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1. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

Author

Paul Trichelair, Kathryn Schutte, Etienne Bendjebbar, Thomas Clozel, Nathalie Lassau, Sagar Verma, Gilles Wainrib, Simon Jégou, Remy Dubois, Frank Chemouni, Gabriel Garcia, Yingping Li, Annabelle Stoclin, Samer Soliman, Imad Bousaid, Paul Jehanno, Fabien Brulport, Hugo Gortais, Olivier Meyrignac, Jocelyn Dachary, Marie-Pauline Talabard, Emilie Chouzenoux, Ana Neacsu, Matthieu Terris, Fabrice Barlesi, Matthieu Devilder, Mikael Azoulay, Hugues Talbot, Kavya Gupta, Paul Herent, Olivier Dehaene, Franck Griscelli, Jean-Baptiste Schiratti, Michael G. B. Blum, Adrian Gonzalez, Jean-Christophe Pesquet, Nicolas Tetelboum, Corinne Balleyguier, Nicolas Loiseau, Marie-France Bellin, Samy Ammari, Elodie Pronier, Tasnim Dardouri, Emmanuel Planchet, Yannick Boursin, Meriem Sefta, Jean-Philippe Lamarque, Mansouria Merad, Unité BioMaps (BIOMAPS), Service Hospitalier Frédéric Joliot (SHFJ), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Département d'imagerie médicale [Gustave Roussy], Institut Gustave Roussy (IGR), OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Département de Radiologie [AP-HP Hôpital Bicêtre], AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Owkin, Inc. [New York, NY, États-Unis], Département interdisciplinaire d’organisation des parcours patients (DIOPP), Département de biologie et pathologie médicales [Gustave Roussy], Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, Heriot-Watt University [Edinburgh] (HWU), Direction de la Transformation Numérique et des Systèmes d’Information, Owkin France, Département de médecine oncologique [Gustave Roussy], LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay (BIOMAPS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
medicine.medical_specialty, Multivariate analysis, Clinical variables, Coronavirus disease 2019 (COVID-19), Science, MEDLINE, General Physics and Astronomy, macromolecular substances, Models, Biological, Severity of Illness Index, Article, General Biochemistry, Genetics and Molecular Biology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Text mining, Artificial Intelligence, Intensive care, Radiologists, Machine learning, Severity of illness, Humans, Medicine, 030212 general & internal medicine, Multidisciplinary, business.industry, Deep learning, COVID-19, General Chemistry, Prognosis, 3. Good health, Risk factors, Viral infection, Multivariate Analysis, Emergency medicine, Neural Networks, Computer, Artificial intelligence, Tomography, X-Ray Computed, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach., The SARS-COV-2 pandemic has put pressure on intensive care units, so that predicting severe deterioration early is a priority. Here, the authors develop a multimodal severity score including clinical and imaging features that has significantly improved prognostic performance in two validation datasets compared to previous scores.

Published
2021
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2. Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment

Author

Paul Herent, Olivier Ernst, Nathalie Lassau, Alain Luciani, M. Cadi, Maud Creze, Jean-Baptiste Schiratti, Paul Jehanno, Olivier Lucidarme, Simon Jégou, Nathan Banaste, Frederic Pigneur, Alexandre Sadate, Alex Dallongeville, Paul Blanc-Durand, Imad Bousaid, Y. Benaceur, A. Rouchaud, Kathryn Schutte, Service de médecine nucléaire [Créteil], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), The Lymphoma Academic Research Organisation [Lyon] (LYSARC), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Owkin France, Service de Radiologie [Mondor], Service de Radiologie [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Radiologie et Imagerie Musculo-Squelettique [CHU Lille], Hôpital Claude Huriez [Lille], CHU Lille-CHU Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Hospices Civils de Lyon (HCL), Unité BioMaps (BIOMAPS), Service Hospitalier Frédéric Joliot (SHFJ), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Centre hospitalier Saint-Joseph [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Léon Bérard [Lyon], Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Institut Gustave Roussy (IGR), Direction de la Transformation Numérique et des Systèmes d’Information, Direction de la recherche [Gustave Roussy], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Université de Lille, LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay (BIOMAPS), CCSD, Accord Elsevier, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-IFR10, Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Hôpital Henri Mondor-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Sarcopenia, Similarity (geometry), Mean squared error, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, X-ray computed, Convolutional neural network, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Segmentation, Convolutional neural networks (CNN), Tomography, Abdominal Muscles, Ground truth, Muscular body bass, Radiological and Ultrasound Technology, business.industry, Deep learning, Pattern recognition, General Medicine, 030220 oncology & carcinogenesis, Test set, Neural Networks, Computer, Artificial intelligence, Tomography, X-Ray Computed, business, Algorithms
Abstract

