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1. Gender balance in skeletal radiology: suffrage rather than suffering?

Author

Giraudo, Chiara, Rosskopf, Andrea B., Klauser, Andrea Sabine, Pillai, Janani K., Adriaensen, Miraude, Bazzocchi, Alberto, Becce, Fabio, Bielecki, Dennis K., Boesen, Mikael, Cotten, Anne, Dalili, Danoob, Eshed, Iris, Feydy, Antoine, Grainger, Andrew, Guglielmi, Giuseppe, Herregods, Nele, Isaac, Amanda, Ivanac, Gordana, Jans, Lennart, Kainberger, Franz, Laloo, Frederiek, Lecouvet, Frederic, Llopis, Eva, Maas, Mario, Mascarenhas, Vasco, Martinoli, Carlo, Papakonstantinou, Olympia, Reijniersee, Monique, Simoni, Paolo, van der Heijden, Rianne, Sudol-Szopinska, Iwona, and Rennie, Winston J.

Published
2024
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2. Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Author

Hennocq, Quentin, Willems, Marjolaine, Amiel, Jeanne, Arpin, Stéphanie, Attie-Bitach, Tania, Bongibault, Thomas, Bouygues, Thomas, Cormier-Daire, Valérie, Corre, Pierre, Dieterich, Klaus, Douillet, Maxime, Feydy, Jean, Galliani, Eva, Giuliano, Fabienne, Lyonnet, Stanislas, Picard, Arnaud, Porntaveetus, Thantrira, Rio, Marlène, Rouxel, Flavien, Shotelersuk, Vorasuk, Toutain, Annick, Yauy, Kevin, Geneviève, David, Khonsari, Roman H., and Garcelon, Nicolas

Published
2024
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7. Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Author

Quentin Hennocq, Marjolaine Willems, Jeanne Amiel, Stéphanie Arpin, Tania Attie-Bitach, Thomas Bongibault, Thomas Bouygues, Valérie Cormier-Daire, Pierre Corre, Klaus Dieterich, Maxime Douillet, Jean Feydy, Eva Galliani, Fabienne Giuliano, Stanislas Lyonnet, Arnaud Picard, Thantrira Porntaveetus, Marlène Rio, Flavien Rouxel, Vorasuk Shotelersuk, Annick Toutain, Kevin Yauy, David Geneviève, Roman H. Khonsari, and Nicolas Garcelon

Subjects
Medicine, Science
Abstract

Abstract The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p

Published
2024
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12. Prognostic factors of the synovial sarcoma of the extremities: imaging does matter

Author

Tordjman, Mickael, Honoré, Charles, Crombé, Amandine, Bouhamama, Amine, Feydy, Antoine, Dercle, Laurent, Haddag, Leila, Bouché, Pierre-Alban, Ngo, Carine, Le Cesne, Axel, Blay, Jean-Yves, Mir, Olivier, Brahmi, Mehdi, Martin, Charlotte, Karanian, Marie, Ammari, Samy, Kind, Michele, Audard, Virginie, Le Loarer, François, Rabiee, Behnam, Italiano, Antoine, Boudou-Rouquette, Pascaline, Biau, David, Balleyguier, Corinne, Larousserie, Frederique, Drapé, Jean-Luc, and Mihoubi, Fadila

Published
2023
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14. Performance of Sonoelastography for predicting malignancy in soft tissue

Author

Sylvain Bodard, Louis Lassalle, Frédérique Larousserie, Sylvain Guinebert, Thomas Hacquart, Raphael Campagna, David Biau, Noreddine Regnard, and Antoine Feydy

