DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications

Yi, Thomas; Pan, Ian; Collins, Scott; Chen, Fiona; Cueto, Robert; Hsieh, B.J.; Hsieh, Celina; Smith, Jo; Yang, Li; Liao, Weihua; Merck, Lisa H.; Bai, Harrison X.; Merck, Derek

doi:10.1007/s10278-021-00488-5

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…In fact, traditional methods to access images from the Picturing Archiving and Communication System (PACS) require repetitive, manual querying of the electronic health records, making real-time communication between advanced analytic systems infeasible. To address these gaps, this study utilizes DICOM Image Analysis and Archive (DIANA) to retrieve requested medical images in real-time and pass them as inputs for AI analysis 25 . DIANA uses Docker containerization to easily deploy AI solutions without customized development and acts as a data retrieval engine from the image database.…”

Section: Discussionmentioning

confidence: 99%

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An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

et al. 2022

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While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital’s image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

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Section: Discussionmentioning

confidence: 99%

“…At a high level, DIANA uses containerized Orthanc instances to communicate with PACS and programmatically retrieve anonymized images. Images are processed on an AI container and presented to end-users on a communications platform of choice 25 .…”

Section: Methodsmentioning

confidence: 99%

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

et al. 2022

Self Cite

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“…In recent years, some techniques have been developed to automate image annotation such as natural language processing techniques and structured radiological reports, but they are not yet widely available in hospitals 25 . And the same is true of data exploitation tools, such as the one published in 2021 by Yi et al 26 . We believe that with these tools it will soon be possible to feed models with large annotated image datasets and with large amounts of tabular data directly from the patient's EHR.…”

Section: Discussionmentioning

confidence: 99%

A deep learning model for prognosis prediction after intracranial hemorrhage

Barrio

Domínguez

Fernández-Miranda

et al. 2022

Journal of Neuroimaging

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Background and Purpose Intracranial hemorrhage (ICH) is a common life‐threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. Methods We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I‐model) and the other with tabular data only (D‐model). Results Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831‐.986), and an accuracy of .861 (95% CI: .760‐.960). The I‐ and D‐models achieved an AUC of .763 (95% CI: .622‐.902) and .746 (95% CI: .598‐.876), respectively. Conclusions The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision‐making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.

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A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey

Vahdati,

Khosravi,

Mahmoudi

et al. 2024

J Digit Imaging. Inform. med.

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In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

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DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications

Cited by 6 publications

References 30 publications

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

A deep learning model for prognosis prediction after intracranial hemorrhage

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey

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