International audience; Purpose :The purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra.Materials and methods :An ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference.Results :A total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm2 respectively.Conclusion :Deep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow.

Published
2020
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3. Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI

Author

B. Bresson, N. Poussange, M. Majer, Marc Zins, D. Guenoun, Olivier Hauger, S. Si-Mohamed, D. Istrati, Théo Estienne, M. Azoulay, S. Molière, Nathalie Lassau, E. Jehanno, C. Balleyguier, Caroline Caramella, A. Bergère, M. Boisserie, J. Behr, F. Dubrulle, J.-F. Meder, François Cornelis, E. Poncelet, A. Paisant, Raphaëlle Renard-Penna, N. Peyron Faure, H. Cauliez, Caroline Malhaire, T. Caramella, A. Perrey, P. de Vomecourt, François Bidault, C. Bordonne, S. Montagne, Alain Luciani, S. Caius Giurca, G. Garcia, M. Faivre-Pierre, Nicolas Amoretti, F. Desmots, Anne Cotten, M. Abitbol, V. Herreros, Aurélie Jalaguier-Coudray, Olivier Rouvière, J.-F. Budzik, J. Cagnol, Laure Fournier, Valérie Juhan, M. Faruch, C. Cyteval, T. Jacques, J. Bocquet, R. Lotte, T. Willaume, J.-L. Drape, S. Brunelle, A. Blum, M. Garetier, L. Di Marco, F. Pigneur, Institut Gustave Roussy (IGR), Imagerie multimodale en cancérologie. (IR4M/U8081 éq.3), Institut Gustave Roussy (IGR)-Imagerie par Résonance Magnétique Médicale et Multi-Modalités (IR4M), Université Paris-Sud - Paris 11 (UP11)-Hôpital Bicêtre-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Bicêtre-Centre National de la Recherche Scientifique (CNRS), Direction de la recherche [Gustave Roussy], Radiothérapie Moléculaire et Innovation Thérapeutique (RaMo-IT), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Direction de la Transformation Numérique et des Systèmes d’Information, Mathématiques et Informatique pour la Complexité et les Systèmes (MICS), CentraleSupélec-Université Paris-Saclay, Comité de Cancérologie (CCAFU), Association Française d'Urologie, Département de radiothérapie [Gustave Roussy], Service de Radiologie et Imagerie Musculosquelettique, Centre de Consultations et d’Imagerie de l’Appareil Locomoteur, Service de Radiologie (LILLE - Radio), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Service de radiologie [CHRU Besancon], Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), Clinique du sport de Bordeaux-Mérignac, Clinique Saint-Jean - Toulon, Polytech'Paris - Sorbonne Université, Sorbonne Université (SU), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Centre Hospitalier Universitaire de Nice (CHU Nice), inconnu temporaire UPEMLV, Inconnu, Service de radiologie et imagerie médicale [Rennes] = Radiology [Rennes], CHU Pontchaillou [Rennes], Application des ultrasons à la thérapie (LabTAU), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL), Centre de résonance magnétique des systèmes biologiques (CRMSB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Hôpital d'Instruction des Armées Clermont Tonnerre, Service de Santé des Armées, Hôpital Henri Mondor, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Department of Radiology, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Université de Lyon, Hôpital Cochin [AP-HP], Service Imagerie de la femme, CH de Valenciennes, Hôpital Hôtel-Dieu [Paris], Centre Hospitalier de Lens, Université catholique de Lille (UCL), Institut Bergonié [Bordeaux], UNICANCER, Hôpital de Hautepierre [Strasbourg], Clinique du Val d'Ouest, Centre d’ Imagerie du Chinonais, Hôpital Européen [Fondation Ambroise Paré - Marseille], Institut du Sein, Service de Génétique Médicale [CHU Clermont-Ferrand], CHU Estaing [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand, Service de Pathologie Infectieuse et Tropicale [HIA Laveran, Marseille], Hôpital d'instruction des armées Laveran, Clinique du Pont Saint-Vaast, Douai, Centre d'Etudes et de Recherche Thérapeutique en Ophtalmologie (CERTO), Association RETINA France, Partenaires INRAE-Partenaires INRAE, Centre méditerranéen de médecine moléculaire (C3M), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA), Département de Radiologie, Armentières (59), Hôpital Sainte-Marguerite [CHU - APHM] (Hôpitaux Sud ), Institut des Sciences du Mouvement Etienne Jules Marey (ISM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Sciences pour l'environnement (SPE), Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS), Centre hospitalier Saint-Joseph [Paris], Service de neuroradiologie [Paris], Hôpital Sainte-Anne, Marrow Adiposity & Bone Lab - Adiposité Médullaire et Os - ULR 4490 (MABLab (ex-pmoi)), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Service de Radiologie Viscèrale, CHRU de Jean-Minjoz, CHU Toulouse [Toulouse], Service de radiologie et imagerie médicale [Rennes], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon]-Institut National de la Santé et de la Recherche Médicale (INSERM), Résonance magnétique des systèmes biologiques (RMSB), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS), CHU Clermont-Ferrand, Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP), Groupe hospitalier Paris Saint-Joseph - Hôpital, Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CCSD, Accord Elsevier, Imagerie par Résonance Magnétique Médicale et Multi-Modalités (IR4M), Centre National de la Recherche Scientifique (CNRS)-Hôpital Bicêtre-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Hôpital Bicêtre-Université Paris-Sud - Paris 11 (UP11)-Institut Gustave Roussy (IGR), and Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)