Subjects
Elastography, Soft-tissue masses, Fatty masses, Ultrasonography, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Separating benign from malignant soft-tissue masses often requires a biopsy. The objective of this study was to assess whether shear-wave elastography (SWE) helped to separate benign from malignant soft-tissue masses. Methods In 2015–2016, we prospectively included patients with soft-tissue masses deemed by our multidisciplinary sarcoma board to require a diagnostic biopsy. All patients underwent ultrasonography (US) followed by SWE to measure elasticity. We compared benign and malignant tumors, overall and after separating tumors with vs. without a fatty component. The biopsy findings, and surgical-specimen histology when available, served as the reference standard. Results We included 136 patients, 99 with non-fatty and 37 with fatty soft-tissue masses. Mean elasticity and tumor-to-fat elasticity ratio (T/F) values were significantly lower for the benign than the malignant soft-tissue masses in the overall cohort (30.9 vs. 50.0 kilopascals (kPa), P = 0.03; and 2.55 vs. 4.30, P = 0.046) and in the non-fatty subgroup (37.8 ± 31.9 vs. 58.9 ± 39.1 kPa, P = 0.049 and 2.89 ± 5.25 vs. 5.07 ± 5.41, P = 0.046). Data for fatty tumors were non relevant due to lack of conclusive results. By receiver operating characteristics curve analysis, a T/F cutoff of 3.5 had 46% sensitivity and 84% specificity for separating benign and malignant soft-tissue masses. Conclusions SWE had good specificity and poor sensitivity for separating benign from malignant soft-tissue masses.

Published
2022
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17. Intra-articular botulinum toxin A injection for painful base-of-thumb osteoarthritis: a double-blind, randomised, controlled, phase 3 trial (RHIBOT)

Author

Nguyen, Christelle, Abdoul, Hendy, Campagna, Raphaël, Guerini, Henri, Jilet, Léa, Bedin, Catherine, Chagny, Franck, Couraud, Gaëlle, Daste, Camille, Drapé, Jean-Luc, Fléchon, Rémy, Gil, Charlotte, Guérin, Corinne, Lefèvre-Colau, Marie-Martine, Poiraudeau, Serge, Randriamampandry, Estelle, Roren, Alexandra, Feydy, Antoine, and Rannou, François

Published
2022
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19. Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department.

Author

Herpe, Guillaume, Nelken, Helena, Vendeuvre, Tanguy, Guenezan, Jeremy, Giraud, Clement, Mimoz, Olivier, Feydy, Antoine, Tasu, Jean-Pierre, and Guillevin, Rémy

Subjects
ARTIFICIAL intelligence, LENGTH of stay in hospitals, ARTIFICIAL bones, HOSPITAL emergency services, BONE fractures
Abstract

Objectives: To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. Materials and Methods: A retrospective study was conducted in two phases—without AIS (Period 1: 1 January 2020–30 June 2020) and with AIS (Period 2: 1 January 2021–30 June 2021). Results: Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% (p = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (−1.8%, p = 0.99). The mean length of stay in the ED was reduced by 9 min (p = 0.03), and expert consultation rates decreased by 1% (p = 0.38). Conclusions: AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. Key Point: After AI software deployment, the rate of radiographic discrepancies decreased by 17% (p = 0.04) but this was not clinically relevant (−2%, p = 0.99). Length of patient stay in the emergency department decreased by 5% with AI (p = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Natural Course of Degenerative Changes of the Spine on Radiographs and Magnetic Resonance Imaging MRI in Axial Spondyloarthritis Followed for 10 Years in the DESIR Cohort.

Author

Vegas, L. Pina, Van Lunteren, M., Loeuille, D., Morizot, C., Newsum, E. A., Van Gaalen, F., Saraux, A. Alain, Feydy, A., and Reijnierse, M.

Subjects
MAGNETIC resonance imaging, SPONDYLOARTHROPATHIES, RADIOGRAPHS, BONE spurs, SPINE, SPINE osteoarthritis
Abstract

This article discusses the natural course of degenerative changes in the spine in patients with axial spondyloarthritis (axSpA) over a 10-year period. The study used radiographs and magnetic resonance imaging (MRI) to assess structural lesions in the spine. The results showed a high prevalence of degenerative changes in the spine, with the number of changes increasing over the 10-year period. The most frequent degenerative changes observed were loss of disk height, osteophytes, facet joint osteoarthritis, disk degeneration, high-intensity zone, Schmorl's node without edema, and disk protrusion. This study provides valuable insights into the progression of degenerative changes in the spine in axSpA patients. [Extracted from the article]

Published
2024
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30. Intervertebral disc therapies for non-specific chronic low back pain: a systematic review and meta-analysis

