An online platform for interactive feedback in biomedical machine learning

Abid, Abubakar; Abdalla, Ali; Ali, Abid; Khan, Dawood Ashraf; Alfozan, Abdulrahman; Zou, James

doi:10.1038/s42256-020-0147-8

Cited by 17 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Making an AI algorithm publically available also presents challenges because there is intellectual property involved, and the complexities around interpreting the code can be complicated. Online platforms have now been created that will host AI models and allow users to interactively test them with their own images or data ( Abid et al., 2020 ).…”

Section: Ai Policies and Positionsmentioning

confidence: 99%

Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential

Kovarik¹

2022

JID Innovations

View full text Add to dashboard Cite

Section: Ai Policies and Positionsmentioning

confidence: 99%

Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential

Kovarik¹

2022

JID Innovations

View full text Add to dashboard Cite

“…Biomedisa, developed at Heidelberg University, is an example of an open-source medical imaging library which has been shown to handle a wide variety of volumetric data ( Lösel et al, 2020 ). Another online tool, GradioHub, has been proposed as a collaborative way for clinicians and biomedical researchers to share and study data ( Abid et al, 2020 ). Finally, the open-source tool Studierfenster has been recently been created for the purpose of biomedical data visualization, enabling rendering of both 3D and 2D data, as well as letting the user annotate the data they are examining ( Egger et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

N-Tools-Browser: Web-Based Visualization of Electrocorticography Data for Epilepsy Surgery

et al. 2022

View full text Add to dashboard Cite

Epilepsy affects more than three million people in the United States. In approximately one-third of this population, anti-seizure medications do not control seizures. Many patients pursue surgical treatment that can include a procedure involving the implantation of electrodes for intracranial monitoring of seizure activity. For these cases, accurate mapping of the implanted electrodes on a patient’s brain is crucial in planning the ultimate surgical treatment. Traditionally, electrode mapping results are presented in static figures that do not allow for dynamic interactions and visualizations. In collaboration with a clinical research team at a Level 4 Epilepsy Center, we developed N-Tools-Browser, a web-based software using WebGL and the X-Toolkit (XTK), to help clinicians interactively visualize the location and functional properties of implanted intracranial electrodes in 3D. Our software allows the user to visualize the seizure focus location accurately and simultaneously display functional characteristics (e.g., results from electrical stimulation mapping). Different visualization modes enable the analysis of multiple electrode groups or individual anatomical locations. We deployed a prototype of N-Tools-Browser for our collaborators at the New York University Grossman School of Medicine Comprehensive Epilepsy Center. Then, we evaluated its usefulness with domain experts on clinical cases.

show abstract

“…In summary, an online environment would make the use of AR and VR devices more widespread in the medical domain, especially for medical teaching, training, and web conferencing. The first steps in this direction have already been taken, for example, with an online platform for interactive feedback in biomedical machine learning ( https://www.gradiohub.com , currently not reachable anymore, last accessed from the authors around July 2021) [ 22 ] and Biomedisa, an open-source online platform for biomedical image segmentation ( https://biomedisa.de/ ) [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform

et al. 2022

View full text Add to dashboard Cite

Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. In this context, we introduce Studierfenster ( www.studierfenster.at ): a free, non-commercial open science client-server framework for (bio-)medical image analysis. Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. When participants were asked about their overall impression of Studierfenster in an ISO standard (ISO-Norm) questionnaire, a mean of 6.3 out of 7.0 possible points were achieved. The evaluation also provided insights into the results achievable with Studierfenster in practice, by comparing these with two ground truth segmentations performed by a physician of the Medical University of Graz in Austria. In this contribution, we presented an online environment for (bio-)medical image analysis. In doing so, we established a client-server-based architecture, which is able to process medical data, especially 3D volumes. Our online environment is not limited to medical applications for humans. Rather, its underlying concept could be interesting for researchers from other fields, in applying the already existing functionalities or future additional implementations of further image processing applications. An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448–456, 68 ], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. Furthermore, applications in entirely non-medical research in which images/volumes need to be processed are also thinkable, such as those in optical measuring techniques, astronomy, or archaeology. ...

show abstract

An online platform for interactive feedback in biomedical machine learning

Cited by 17 publications

References 13 publications

Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential

Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential

N-Tools-Browser: Web-Based Visualization of Electrocorticography Data for Epilepsy Surgery

Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform

Contact Info

Product

Resources

About