Subjects
Kidney Cortex, [SPI] Engineering Sciences [physics], Interprofessional Relations, education, Meniscal tears, Datasets as Topic, Computed tomography, Breast Neoplasms, Artificial intelligence (AI), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, [SPI]Engineering Sciences [physics], 0302 clinical medicine, Multidisciplinary approach, Artificial Intelligence, Ultrasound, Medicine, Data Protection Act 1998, Humans, Radiology, Nuclear Medicine and imaging, Neoplasm Invasiveness, Thyroid Neoplasms, Magnetic resonance imaging (MRI), Computer Security, Ultrasonography, Modalities, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Communication, Liver Neoplasms, Computed Tomography (CT), Deep learning, General Medicine, Magnetic Resonance Imaging, Tibial Meniscus Injuries, 030220 oncology & carcinogenesis, General Data Protection Regulation, Thyroid Cartilage, Artificial intelligence, business, Tomography, X-Ray Computed
Abstract

Summary Purpose The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ultrasound, computed tomography (CT) and MRI patient images; (2) build a network including radiologists, researchers, start-ups, large companies, and students from engineering schools, and; (3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018. Materials and methods Relevant clinical questions were chosen by the Societe Francaise de Radiologie. The challenge was designed to respect all French ethical and data protection constraints. Multidisciplinary teams with at least one radiologist, one engineering student, and a company and/or research lab were gathered using different networks, and clinical databases were created accordingly. Results Five challenges were launched: detection of meniscal tears on MRI, segmentation of renal cortex on CT, detection and characterization of liver lesions on ultrasound, detection of breast lesions on MRI, and characterization of thyroid cartilage lesions on CT. A total of 5,170 images within 4 months were provided for the challenge by 46 radiology services. Twenty-six multidisciplinary teams with 181 contestants worked for one month on the challenges. Three challenges, meniscal tears, renal cortex, and liver lesions, resulted in an accuracy > 90%. The fourth challenge (breast) reached 82% and the lastone (thyroid) 70%. Conclusion Theses five challenges were able to gather a large community of radiologists, engineers, researchers, and companies in a very short period of time. The accurate results of three of the five modalities suggest that artificial intelligence is a promising tool in these radiology modalities.