Author

Camille Daste, Stéphanie Laclau, Margaux Boisson, François Segretin, Antoine Feydy, Marie-Martine Lefèvre-Colau, François Rannou, and Christelle Nguyen

Subjects
Diseases of the musculoskeletal system, RC925-935
Abstract

Objectives: We aim to evaluate the benefits and harms of intervertebral disc therapies (IDTs) in people with non-specific chronic low back pain (NScLBP). Methods: We conducted a systematic review and meta-analysis of randomized trials of IDTs versus placebo interventions, active comparators or usual care. EMBASE, MEDLINE, CENTRAL and CINHAL databases and conference abstracts were searched from inception to June 2020. Two independent investigators extracted data. The primary outcome was LBP intensity at short term (1 week–3 months), intermediate term (3–6 months) and long term (after 6 months). Results: Of 18 eligible trials (among 1396 citations), five assessed glucocorticoids (GCs) IDTs and were included in a quantitative synthesis; 13 assessed other products including etanercept ( n = 2), tocilizumab ( n = 1), methylene blue ( n = 2), ozone ( n = 2), chymopapaine ( n = 1), glycerol ( n = 1), stem cells ( n = 1), platelet-rich plasma ( n = 1) and recombinant human growth and differentiation factor-5 ( n = 2), and were included in a narrative synthesis. Standardized mean differences (95% CI) for GC IDTs for LBP intensity and activity limitations were −1.33 (−2.34; −0.32) and −0.76 (−1.85; 0.34) at short term, −2.22 (−5.34; 0.90) and −1.60 (−3.51; 0.32) at intermediate term and −1.11 (−2.91; 0.70) and −0.63 (−1.68; 0.42) at long term, respectively. Odds ratios (95% CI) for serious and minor adverse events with GC IDTs were 1.09 (0.25; 4.65) and 0.97 (0.49; 1.91). Conclusion: GC IDTs are associated with a reduction in LBP intensity at short term in people with NScLBP. Positive effects are not sustained. IDTs have no effect on activity limitations. Our conclusions are limited by high heterogeneity and a limited methodological quality across studies. Registration PROSPERO: CRD42019106336.

Published
2021
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31. Overweight is associated to a better prognosis in metastatic colorectal cancer: A pooled analysis of FFCD trials

