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Diot-Dejonghe T, Leporq B, Bouhamama A, Ratiney H, Pilleul F, Beuf O, Cervenansky F. Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01110-0. [PMID: 38689149 DOI: 10.1007/s10278-024-01110-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/12/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024]
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.
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Affiliation(s)
- Tiphaine Diot-Dejonghe
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France
| | - Benjamin Leporq
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France
| | - Amine Bouhamama
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France
- Department of Radiology, Centre Léon Bérard, 28 Prom. Léa et Napoléon Bullukian, Lyon, 69008, France
| | - Helene Ratiney
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France
| | - Frank Pilleul
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France
- Department of Radiology, Centre Léon Bérard, 28 Prom. Léa et Napoléon Bullukian, Lyon, 69008, France
| | - Olivier Beuf
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France
| | - Frederic Cervenansky
- INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France.
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Chiumello D, Coppola S, Catozzi G, Danzo F, Santus P, Radovanovic D. Lung Imaging and Artificial Intelligence in ARDS. J Clin Med 2024; 13:305. [PMID: 38256439 PMCID: PMC10816549 DOI: 10.3390/jcm13020305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/26/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible.
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Affiliation(s)
- Davide Chiumello
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
- Department of Anesthesia and Intensive Care, ASST Santi Paolo e Carlo, San Paolo University Hospital Milan, 20142 Milan, Italy
- Coordinated Research Center on Respiratory Failure, University of Milan, 20122 Milan, Italy
| | - Silvia Coppola
- Department of Anesthesia and Intensive Care, ASST Santi Paolo e Carlo, San Paolo University Hospital Milan, 20142 Milan, Italy
| | - Giulia Catozzi
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Fiammetta Danzo
- Division of Respiratory Diseases, Luigi Sacco University Hospital, ASST Fatebenefratelli-Sacco, 20157 Milan, Italy
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, 20157 Milan, Italy
| | - Pierachille Santus
- Division of Respiratory Diseases, Luigi Sacco University Hospital, ASST Fatebenefratelli-Sacco, 20157 Milan, Italy
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, 20157 Milan, Italy
| | - Dejan Radovanovic
- Division of Respiratory Diseases, Luigi Sacco University Hospital, ASST Fatebenefratelli-Sacco, 20157 Milan, Italy
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, 20157 Milan, Italy
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