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2. More Than 50 Percent Reduction in LDL Cholesterol in Patients With Target LDL <70 mg/dL After a Stroke

Author

Amarenco, Pierre, Lavallée, Philippa C., Kim, Jong S., Labreuche, Julien, Charles, Hugo, Giroud, Maurice, Lee, Byung-Chul, Mahagne, Marie-Hélène, Meseguer, Elena, Nighoghossian, Norbert, Steg, Philippe Gabriel, Vicaut, Éric, Bruckert, Eric, Kim, Jong S, Touboul, Pierre-Jean, Leys, Didier, Béjot, Yannick, Pico, Fernando, Touzé, Emmanuel, Ducrocq, Gregory, Abtan, Jérémy, Varenne, Olivier, Touboul, Pierre-Jean, Kemmel, Agnes, Syana, Fausta, Ledra, Manele, Nagasara, Tharani, Ledjeroud, Mervette, Samia, Bahous, Hadia, Hafirassou, Hazare, Benyoub, Jaghouni, Ikrame El, Yelles, Nessima, Zemouri, Sofia, Ladjeroud, Mervette, Kerai, Salim, In, YunJeong, Hobeanu, Cristina, Guidoux, Celine, Cabrejo, Lucie, Lapergue, Bertrand, Sabben, Candice, Gonzalez-Valcarcel, Jaime, Rigual, Ricardo, Sirimarco, Gaia, Martin-Bechet, Anna, Viedma, Elena, Avram, Ioan, Samson, Yves, Rosso, Charlotte, Crozier, Sophie, Leder, Sara, Léger, Anne, Deltour, Sandrine, Mutlu, Gurkan, Yger, Marion, Zavanone, Chiara, Baronnet, Flore, Pires, Christine, Lapergue, Bertrand, Wang, Adrien, Evrard, Serge, Tchikviladze, Maya, Bourdain, Frédéric, Lopez, Delphine, Pico, Fernando, de la Tour, Laetitia Bayon, Chadenat, Marie-Laure, Duong, Duc Long, Genty, Solène, Hirel, Catherine, Mutlu, Gurkan, Nifle, Chantal, Servan, Jérôme, Stanciu, Daniela, Sudacevschi, Veronica, Tir, Mélissa, Troussière, Anne-Cécile, Yeung, Jennifer, Zeghoudi, Anne-Céline, Tidafi-Bayou, Ikram, Lachaud, Sylvain, Cho, Tae-Hee, Mechtouff, Laura, Ritzenthaller, Thomas, Derex, Laurent, Albanesi, Carlo, Ong, Elodie, Benoit, Amandine, Berhoune, Nadia, Felix, Sandra, Esteban-Mader, Maud, Sibon, Igor, Kazadi, Annabelle, Rouanet, François, Renou, Pauline, Debruxelles, Sabrina, Poli, Mathilde, Sagnier, Sharmila, Mas, Jean-Louis, Domigo, Valérie, Lamy, Catherine, Bodiguel, Eric, Grimaud, Jérôme, Bohotin, Valentin, Obadia, Michael, Sabben, Candice, Morvan, Erwan, Rodier, Gilles, Vadot, Wilfried, Hénon, Hilde, Cordonnier, Charlotte, Dumont, Frédéric, Bodenant, Marie, Lucas, Christian, Moulin, Solène, Dequatre, Nelly, Alamowitch, Sonia, Muresan, Jean-Paul, Drouet, Thomas, Gallea, Magalie, Dalloz, Marie-Amélie, Delorme, Stephen, Yger, Marion, Béjot, Yannick, Loisel, Philippe, Bonnin, Carine, Bernigal, Virginie, Osseby, Guy Victor, Hervieu-BègueMarsac, Marie, Garnier, Pierre, Accassat, Sandrine, Epinat, Magali, Varvat, Jérôme, Marinescu, Doïna, Triquenot-Bagan, Aude, Ozkul- Wermester, Ozlem, Philippeau, Frédéric, Olaru, Angel, Vieillart, Anne, Lannuzel, Annie, Demoly, Alice, Wolff, Valérie, Diaconu, Mihaela, Bataillard, Marc, Montoro, Francisco