Author

Adenis, A., Alessio, A., Aouakli, A., Azzedine, A., Bedjaoui, A., Bidault, A., Blanchi, A., Botton, A., Cadier-Lagnes, A., Fatisse, A., Gagnaire, A., Gilbert, A., Gueye, A., Hollebecque, A., Lemaire, A., Mahamat, A., Marre, A., Patenotte, A., Rotenberg, A., Roussel, A., Thirot-Bidault, A., Votte, A., Weber, A., Zaanan, A., Dupont-Gossart, A.C., Villing, A.L., Queuniet, A.M., Coudert, B., Denis, B., Garcia, B., Lafforgue, B., Landi, B., Leduc, B., Linot, B., Paillot, B., Rhein, B., Winkfield, B., Barberis, C., Becht, C., Belletier, C., Berger, C., Bineau, C., Borel, C., Brezault, C., Buffet, C., Cornila, C., Couffon, C., De La Fouchardière, C., Giraud, C., Lecaille, C., Lepere, C., Lobry, C., Locher, C., Lombard-Bohas, C., Paoletti, C., Platini, C., Rebischung, C., Sarda, C., Vilain, C., Briac-Levaché, C., Auby, D., Baudet-Klepping, D., Bechade, D., Besson, D., Cleau, D., Festin, D., Gargot, D., Genet, D., Goldfain, D., Luet, D., Malka, D., Peré-Vergé, D., Pillon, D., Sevin-Robiche, D., Smith, D., Soubrane, D., Tougeron, D., Zylberait, D., Carola, E., Cuillerier, E., Dorval Danquechin, E., Echinard, E., Janssen, E., Maillard, E., Mitry, E., Norguet-Monnereau, E., Suc, E., Terrebonne, E., Zrihen, E., Pariente, E.A., Almaric, F., Audemar, F., Bonnetain, F., Desseigne, F., Dewaele, F., Di Fiore, F., Ghiringhelli, F., Husseini, F., Khemissa, F., Kikolski, F., Morvan, F., Petit-Laurent, F., Riot, F., Subtil, F., Zerouala-Boussaha, F., Caroli-Bosc, F.X., Boilleau-Jolimoy, G., Bordes, G., Cavaglione, G., Coulanjon, G., Deplanque, G., Gatineau-Saillant, G., Goujon, G., Medinger, G., Roquin, G., Brixi-Benmansour, H., Castanie, H., Lacroix, H., Maechel, H., Perrier, H., Salloum, H., Senellart, H., Baumgaertner, I., Cumin, I., Graber, I., Trouilloud, I., Boutin, J., Butel, J., Charneau, J., Cretin, J., Dauba, J., Deguiral, J., Egreteau, J., Ezenfis, J., Forestier, J., Goineau, J., Lacourt, J., Lafon, J., Martin, J., Meunier, J., Moreau, J., Provencal, J., Taieb, J., Thaury, J., Tuaillon, J., Vergniol, J., Villand, J., Vincent, J., Volet, J., Bachet, J.B., Barbare, J.C., Souquet, J.C., Grangé, J.D., Dor, J.F., Paitel, J.F., Jouve, J.L., Raoul, J.L., Cheula, J.M., Gornet, J.M., Sabate, J.M., Vantelon, J.M., Vaillant, J.N., Aucouturier, J.P., Barbieux, J.P., Herr, J.P., Lafargue, J.P., Lagasse, J.P., Latrive, J.P., Plachot, J.P., Ramain, J.P., Robin, J.P., Spano, J.P., Douillard, J.Y., Beerblock, K., Bouhier-Leporrier, K., Slimane Fawzi, K., Cany, L., Chone, L., Dahan, L., Gasnault, L., Rob, L., Stefani, L., Wander, L., Baconnier, M., Ben Abdelghani, M., Benchalal, M., Blasquez, M., Carreiro, M., Charbit, M., Combe, M., Duluc, M., Fayolle, M., Gignoux, M., Giovannini, M., Glikmanas, M., Mabro, M., Mignot, M., Mornet, M., Mousseau, M., Mozer, M., Pauwels, M., Pelletier, M., Porneuf, M., Ramdani, M., Schnee, M., Tissot, M., Zawadi, M., Clavero-Fabri, M.C., Gouttebel, M.C., Kaminsky, M.C., Galais, M.P., Abdelli, N., Barrière, N., Bouaria, N., Bouarioua, N., Delas, N., Gérardin, N., Hess-Laurens, N., Stremsdoerfer, N., Berthelet, O., Boulat, O., Capitain, O., Favre, O., Amoyal, P., Bergerault, P., Burtin, P., Cassan, P., Chatrenet, P., Chiappa, P., Claudé, P., Couzigou, P., Feydy, P., Follana, P., Geoffroy, P., Godeau, P., Hammel, P., Laplaige, P., Lehair, P., Martin, P., Novello, P., Pantioni, P., Pienkowski, P., Pouderoux, P., Prost, P., Ruszniewski, P., Souillac, P., Texereau, P., Thévenet, P., Haineaux, P.A., Benoit, R., Coriat, R., Lamy, R., Mackiewicz, R., Beorchia, S., Chaussade, S., Hiret, S., Jacquot, S., Lavau Denes, S., Montembault, S., Nahon, S., Nasca, S., Nguyen, S., Oddou-Lagraniere, S., Pesque-Penaud, S., Fratte, S.P., Chatellier, T., Mansourbakht, T., Morin, T., Walter, T., Boige, V., Bourgeois, V., Derias, V., Guérin-Meyer, V., Hautefeuille, V., Jestin Le Tallec, V., Lorgis, V., Quentin, V., Sebbagh, V., Veuillez, V., Adhoute, X., Coulaud, X., Becouarn, Y., Coscas, Y., Courouble, Y., Le Bricquir, Y., Molin, Y., Rinaldi, Y., Lam, Y.H., Ladhib, Z., Aparicio, Thomas, Ducreux, Michel, Faroux, Roger, Barbier, Emilie, Manfredi, Sylvain, Lecomte, Thierry, Etienne, Pierre-Luc, Bedenne, Laurent, Bennouna, Jaafar, Phelip, Jean-Marc, François, Eric, Michel, Pierre, Legoux, Jean-Louis, Gasmi, Mohamed, Breysacher, Gilles, Rougier, Philippe, De Gramont, Aimery, Lepage, Come, Bouché, Olivier, and Seitz, Jean-François

Published
2018
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33. Pre-test probability for SARS-Cov-2-related infection score: The PARIS score.

