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1. 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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2. Patient and public involvement is suboptimal in randomized controlled trials addressing a chronic condition.

Author

Husson M, Dechartres A, Ramdjee B, Diverres M, Zejli T, L'Hénaff M, Jehanno E, Tubach F, and Caille A

Subjects
Humans, Randomized Controlled Trials as Topic, Chronic Disease, United Kingdom, Patient Participation, Outcome Assessment, Health Care
Abstract

Objectives: To describe patient and public involvement (PPI) in randomized controlled trials (RCTs) addressing a chronic condition and to analyze whether PPI is associated with trial characteristics., Study Design and Setting: We used PubMed search to identify RCTs addressing a chronic condition and published in journals with a mandatory PPI statement., Results: Across 101 RCTs; 40 (40%) reported PPI at any stage of the research process. PPI contribution occurred mostly at the design stage of RCTs (n = 36), especially for assessing the burden of the intervention (n = 24), and at the conduct stage (n = 21), with the elaboration of communication materials (n = 14). Less than one-third (13/40) of RCTs included PPI in the development or choice of outcome measures. As compared with non-PPI RCTs, PPI RCTs more frequently were published in The BMJ, had a corresponding author from the United Kingdom, reported a public funding source, had a higher inclusion rate, used usual care as a control and evaluated a digital intervention. PPI RCTs were associated with less frequent use of placebo as a control group., Conclusion: Our results underline that PPI is not uncommon in RCTs of chronic conditions but infrequently occurred at a key stage. Yet, the engagement of patients as a real partner in RCTs of chronic conditions should be enhanced., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Published
2023
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3. Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model.

Author

Taghiakbari M, Hamidi Ghalehjegh S, Jehanno E, Berthier T, di Jorio L, Ghadakzadeh S, Barkun A, Takla M, Bouin M, Deslandres E, Bouchard S, Sidani S, Bengio Y, and von Renteln Md D

Abstract

Background and Aims: Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps., Methods: We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks., Results: After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%)., Conclusion: This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality., Competing Interests: MT, MT, MB, ED, SB, and SS do not have any conflicts of interest relevant to this paper to disclose. AB is a co-awardee in funded research projects with Imagia Canexia Health Inc. and a consultant for Medtronic Inc. and A.I. VALI Inc. DvR is supported by a “Fonds de Recherche du Québec Santé” career development award. He has also received research funding from ERBE, Ventage, Pendopharm, and Pentax, and is a consultant for Boston Scientific and Pendopharm. SHG, EJ, TB, SG, and LdJ are full-time and direct employees of the Imagia Canexia Health Inc. at 6650 Saint-Urbain Street, Suite 100, Montréal, Québec H2S 3G9, CANADA., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Canadian Association of Gastroenterology.)

Published
2023
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4. 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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