Macian, Faugeras, Frédéric, Gimenez, Laeticia, Abdallah-Lebeau, Françoise, Timsit, Serge, Viakhireva-Dovganyuk, Irina, Tirel-Badets, Anne, Merrien, François-Mathias, Goas, Philippe, Rouhart, François, Jourdain, Aurore, Guillon, Benoit, Hérissson, Fanny, Sevin-Allouet, Mathieu, Nasr, Nathalie, Olivot, Jean-Marc, Lecluse, Alderic, Marc, Guillaume, Touzé, Emmanuel, de la Sayette, Vincent, Apoil, Marion, Lin, Li, Cogez, Julien, Guettier, Sophie, Godefroy, Olivier, Lamy, Chantal, Bugnicourt, Jean-Marc, Taurin, Grégory, Mérienne, Marc, Gere, Julien, Chessak, Anne-Marie, Habet, Tarik, Ferrier, Anna, Bourgois, Nathalie, Minier, Dominique, Caillier-Minier, Marie, Contégal- Callier, Fabienne, Vion, Philippe, Vaschalde, Yvan, Amrani, Mohammed El, Emilie, Zuber, Mathieu, Bruandet, Marie, Join- Lambert, Claire, Garcia, Pierre-Yves, Serre, Isabelle, Faucheux, Jean-Marc, Radji, Fatia, Leca-Radu, Elena, Debroucker, Thomas, Cumurcuc, Rodica, Cakmak, Serkan, Peysson, Stéphane, Ellie, Emmanuel, Bernady, Patricia, Moulin, Thierry, Montiel, Paola, Revenco, Eugeniu, Decavel, Pierre, Medeiros, Elisabeth, Bouveret, Myriam, Louchart, Pierre, Vaduva, Claudia, Couvreur, Grégory, Sartori, Eric, Carpentier, Alnajar, Levasseur, Michèle, Louchart, Pierre, Faucheux, Jean-Marc, Neau, Philippe, Vandamme, Xavier, Meresse, Isabelle, Stantescu, Bataillard, Marc, Ozsancak, Canan, Beauvais, Katell, Auzou, Pascal, Amevigbe, Joséphine, Vuillemet, Francis, Dugay-Arentz, Marie-Hélène, Carelli, Gabriela, Martinez, Mikel, Maillet-Vioud, Marcel, Escaillas, Jean-Pierre, Chapuis, Stéphane, Tardy, Jean, Manchon, Eric, Varnet, Olivier, Kim, Yong-Jae, Chang, Yoonkyung, Song, Tae-Jin, Kim, Jong Sung, Han, Jung-Hoon, Noh, Kyung Chul, Lee, Eun-Jae, Kang, Dong-Wha, Kwon, Sun Uck, Kwon, Boseoung, Park, Seongho, Lee, Dongwhane, Kwon, Hyuk Sung, Jeong, Daeun, Lee, MinHwan, Kim, Joonggoo, Lee, Hanbin, Nam, Hyo Jung, Lee, Sang Hun, Kim, Bum Joon, Cha, Jae-kwan, Kim, DaeHyun, Kim, Rae Young, Sohn, Sang Wuk, Shim, Dong-Hyun, Lee, Hyungjin, Nah, Hyun-Wook, Sung, Sang Min, Lee, Kyung Bok, Yoon Lee, Jeong, Yoon, Jee Eun, Kim, Eung-Gyu, Seo, Jung Hwa, Kim, Yong-Won, Hwang, Yangha, Park, Man Seok, Kim, Joon-Tae, Choi, Kang-Ho, Nam, Hyo Suk, Heo, Ji Hoe, Kim, Young Dae, Hwang, In Gun, Park, Hyung Jong, Kim, Kyoung Sub, Baek, Jang Hyun, Song, Dong Beom, Yoo, Joon Sang, Park, Jong-Moo, Kwon, Ohyun, Lee, Woong-Woo, Lee, Jung-Ju, Kang, Kyusik, Kim, Byung Kun, Lim, Jae-Sung, Oh, Mi Sun, Yu, Kyung-Ho, Hong, Bora, Jang, Mihoon, Jang, Seyoung, Jin, Jung Eun, Kim, Jei, Jeong, Hye Seon, Hong, Keun Sik, Park, Hong Kyun, Cho, Yong Jin, Bang, Oh Young, Seo, Woo-Keun, and Chung, Jongwon