Author

Mickael Tordjman, Ahmed Mekki, Rahul D Mali, Ines Saab, Guillaume Chassagnon, Enora Guillo, Robert Burns, Deborah Eshagh, Sebastien Beaune, Guillaume Madelin, Simon Bessis, Antoine Feydy, Fadila Mihoubi, Benoit Doumenc, Luc Mouthon, Robert-Yves Carlier, Jean-Luc Drapé, and Marie-Pierre Revel

Subjects
Medicine, Science
Abstract

IntroductionIn numerous countries, large population testing is impossible due to the limited availability of RT-PCR kits and CT-scans. This study aimed to determine a pre-test probability score for SARS-CoV-2 infection.MethodsThis multicenter retrospective study (4 University Hospitals) included patients with clinical suspicion of SARS-CoV-2 infection. Demographic characteristics, clinical symptoms, and results of blood tests (complete white blood cell count, serum electrolytes and CRP) were collected. A pre-test probability score was derived from univariate analyses of clinical and biological variables between patients and controls, followed by multivariate binary logistic analysis to determine the independent variables associated with SARS-CoV-2 infection.Results605 patients were included between March 10th and April 30th, 2020 (200 patients for the training cohort, 405 consecutive patients for the validation cohort). In the multivariate analysis, lymphocyte (ConclusionsThe PARIS score has a good performance to categorize the pre-test probability of SARS-CoV-2 infection based on complete white blood cell count. It could help clinicians adapt testing and for rapid triage of patients before test results.

Published
2020
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34. Evaluation of the performances of ‘typical’ imaging abnormalities of axial spondyloarthritis: results of the cross-sectional ILOS-DESIR study

Author

Maxime Dougados, Laure Gossec, Anna Molto, Pascal Richette, Marie-Martine Lefèvre-Colau, Violaine Foltz, Romain Beaufort, Jean-Denis Laredo, Philippe Dieude, Philippe Goupille, and Antoine Feydy

Subjects
Medicine
Abstract

Objective To evaluate the prevalence and performance as axial Spondyloarthritis (axSpA) diagnostic feature of radiographic and MRI lesions ‘typical’ of axSpA of the sacroiliac joint (SIJ) and spine in a mechanical chronic back pain (CBP) population and in an axSpA cohort.Methods Cross-sectional multicentre study. Patients: (1) recent onset axSpA (DESIR cohort) and (2) mechanical non-axSpA CBP matched for age and gender (ILOS study). Imaging: radiographs and MR scans were performed identically in both groups. All images were centrally read, blinded for diagnosis and for other imaging findings in the same patient. Statistical analysis: prevalence of lesions ‘typical of axSpA’ were compared in both groups. Sensitivity, specificity and positive likelihood ratios (LR+) of each lesion (and combination of lesions) were calculated.Results A total of 98 patients with CBP were included, and compared with 100 patients with recent onset axSpA. SIJ lesions were consistently more frequent in the axSpA group (35.0% vs 11.8% p

Published
2019
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36. A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging

Author

Adrien Bordner, Théodore Aouad, Clementina Lopez Medina, Sisi Yang, Anna Molto, Hugues Talbot, Maxime Dougados, Antoine Feydy, Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Dèpartement de Radiologie, Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, Department of Rheumatology, Universidad de Córdoba = University of Córdoba [Córdoba], Université Paris Cité (UPCité), Equipe 4 : ECaMO - Épidémiologie clinique appliquée aux maladies rhumatismales et musculo-squelettiques (CRESS - U1153), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Dèpartement de Rheumatologie

