A Web-platform for radiotherapy, a new workflow concept and an information sharing tool
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Create an information network to develop predictive individualized models of the tumor response to radiotherapy, able to define more effective adaptive treatments.
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2015 IEEE Papers just contact us for implementation www.redpel.com +917620593389 redpelsoftware@gmail.com
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[DSC MENA 24] Amal_Elgammal_-_QUALITOP_presentation.pptx
Background: The digital twin paradigm holds great promise for healthcare, most importantly efficiently integrating many disparate healthcare data sources and servicing complex tasks like personalizing care, predicting health outcomes, and planning patient care, even though many technical and scientific challenges remain to be overcome. Objective: As part of the QUALITOP project, we conducted a comprehensive analysis of diverse healthcare data, encompassing both prospective and retrospective datasets, along with an in-depth examination of the advanced analytical needs of medical institutions across five European Union countries. Through these endeavors, we have systematically developed and refined a formal Personal Medical Digital Twin (PMDT) model subjected to iterative validation by medical institutions to ensure its applicability, efficacy, and utility. Findings: The PMDT is based on an interconnected set of expressive knowledge structures that are calibrated to capture an individual patient’s psychosomatic, cognitive, biometrical and genetic information in one personal digital footprint in a manner that allows medical professionals to run various models to predict an individual’s health issues over time and intervene early with personalized preventive care.Conclusion: At the forefront of digital transformation, the PMDT emerges as a pivotal entity, positioned at the convergence of Big Data and Artificial Intelligence. This paper introduces a PMDT environment that lays the foundation for the application of comprehensive big data analytics, continuous monitoring, cognitive simulations, and AI techniques. By integrating stakeholders across the care continuum, including patients, this system enables the derivation of insights and facilitates informed decision-making for personalized preventive care.
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Components And Workflow Of A Digital Radiology Department
Components And Workflow Of A Digital Radiology DepartmentMonief Eid,Prince2,Prosci, Lean Six Sigma &ITIL
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2015 IEEE Papers just contact us for implementation www.redpel.com +917620593389 redpelsoftware@gmail.com
2019 Triangle Machine Learning Day - Biomedical Image Understanding and EHRs ...
2019 Triangle Machine Learning Day - Biomedical Image Understanding and EHRs ...The Statistical and Applied Mathematical Sciences Institute
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.Picture Archiving and Communication Systems
Theoretical principles and practical implications of Picture Archiving and Communication Systems (PACS). I also introduce the concept of PACS Maturity, strategic planning, Business/IT-alingment in radiology
Distriburted medical image system
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[DSC MENA 24] Amal_Elgammal_-_QUALITOP_presentation.pptx
Background: The digital twin paradigm holds great promise for healthcare, most importantly efficiently integrating many disparate healthcare data sources and servicing complex tasks like personalizing care, predicting health outcomes, and planning patient care, even though many technical and scientific challenges remain to be overcome. Objective: As part of the QUALITOP project, we conducted a comprehensive analysis of diverse healthcare data, encompassing both prospective and retrospective datasets, along with an in-depth examination of the advanced analytical needs of medical institutions across five European Union countries. Through these endeavors, we have systematically developed and refined a formal Personal Medical Digital Twin (PMDT) model subjected to iterative validation by medical institutions to ensure its applicability, efficacy, and utility. Findings: The PMDT is based on an interconnected set of expressive knowledge structures that are calibrated to capture an individual patient’s psychosomatic, cognitive, biometrical and genetic information in one personal digital footprint in a manner that allows medical professionals to run various models to predict an individual’s health issues over time and intervene early with personalized preventive care.Conclusion: At the forefront of digital transformation, the PMDT emerges as a pivotal entity, positioned at the convergence of Big Data and Artificial Intelligence. This paper introduces a PMDT environment that lays the foundation for the application of comprehensive big data analytics, continuous monitoring, cognitive simulations, and AI techniques. By integrating stakeholders across the care continuum, including patients, this system enables the derivation of insights and facilitates informed decision-making for personalized preventive care.
Computational Pathology Workshop July 8 2014
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tranSMART Community Meeting 5-7 Nov 13 - Session 3: The TraIT user stories fo...
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- Existing demonstrators using tranSMART include DeCoDe (colorectal cancer) and PCMM (prostate cancer).
