance of a model usually degrades when tested on a distinct dataset due to the domain shift (27,28). Moreover, it is well recognized that medical image datasets are most often heterogeneous due to scan, acquisition protocol, and subject level differences.…”

Section: Introductionmentioning

confidence: 99%

A multi-object deep neural network architecture to detect prostate anatomy in T2-weighted MRI: Performance evaluation

Baldeon-Calisto

Wei²,

Abudalou³

et al. 2023

Front. Nucl. Med.

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Prostate gland segmentation is the primary step to estimate gland volume, which aids in the prostate disease management. In this study, we present a 2D-3D convolutional neural network (CNN) ensemble that automatically segments the whole prostate gland along with the peripheral zone (PZ) (PPZ-SegNet) using a T2-weighted sequence (T2W) of Magnetic Resonance Imaging (MRI). The study used 4 different public data sets organized as Train #1 and Test #1 (independently derived from the same cohort), Test #2, Test #3 and Test #4. The prostate gland and the peripheral zone (PZ) anatomy were manually delineated with consensus read by a radiologist, except for Test #4 cohorts that had pre-marked glandular anatomy. A Bayesian hyperparameter optimization method was applied to construct the network model (PPZ-SegNet) with a training cohort (Train #1, n = 150) using a five-fold cross validation. The model evaluation was performed on an independent cohort of 283 T2W MRI prostate cases (Test #1 to #4) without any additional tuning. The data cohorts were derived from The Cancer Imaging Archives (TCIA): PROSTATEx Challenge, Prostatectomy, Repeatability studies and PROMISE12-Challenge. The segmentation performance was evaluated by computing the Dice similarity coefficient and Hausdorff distance between the estimated-deep-network identified regions and the radiologist-drawn annotations. The deep network architecture was able to segment the prostate gland anatomy with an average Dice score of 0.86 in Test #1 (n = 192), 0.79 in Test #2 (n = 26), 0.81 in Test #3 (n = 15), and 0.62 in Test #4 (n = 50). We also found the Dice coefficient improved with larger prostate volumes in 3 of the 4 test cohorts. The variation of the Dice scores from different cohorts of test images suggests the necessity of more diverse models that are inclusive of dependencies such as the gland sizes and others, which will enable us to develop a universal network for prostate and PZ segmentation. Our training and evaluation code can be accessed through the link: https://github.com/mariabaldeon/PPZ-SegNet.git.

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“… 19 , 20 Standardized representation of data, including structured data and images, is needed to facilitate data sharing and has the potential to increase the quality of clinical care and research. 20 , 21 , 22 …”

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confidence: 99%

Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts

Chen,

Copado,

Vallejos

et al. 2024

Ophthalmology Science

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Towards effective data sharing in ophthalmology: data standardization and data privacy

Cited by 7 publications

References 39 publications

Review of Visualization Approaches in Deep Learning Models of Glaucoma

Review of Visualization Approaches in Deep Learning Models of Glaucoma

A multi-object deep neural network architecture to detect prostate anatomy in T2-weighted MRI: Performance evaluation

Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts

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