Subjects
Artificial intelligence, Magnetic resonance imaging, Radiological and Ultrasound Technology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV]Life Sciences [q-bio], Spondyloarthritis, Radiology, Nuclear Medicine and imaging, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, General Medicine, Sacroiliac joint, Deep-learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; PurposeThe purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain.Materials and methodsMRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited. Test datasets were from MRI follow-ups at five years and ten years. The model was evaluated using an external test dataset from the ASAS cohort. A neuronal network classifier (mask-RCNN) was trained and evaluated for sacroiliac joints detection and BME classification. Diagnostic capabilities of the model to predict ASAS MRI active sacroiliitis (BME in at least two half-slices) were assessed using Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy and AUC. The gold standard was experts' majority decision.ResultsA total of 256 patients with 362 MRI examinations from the DESIR cohort were included, with 27% meeting the ASAS definition for experts. A total of 178 MRI examinations were used for the training set, 25 for the validation set and 159 for the evaluation set. MCCs for DESIR baseline, 5-years, and 10-years follow-up were 0.90 (n = 53), 0.64 (n = 70), and 0.61 (n = 36), respectively. AUCs for predicting ASAS MRI were 0.98 (95% CI: 0.93–1), 0.90 (95% CI: 0.79–1), and 0.80 (95% CI: 0.62–1), respectively. The ASAS external validation cohort included 47 patients (mean age 36 ± 10 [SD] years; women, 51%) with 19% meeting the ASAS definition. MCC was 0.62, sensitivity 56% (95% CI: 42–70), specificity 100% (95% CI: 100–100) and AUC 0.76 (95% CI: 0.57–0.95).ConclusionThe deep learning model achieves performance close to those of experts for BME detection in sacroiliac joints and determination of active sacroiliitis according to the ASAS definition.

Published
2023

37. Scoliosis.

Author

Lacroix, Maxime, Khalifé, Marc, Ferrero, Emmanuelle, Clément, Olivier, Nguyen, Christelle, and Feydy, Antoine

Subjects
ADOLESCENT idiopathic scoliosis, SCOLIOSIS, RADIOGRAPHS, MAGNETIC resonance imaging, SPINE abnormalities, STANDING position
Abstract

Scoliosis is a three-dimensional spinal deformity that can occur at any age. It may be idiopathic or secondary in children, idiopathic and degenerative in adults. Management of patients with scoliosis is multidisciplinary, involving rheumatologists, radiologists, orthopaedic surgeons, and prosthetists. Imaging plays a central role in diagnosis, including the search for secondary causes, follow-up, and preoperative work-up if surgery is required. Evaluating scoliosis involves obtaining frontal and lateral full-spine radiographs in the standing position, with analysis of coronal and sagittal alignment. For adolescent idiopathic scoliosis, imaging follow-up is often required, accomplished using low-dose stereoradiography such as EOS imaging. For adult degenerative scoliosis, the crucial characteristic is rotatory subluxation, also well detected on radiographs. Magnetic resonance imaging is usually more informative than computed tomography for visualizing associated canal and foraminal stenoses. Radiologists must also have a thorough understanding of postoperative features and complications of scoliosis surgery because aspects can be misleading. [ABSTRACT FROM AUTHOR]

Published
2023
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38. Results of an International Survey on Spinal Imaging by the ASNR/ASSR/ESNR/ESSR "Nomenclature 3.0" Working Group.

Author

D'Anna, Gennaro, Shah, Lubdha, Kranz, Peter G., Hirsch, Joshua A., Khan, M., Johnson, Michele, Feydy, Antoine, Nathan, J., Manfre, L., Nguyen, Dan T., Sze, Gordan, Goethem, Johan Van, and Vanhoenacker, Filip M.

