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Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.

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Title: Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.
Authors: Diot-Dejonghe, Tiphaine, Leporq, Benjamin, Bouhamama, Amine, Ratiney, Helene, Pilleul, Frank, Beuf, Olivier, Cervenansky, Frederic
Source: Journal of Digital Imaging; Oct2024, Vol. 37 Issue 5, p2612-2626, 15p
Subject Terms: OSTEOSARCOMA, DATA security, DIAGNOSTIC imaging, COMPUTER-assisted image analysis (Medicine), MEDICAL informatics, RESEARCH funding, RADIOMICS, TREATMENT effectiveness, INTERNET, DEEP learning, SOFTWARE architecture, MACHINE learning, PATIENT monitoring, INDIVIDUALIZED medicine, ACCESS to information, EVALUATION
Abstract: Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians. [ABSTRACT FROM AUTHOR]
ISSN:08971889
DOI:10.1007/s10278-024-01110-0