Published
2019
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4. Three artificial intelligence data challenges based on CT and ultrasound.

Author

Lassau N, Bousaid I, Chouzenoux E, Verdon A, Balleyguier C, Bidault F, Mousseaux E, Harguem-Zayani S, Gaillandre L, Bensalah Z, Doutriaux-Dumoulin I, Monroc M, Haquin A, Ceugnart L, Bachelle F, Charlot M, Thomassin-Naggara I, Fourquet T, Dapvril H, Orabona J, Chamming's F, El Haik M, Zhang-Yin J, Guillot MS, Ohana M, Caramella T, Diascorn Y, Airaud JY, Cuingnet P, Gencer U, Lawrance L, Luciani A, Cotten A, and Meder JF

Subjects
Humans, Radiologists, Ultrasonography, Artificial Intelligence, Tomography, X-Ray Computed
Abstract

Purpose: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers., Materials and Methods: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams. The objectives were proposed by the different organizations depending on their core areas of expertise. A dedicated platform was used to upload the medical image data, to automatically anonymize the uploaded data., Results: Three challenges were proposed including classification of benign or malignant breast nodules on ultrasound examinations, detection and contouring of pathological neck lymph nodes from cervical CT examinations and classification of calcium score on coronary calcifications from thoracic CT examinations. A total of 2076 medical examinations were included in the database for the three challenges, in three months, by 18 different centers, of which 12% were excluded. The 39 participants were divided into six multidisciplinary teams among which the coronary calcification score challenge was solved with a concordance index > 95%, and the other two with scores of 67% (breast nodule classification) and 63% (neck lymph node calcifications)., (Published by Elsevier Masson SAS.)

Published
2021
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5. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

Author

Lassau N, Ammari S, Chouzenoux E, Gortais H, Herent P, Devilder M, Soliman S, Meyrignac O, Talabard MP, Lamarque JP, Dubois R, Loiseau N, Trichelair P, Bendjebbar E, Garcia G, Balleyguier C, Merad M, Stoclin A, Jegou S, Griscelli F, Tetelboum N, Li Y, Verma S, Terris M, Dardouri T, Gupta K, Neacsu A, Chemouni F, Sefta M, Jehanno P, Bousaid I, Boursin Y, Planchet E, Azoulay M, Dachary J, Brulport F, Gonzalez A, Dehaene O, Schiratti JB, Schutte K, Pesquet JC, Talbot H, Pronier E, Wainrib G, Clozel T, Barlesi F, Bellin MF, and Blum MGB

Subjects
Artificial Intelligence, COVID-19 classification, Humans, Models, Biological, Multivariate Analysis, Prognosis, Radiologists, Severity of Illness Index, COVID-19 diagnosis, COVID-19 physiopathology, Deep Learning, Neural Networks, Computer, Tomography, X-Ray Computed methods
Abstract

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

Published
2021
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6. Three artificial intelligence data challenges based on CT and MRI.

Author

Lassau N, Bousaid I, Chouzenoux E, Lamarque JP, Charmettant B, Azoulay M, Cotton F, Khalil A, Lucidarme O, Pigneur F, Benaceur Y, Sadate A, Lederlin M, Laurent F, Chassagnon G, Ernst O, Ferreti G, Diascorn Y, Brillet PY, Creze M, Cassagnes L, Caramella C, Loubet A, Dallongeville A, Abassebay N, Ohana M, Banaste N, Cadi M, Behr J, Boussel L, Fournier L, Zins M, Beregi JP, Luciani A, Cotten A, and Meder JF

Subjects
Humans, Radiologists, Artificial Intelligence, Magnetic Resonance Imaging, Tomography, X-Ray Computed
Abstract