Subjects
SPINAL stenosis, MAGNETIC resonance, SPINE diseases
Abstract

Our goal was to determine if "Nomenclature 2.0," the classification of lumbar disk pathology consensus, should be updated. We conducted a social media and e-mail-based survey on preferences regarding the use of classification on magnetic resonance spine reporting. Members of the European Society of Neuroradiology, European Society of Musculoskeletal Radiology, American Society of Neuroradiology, and American Society of Spine Radiology received a 15-question online survey between February and March 2022. A total of 600 responses were received from 63 countries. The largest number of responses came from Italy and the United States. We found that 71.28% of respondents used Nomenclature 2.0, Classification of Lumbar Disk Pathology. But classification on stenosis is used less often: 53.94% and 60% of respondents do not use any classification of spinal canal stenosis and foraminal stenosis, respectively. When queried about which part of Nomenclature needs improving, most respondents asked for a Structured Reporting Template (SRT), even though 58.85% of respondents do not currently use any template and 54% routinely use a clinical information questionnaire. These results highlight the importance of an updated Nomenclature 3.0 version that integrates the classifications of lumbar disk disease and spinal canal and foraminal stenosis. Further attention should also be directed toward developing a robust endorsed SRT. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Clinical presentation of patients suffering from recent onset chronic inflammatory back pain suggestive of spondyloarthritis: The DESIR cohort

Author

Dougados, Maxime, Etcheto, Adrien, Molto, Anna, Alonso, Sandrine, Bouvet, Sophie, Daurès, Jean-Pierre, Landais, Paul, d’Agostino, Maria-Antonietta, Berenbaum, Francis, Breban, Maxime, Claudepierre, Pascal, Combe, Bernard, Fautrel, Bruno, Feydy, Antoine, Goupille, Philippe, Richette, Pascal, Pham, Thao, Roux, Christian, Treluyer, Jean-Marc, Saraux, Alain, van der Heijde, Désirée, and Wendling, Daniel

Published
2015
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41. Approximation and Structured Prediction with Sparse Wasserstein Barycenters

Author

Do, Minh-Hieu, Feydy, Jean, and Mula, Olga

Subjects
FOS: Mathematics, Numerical Analysis (math.NA), Mathematics - Numerical Analysis
Abstract

We develop a general theoretical and algorithmic framework for sparse approximation and structured prediction in $\mathcal{P}_2(\Omega)$ with Wasserstein barycenters. The barycenters are sparse in the sense that they are computed from an available dictionary of measures but the approximations only involve a reduced number of atoms. We show that the best reconstruction from the class of sparse barycenters is characterized by a notion of best $n$-term barycenter which we introduce, and which can be understood as a natural extension of the classical concept of best $n$-term approximation in Banach spaces. We show that the best $n$-term barycenter is the minimizer of a highly non-convex, bi-level optimization problem, and we develop algorithmic strategies for practical numerical computation. We next leverage this approximation tool to build interpolation strategies that involve a reduced computational cost, and that can be used for structured prediction, and metamodelling of parametrized families of measures. We illustrate the potential of the method through the specific problem of Model Order Reduction (MOR) of parametrized PDEs. Since our approach is sparse, adaptive and preserves mass by construction, it has potential to overcome known bottlenecks of classical linear methods in hyperbolic conservation laws transporting discontinuities. It also paves the way towards MOR for measure-valued PDE problems such as gradient flows.

Published
2023

44. Incrementally semi-supervised classification of arthritis inflammation on a clinical dataset

Author

Theodore Aouad, Clementina Lopez-Medina, Charlotte Martin-Peltier, Adrien Bordner, Sisi Yang, Anna Molto, Maxime Dougados, Antoine Feydy, Hugues Talbot, 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, Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), IEEE, and Aouad, Théodore

Subjects
Image segmentation, Benchmark testing, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Deep Learning, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Arthritis, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Annotations, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Semi-supervised Learning, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Magnetic Resonance Imaging
Abstract

International audience; For best medical imaging application results, learning-based approaches such as deep learning necessitate specific, extensive and precise annotations. Outside well-curated public benchmarks, these are rarely available in practice, and so it becomes necessary to use less-than-perfect annotations. One way of compensating for this is the embedding of anatomical knowledge. Complementing this, there is the incremental semi-supervised learning technique, whereby a small amount of annotations can be used to derive more and superior labels.In this article, we illustrate this approach on a deep learning system to help radiologists and rheumatologists finely and interactively assess MRI scans of the sacro-iliac joint in order to correctly diagnose Axial Spondyloarthritis. Our model is trained initially on a relatively small set of images with promising results, on par with expert opinion and generalizable to new datasets.

Published
2022

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