Published
2023
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4. Intravenous thrombolysis and thrombectomy decisions in acute ischemic stroke: An interrater and intrarater agreement study

Author

Ducroux, C., Fahed, R., Khoury, N. N., Gevry, G., Kalsoum, E., Labeyrie, M-A, Ziegler, D., Sauve, C., Chagnon, M., Darsaut, T. E., Raymond, J., Niclot, Philippe, Ameri, Alain, Hodel, Jerome, Gentric, Jean-Christophe, Merrien, Francois-Mathias, Jourdain, Aurore, Rouhart, Francois, Viakhireva, Irina, Timsit, Serge, Clarencon, Frederic, Pistocchi, Silvia, Rosso, Charlotte, Zavanone, Chiara, Houdart, Emmanuel, Labeyrie, Marc-Antoine, Reiner, Peggy, Gory, Benjamin, Riva, Roberto, Nighoghossian, Norbert, Cho, Tae-Hee, Derex, Laurent, Eugene, Francois, Ferré, Jean-Christophe, Le Bras, Anthony, Vannier, Stephane, Lassalle, Maria, Chivot, Cyril, Arnoux, Audrey, Alla, Philippe, Veyrieres, Jean-Brice, Marnat, Gaultier, Berge, Jerome, Olindo, Stephane, Sibon, Igor, Lapergue, Bertrand, Wang, Adrien, Consoli, Arturo, Di Maria, Federico, Obadia, Michael, Sabben, Candice, Mazighi, Mikael, Redjem, Hocine, Piotin, Michel, Boisseau, William, Razlog, Lilia, Pop, Raoul, Beaujeux, Remy, Mihoc, Dan, Richter, Sebastian, Manisor, Monica, Wolff, Valerie, Quenardelle, Veronique, Zinchenko, Lisa, Diaconu, Mihaela, Yger, Marion, Delorme, Stephen, Iosif, Christina, Moulin, Thierry, Zekri, Hatem, Lallement, Francois, Vercruysse, Olivier, Papagiannaki, Christanthi, Ozkul-Wermester, Ozlem, Dargazanli, Cyril, Costalat, Vincent, Gaillard, Nicolas, Arquizan, Caroline, Cayrecastel, Mireille, Bracard, Serge, Derelle, Anne-Laure, Richard, Sebastien, Humbertjean, Lisa, Herve, Yann, Favrole, Pascal, Le Coz, Patrick, de Brouker, Thomas, Force, Marie-Isabelle, Akono, Serge, Cakmak, Serkan, Florea, Alexandru, Lalu, Thibault, Taurin, Gregory, Duong, Duc Long, Chadenat, Marie-Laure, Pico, Fernando, Allibert, Remi, Evain, Sarah, Tassan, Philippe, Berthier, Eric, Ducrocq, Xavier, Rigal, Matthieu, Boulanger, Marion, Hôpital de la Fondation Ophtalmologique Adolphe de Rothschild [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal (UdeM), CHU Henri Mondor [Créteil], Service de Neuroradiologie [CHU Lariboisière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Département de Mathématiques et de statistique [UdeM- Montréal] (DMS), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CIC Brest, Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hôpital de la Cavale Blanche, Service de Neurologie [Brest], Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Génétique et Physiopathologie des Maladies Cérébro-Vasculaires (U1161 / UMR_S 1161), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), EFS-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Hôpital Lariboisière-Fernand-Widal [APHP], Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Hospices Civils de Lyon (HCL), Department of Stroke Medicine [Lyon], Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Hospices Civils de Lyon, Departement de Neurologie (HCL), Cerebrovascular Unit [Lyon], Hôpital neurologique et neurochirurgical Pierre Wertheimer [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Health Service and Performance Research (HESPER), Université de Lyon-Université de Lyon, Service de radiologie et imagerie médicale [Rennes] = Radiology [Rennes], CHU Pontchaillou [Rennes], Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département de Radiologie [CHU de Rennes], Université de Rennes (UR), Institut de Recherche Technologique b-com (IRT b-com), Service de Neurologie [CHU Rennes], Laboratoire de Psychologie et NeuroCognition (LPNC ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), CHU Bordeaux [Bordeaux], Centre Hospitalier Universitaire de Bordeaux (CHU de Bordeaux), Université de Bordeaux (UB), Hôpital Foch [Suresnes], CHU Rothschild [AP-HP], Fondation Ophtalmologique Adolphe de Rothschild [Paris], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), L'Institut hospitalo-universitaire de Strasbourg (IHU Strasbourg), Institut National de Recherche en Informatique et en Automatique (Inria)-l'Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD)-Les Hôpitaux Universitaires de Strasbourg (HUS)-La Fédération des Crédits Mutuels Centre Est (FCMCE)-L'Association pour la Recherche contre le Cancer (ARC)-La société Karl STORZ, Département de Neuroradiologie [Strasbourg], Les Hôpitaux Universitaires de Strasbourg (HUS), Mitochondrie, stress oxydant et protection musculaire (MSP), Université de Strasbourg (UNISTRA), CHU Saint-Antoine [AP-HP], Centre de Recherche Saint-Antoine (CRSA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Equipe de Recherche Médicale Appliquée (ERMA), Université de Limoges (UNILIM)-CHU Limoges-Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST FR CNRS 3503), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), Service de neurologie [Rouen], CHU Rouen, Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Département de Neuroradiologie[Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université de Montpellier (UM), Département de neuroradiologie diagnostique et thérapeutique [CHRU Nancy], Service de neurologie [CHRU Nancy], Service de Neurologie [Aix-en-Provence], Centre Hospitalier du Pays d'Aix, Neuroépidémiologie, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de radiologie [Vannes], Centre hospitalier Bretagne Atlantique (Morbihan) (CHBA), Centre hospitalier régional Metz-Thionville (CHR Metz-Thionville), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Troubles cognitifs dégénératifs et vasculaires - U 1171 (TCDV), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)