Purpose: The second edition of the artificial intelligence (AI) data challenge was organized by the French Society of Radiology with the aim to: (i), work on relevant public health issues; (ii), build large, multicentre, high quality databases; and (iii), include three-dimensional (3D) information and prognostic questions., Materials and Methods: Relevant clinical questions were proposed by French subspecialty colleges of radiology. Their feasibility was assessed by experts in the field of AI. A dedicated platform was set up for inclusion centers to safely upload their anonymized examinations in compliance with general data protection regulation. The quality of the database was checked by experts weekly with annotations performed by radiologists. Multidisciplinary teams competed between September 11 th and October 13 th 2019., Results: Three questions were selected using different imaging and evaluation modalities, including: pulmonary nodule detection and classification from 3D computed tomography (CT), prediction of expanded disability status scale in multiple sclerosis using 3D magnetic resonance imaging (MRI) and segmentation of muscular surface for sarcopenia estimation from two-dimensional CT. A total of 4347 examinations were gathered of which only 6% were excluded. Three independent databases from 24 individual centers were created. A total of 143 participants were split into 20 multidisciplinary teams., Conclusion: Three data challenges with over 1200 general data protection regulation compliant CT or MRI examinations each were organized. Future challenges should be made with more complex situations combining histopathological or genetic information to resemble real life situations faced by radiologists in routine practice., (Copyright © 2020 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Published
2020
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7. Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI.

Author

Lassau N, Estienne T, de Vomecourt P, Azoulay M, Cagnol J, Garcia G, Majer M, Jehanno E, Renard-Penna R, Balleyguier C, Bidault F, Caramella C, Jacques T, Dubrulle F, Behr J, Poussange N, Bocquet J, Montagne S, Cornelis F, Faruch M, Bresson B, Brunelle S, Jalaguier-Coudray A, Amoretti N, Blum A, Paisant A, Herreros V, Rouviere O, Si-Mohamed S, Di Marco L, Hauger O, Garetier M, Pigneur F, Bergère A, Cyteval C, Fournier L, Malhaire C, Drape JL, Poncelet E, Bordonne C, Cauliez H, Budzik JF, Boisserie M, Willaume T, Molière S, Peyron Faure N, Caius Giurca S, Juhan V, Caramella T, Perrey A, Desmots F, Faivre-Pierre M, Abitbol M, Lotte R, Istrati D, Guenoun D, Luciani A, Zins M, Meder JF, and Cotten A

Subjects
Breast Neoplasms diagnostic imaging, Communication, Computer Security, Humans, Interprofessional Relations, Kidney Cortex diagnostic imaging, Liver Neoplasms diagnostic imaging, Magnetic Resonance Imaging, Neoplasm Invasiveness diagnostic imaging, Thyroid Cartilage diagnostic imaging, Thyroid Neoplasms diagnostic imaging, Thyroid Neoplasms pathology, Tibial Meniscus Injuries diagnostic imaging, Tomography, X-Ray Computed, Ultrasonography, Artificial Intelligence, Datasets as Topic
Abstract

Purpose: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ultrasound, computed tomography (CT) and MRI patient images; (2) build a network including radiologists, researchers, start-ups, large companies, and students from engineering schools, and; (3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018., Materials and Methods: Relevant clinical questions were chosen by the Société Francaise de Radiologie. The challenge was designed to respect all French ethical and data protection constraints. Multidisciplinary teams with at least one radiologist, one engineering student, and a company and/or research lab were gathered using different networks, and clinical databases were created accordingly., Results: Five challenges were launched: detection of meniscal tears on MRI, segmentation of renal cortex on CT, detection and characterization of liver lesions on ultrasound, detection of breast lesions on MRI, and characterization of thyroid cartilage lesions on CT. A total of 5,170 images within 4 months were provided for the challenge by 46 radiology services. Twenty-six multidisciplinary teams with 181 contestants worked for one month on the challenges. Three challenges, meniscal tears, renal cortex, and liver lesions, resulted in an accuracy>90%. The fourth challenge (breast) reached 82% and the lastone (thyroid) 70%., Conclusion: Theses five challenges were able to gather a large community of radiologists, engineers, researchers, and companies in a very short period of time. The accurate results of three of the five modalities suggest that artificial intelligence is a promising tool in these radiology modalities., (Copyright © 2019 Soci showét showé françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Published
2019
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