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TCIA Update
The Cancer Imaging Archive provides several updates, including a text search feature to search DICOM metadata fields, filtering data collections by available data types, and new web pages on imaging proteogenomics and clinical trials. A prostate MRI repeatability collection was added and publications were highlighted using TCIA MRI and genetic data to develop models predicting glioma prognosis and treatment response. The NCI Imaging Data Commons RFP was promoted and BraTS test data is now available upon request.
AAPM Foster July 2009
I gave this talk in the "Presidential Symposium" at the annual meeting of the American Association of Physicists in Medicine, in Annaheim, California. The President of AAPM, Dr. Maryellen Giger, wanted some people to give some visionary talks. She invited (I kid you not) Foster, Gates, and Obama. Fortunately Bill and Barack had other commitments, so I did not need to share the time with them.
HETT Conference Olympic Central 2014 Integrating Healthcare Delivery
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BIG DATA forest
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HP E INTEL CONMEMORAN LOS 20 AÑOS DEL CENTRO DE SUPERCOMPUTACIÓN DE GALICIA
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A Web-platform for radiotherapy, a new workflow concept and an information sharing tool
- 1. A Web-platform for radiotherapy, a new workflow concept and an information sharing tool Andrés Gómez, J.C. Mouriño, & M Sánchez agomez@cesga.es This work has been supported by ISCIII Grant PI11/02035
- 2. ARTFIBio •Objective: create an information network to develop predictive individualized models of the tumor response to radiotherapy, able to define more effective adaptive treatments –Developing all the tools for the evaluation of the tumor response in Head&Neck Cancer based on functional images, dissymmetry, incidents during the treatment and biopsies, with a predictive analytic model that considers the initial state and the expected treatment and an adaptive model that considers the early tumor response to the treatment. –Develop a web infrastructure for research in RT and other oncology specializations that allows the evaluation of the tumor response to several treatments based on functional images and other data. •Radiotherapy (RT) is changing from image guided to biology guided
- 3. ARTFIBio. WEB Infrastructure •Shared data store –Chemotherapy data (*) –Surgery data (*) –Biopsies data –Patient’s images (CT, MRI, PET) (*) –Radiotherapy Plan data (*) –Radiotherapy Delivery data •Registration tools (*) •Infrastructure to check new models •Integration with e-IMRT (verification of treatment doses)
- 4. ARTFIBIO - DICOM Hierarchy STUDY CT Object PET Object MRI Object RT Dose Object RT Image Object RT Plan Object RT Struct Object CASE SERIE ??
- 5. ARTFIBio. General view NEW CASE GENERAL DATA UPLOAD FILES VERIFY FILES CHEMOTERAPY SURGERY
- 6. Treatment General Data NEW CASE GENERAL DATA UPLOAD FILES VERIFY FILES CHEMOTERAPY SURGERY
- 7. Upload Files NEW CASE GENERAL DATA UPLOAD FILES VERIFY FILES CHEMOTERAPY SURGERY •Anonymises the DICOM Objects •Compresses them in one file. •Submits them to server.
- 8. Check FIles NEW CASE GENERAL DATA UPLOAD FILES VERIFY FILES CHEMOTERAPY SURGERY
- 9. CHEMOTHERAPY NEW CASE GENERAL DATA UPLOAD FILES VERIFY FILES CHEMOTERAPY SURGERY •Optional. From the case list.
- 10. SURGERY NEW CASE GENERAL DATA UPLOAD FILES VERIFY FILES CHEMOTERAPY SURGERY •Optional. From the case list.
- 12. See Case. General Info and Actions
- 13. Schedule. CT Calendar View to see when data were collected Can select the data type to view View and annotate the data Download the annonymised DICOM
- 14. Schedule. PET For each type of DICOM object, some metadata can be edited. But DICOM object is NOT changed.
- 15. Schedule. MRI For each type of DICOM object, some metadata can be edited. But DICOM object is NOT changed.
- 16. SCHEDULE. RT DOSE •All info in a single DICOM object. Each z-plane of the bulk image can be previewed
- 17. See Case. Genral Info and Actions
- 18. Visualization & Registry •Based on Virtual Machines •Integrates Registry Application •This method can be used for other desktop Linux Applications
- 19. That’s all Folks! Thanks to the ARTFIBio team for their inputs