Subjects
medicine.medical_specialty, Consensus, medicine.medical_treatment, [SDV]Life Sciences [q-bio], Decision Making, Single Center, law.invention, Brain Ischemia, 03 medical and health sciences, 0302 clinical medicine, Combined treatment, Cohen's kappa, Randomized controlled trial, Fibrinolytic Agents, law, Medicine, Humans, Thrombolytic Therapy, 030212 general & internal medicine, Infusions, Intravenous, Acute ischemic stroke, Thrombectomy, business.industry, Reproducibility of Results, Thrombolysis, Mechanical thrombectomy, Stroke, Inter-rater reliability, Neurology, Acute Disease, Physical therapy, Administration, Intravenous, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

International audience; Purpose. - We aimed to assess agreement on intravenous tissue-plasminogen activator (IV tPA) and mechanical thrombectomy (MT) management decisions in acute ischemic stroke (AIS) patients. Secondary objectives were to assess agreement on Diffusion-Weighted-Imaging-Alberta-Stroke-Program-EArly-CT-Score (DWI-ASPECTS), and clinicians' willingness to recruit patients in a randomized controlled trial (RCT) comparing medical management with or without MT. Materials and Methods. - Studies assessing agreement of IV tPA and MT were systematically reviewed. An electronic portfolio of 41 AIS patients was sent to randomly selected providers at French stroke centers. Raters were asked 4 questions for each case: (1) What is the DWI-ASPECTS? (2) Would you perform IV tPA? (3) Would you perform MT? (4) Would you include the patient in a RCT comparing standard medical therapy with or without MT? Twenty responders were randomly selected to study intrarater agreement. Agreement was assessed using Fleiss' Kappa statistics. Results. - The review yielded two single center studies involving 2-5 raters, with various results. The electronic survey was answered by 86 physicians (60 vascular neurologists and 26 interventional neuroradiologists). The interrater agreement was moderate for IV tPA treatment decisions (kappa = 0.565 [0.420-0.680]), but only fair for MT (kappa = 0.383 [0.289-0.491]) and for combined treatment decisions (kappa = 0.399 [0.320-0.486]). The intrarater agreement was at least substantial for the majority of raters. The interrater agreement for DWI-ASPECTS was fair (kappa = 0.325 [0.276-0.387]). Physicians were willing to include a mean of 14 +/- 9 patients (33.1% +/- 21.7%) in a RCT. Conclusion. - Disagreements regarding the use of IVtPA or MT in the management of AIS patients remain frequent. Further trials are needed to resolve the numerous areas of uncertainty. (C) 2019 Elsevier Masson SAS. All rights reserved.

Published
2018
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5. Automated caries detection with smartphone color photography using machine learning.

Author

Duong, Duc Long, Kabir, Malitha Humayun, and Kuo, Rong Fu

Subjects
*DIAGNOSIS of dental caries, *SUPPORT vector machines, *DIGITAL image processing, *PREDICTIVE tests, *ACADEMIC medical centers, *SMARTPHONES, *MACHINE learning, *PHOTOGRAPHY, *AUTOMATION, *DESCRIPTIVE statistics, *STATISTICAL models, *DIGITAL diagnostic imaging
Abstract

Untreated caries is significant problem that affected billion people over the world. Therefore, the appropriate method and accuracy of caries detection in clinical decision-making in dental practices as well as in oral epidemiology or caries research, are required urgently. The aim of this study was to introduce a computational algorithm that can automate recognize carious lesions on tooth occlusal surfaces in smartphone images according to International Caries Detection and Assessment System (ICDAS). From a group of extracted teeth, 620 unrestored molars/premolars were photographed using smartphone. The obtained images were evaluated for caries diagnosis with the ICDAS II codes, and were labeled into three classes: "No Surface Change" (NSC); "Visually Non-Cavitated" (VNC); "Cavitated" (C). Then, a two steps detection scheme using Support Vector Machine (SVM) has been proposed: " C versus (VNC + NSC) " classification, and " VNC versus NSC " classification. The accuracy, sensitivity, and specificity of best model were 92.37%, 88.1%, and 96.6% for " C versus (VNC + NSC)," whereas they were 83.33%, 82.2%, and 66.7% for " VNC versus NSC." Although the proposed SVM system required further improvement and verification, with the data only imaged from the smartphone, it performed an auspicious potential for clinical diagnostics with reasonable accuracy and minimal cost. [ABSTRACT FROM AUTHOR]

Published
2021
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6. Prediction of Early Neurological Deterioration in Individuals With Minor Stroke and Large Vessel Occlusion Intended for Intravenous Thrombolysis Alone.

Author

Seners, Pierre, Ben Hassen, Wagih, Lapergue, Bertrand, Arquizan, Caroline, Heldner, Mirjam R., Henon, Hilde, Perrin, Claire, Strambo, Davide, Cottier, Jean-Philippe, Sablot, Denis, Girard Buttaz, Isabelle, Tamazyan, Ruben, Preterre, Cécile, Agius, Pierre, Laksiri, Nadia, Mechtouff, Laura, Béjot, Yannick, Duong, Duc-Long, Mounier-Vehier, François, and Mione, Gioia

Published
2021
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7. Effect of In-Hospital Remote Ischemic Perconditioning on Brain Infarction Growth and Clinical Outcomes in Patients With Acute Ischemic Stroke: The RESCUE BRAIN Randomized Clinical Trial.

Author

Pico, Fernando, Lapergue, Bertrand, Ferrigno, Marc, Rosso, Charlotte, Meseguer, Elena, Chadenat, Marie-Laure, Bourdain, Frederic, Obadia, Michael, Hirel, Catherine, Duong, Duc Long, Deltour, Sandrine, Aegerter, Philippe, Labreuche, Julien, Cattenoy, Amina, Smadja, Didier, Hosseini, Hassan, Guillon, Benoit, Wolff, Valérie, Samson, Yves, and Cordonnier, Charlotte

Published
2020
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8. MRI-Based Predictors of Hemorrhagic Transformation in Patients With Stroke Treated by Intravenous Thrombolysis.

Author

El Nawar, Rody, Yeung, Jennifer, Labreuche, Julien, Chadenat, Marie-Laure, Duong, Duc Long, De Malherbe, Maxime, Cordoliani, Yves-Sebastien, Lapergue, Bertrand, and Pico, Fernando

Subjects
CEREBRAL infarction, RECEIVER operating characteristic curves, DIFFUSION magnetic resonance imaging, STROKE patients, THROMBOLYTIC therapy
Abstract

Background: Clinical and biological risk factors for hemorrhagic transformation (HT) after intravenous thrombolysis (IT) have been well-established in several registries. The added value of magnetic resonance imaging (MRI) variables has been studied in small samples, and is controversial. We aimed to assess the added value of MRI variables in HT, beyond that of clinical and biological factors. Methods: We enrolled 474 consecutive patients with brain infarction treated by IT alone at our primary stroke center between January 2011 and August 2017. Baseline demographic, clinical, biological, and imaging characteristics were collected. MRI variables were: brain infarction volume in cm3; parenchymal fluid attenuated inversion recovery (FLAIR) hyperintensity; FLAIR hyperintense vessel signs; number of microbleeds; subcortical white matter hyperintensity; and thrombus length. Results: Overall, 301 patients were included out of 474 (64%). The main causes of exclusion were combined thrombectomy (n = 98) and no MRI before IT (n = 44). In the bivariate analysis, HT was significantly associated with the presence of more FLAIR hyperintense vessel signs, thrombus length (>8 mm), and larger brain infarction volume (diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) < 500 × 10−6 mm2/s). In the multivariable analysis, only brain infarction volume was significantly associated with HT. The discrimination value of the multivariable model, including both the DWI volume and the clinical model (area under the receiver operating characteristic curve, 0.80; 95% confidence interval 0.74–0.86), was improved significantly compared with the model based only on clinical variables (P = 0.012). Conclusions: Brain infarction volume on DWI was the only MRI variable that added value to clinico biological variables for predicting HT after IT. [ABSTRACT FROM AUTHOR]

Published
2019
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9. Efficacy of Endovascular Therapy in Acute Ischemic Stroke Depends on Age and Clinical Severity.

Author

Le Bouc, Raphaël, Clarençon, Frédéric, Meseguer, Elena, Lapergue, Bertrand, Consoli, Arturo, Turc, Guillaume, Naggara, Olivier, Duong, Duc Long, Servan, Jerome, Reiner, Peggy, Labeyrie, Marc Antoine, Fisselier, Mathieu, Blanc, Raphaël, Farhat, Wassim, Pires, Christine, Zuber, Mathieu, Obadia, Michael, Mazighi, Mikael, Pico, Fernando, and Mas, Jean-Louis

Published
2018
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10. Proof-of-Concept Study on an Automatic Computational System in Detecting and Classifying Occlusal Caries Lesions from Smartphone Color Images of Unrestored Extracted Teeth.

Author

Duong, Duc Long, Nguyen, Quoc Duy Nam, Tong, Minh Son, Vu, Manh Tuan, Lim, Joseph Dy, and Kuo, Rong Fu

Subjects
*SMARTPHONES, *DENTAL caries, *TOOTH sensitivity, *SUPPORT vector machines, *DENTAL arch, *ARTIFICIAL intelligence, *TEETH
Abstract

Dental caries has been considered the heaviest worldwide oral health burden affecting a significant proportion of the population. To prevent dental caries, an appropriate and accurate early detection method is demanded. This proof-of-concept study aims to develop a two-stage computational system that can detect early occlusal caries from smartphone color images of unrestored extracted teeth according to modified International Caries Detection and Assessment System (ICDAS) criteria (3 classes: Code 0; Code 1–2; Code 3–6): in the first stage, carious lesion areas were identified and extracted from sound tooth regions. Then, five characteristic features of these areas were intendedly selected and calculated to be inputted into the classification stage, where five classifiers (Support Vector Machine, Random Forests, K-Nearest Neighbors, Gradient Boosted Tree, Logistic Regression) were evaluated to determine the best one among them. On a set of 587 smartphone images of extracted teeth, our system achieved accuracy, sensitivity, and specificity that were 87.39%, 89.88%, and 68.86% in the detection stage when compared to modified visual and image-based ICDAS criteria. For the classification stage, the Support Vector Machine model was recorded as the best model with accuracy, sensitivity, and specificity at 88.76%, 92.31%, and 85.21%. As the first step in developing the technology, our present findings confirm the feasibility of using smartphone color images to employ Artificial Intelligence algorithms in caries detection. To improve the performance of the proposed system, there is a need for further development in both in vitro and in vivo modeling. Besides that, an applicable system for accurately taking intra-oral images that can capture entire dental arches including the occlusal surfaces of premolars and molars also needs to be developed. [ABSTRACT FROM AUTHOR]

Published
2021
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