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4. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Author

Slart, R, Williams, M, Juarez-Orozco, L, Rischpler, C, Dweck, M, Glaudemans, A, Gimelli, A, Georgoulias, P, Gheysens, O, Gaemperli, O, Habib, G, Hustinx, R, Cosyns, B, Verberne, H, Hyafil, F, Erba, P, Lubberink, M, Slomka, P, Isgum, I, Visvikis, D, Kolossvary, M, Saraste, A, Slart R. H. J. A., Williams M. C., Juarez-Orozco L. E., Rischpler C., Dweck M. R., Glaudemans A. W. J. M., Gimelli A., Georgoulias P., Gheysens O., Gaemperli O., Habib G., Hustinx R., Cosyns B., Verberne H. J., Hyafil F., Erba P. A., Lubberink M., Slomka P., Isgum I., Visvikis D., Kolossvary M., Saraste A., Slart, R, Williams, M, Juarez-Orozco, L, Rischpler, C, Dweck, M, Glaudemans, A, Gimelli, A, Georgoulias, P, Gheysens, O, Gaemperli, O, Habib, G, Hustinx, R, Cosyns, B, Verberne, H, Hyafil, F, Erba, P, Lubberink, M, Slomka, P, Isgum, I, Visvikis, D, Kolossvary, M, Saraste, A, Slart R. H. J. A., Williams M. C., Juarez-Orozco L. E., Rischpler C., Dweck M. R., Glaudemans A. W. J. M., Gimelli A., Georgoulias P., Gheysens O., Gaemperli O., Habib G., Hustinx R., Cosyns B., Verberne H. J., Hyafil F., Erba P. A., Lubberink M., Slomka P., Isgum I., Visvikis D., Kolossvary M., and Saraste A.

Abstract

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Published
2021

8. Detection of osteomyelitis in the diabetic foot by imaging techniques: A systematic review and meta-analysis comparing mri, white blood cell scintigraphy, and FDG-PET

Author

Lauri, C, Tamminga, M, Glaudemans, A, Juarez Orozco, L, Erba, P, Jutte, P, Lipsky, B, Ijzerman, M, Signore, A, Slart, R, Lauri C., Tamminga M., Glaudemans A. W. J. M., Juarez Orozco L. E., Erba P. A., Jutte P. C., Lipsky B. A., IJzerman M. J., Signore A., Slart R. H. J. A., Lauri, C, Tamminga, M, Glaudemans, A, Juarez Orozco, L, Erba, P, Jutte, P, Lipsky, B, Ijzerman, M, Signore, A, Slart, R, Lauri C., Tamminga M., Glaudemans A. W. J. M., Juarez Orozco L. E., Erba P. A., Jutte P. C., Lipsky B. A., IJzerman M. J., Signore A., and Slart R. H. J. A.

Abstract

OBJECTIVE Diagnosing bone infection in the diabetic foot is challenging and often requires several diagnostic procedures, including advanced imaging. We compared the diagnostic performances of MRI, radiolabeled white blood cell (WBC) scintigraphy (either with 99mTc-hexamethylpropyleneamineoxime [HMPAO] or 111In-oxine), and [18F]fluorodeoxyglucose positron emission tomography (18F-FDG-PET)/ computed tomography. RESEARCH DESIGN AND METHODS We searchedMedline andEmbase as of August 2016 for studies of diagnostic tests on patients known or suspected to have diabetes and a foot infection. We performed a systematic review using criteria recommended by the Cochrane Review of a database that included prospective and retrospective diagnostic studies performed on patients with diabetes in whom there was a clinical suspicion of osteomyelitis of the foot. The preferred reference standard was bone biopsy and subsequent pathological (or microbiological) examination. RESULTS Our review found 6,649 articles; 3,894 in Medline and 2,755 in Embase. A total of 27 full articles and 2 posters was selected for inclusion in the analysis. The performance characteristics for the 18F-FDG-PET were: sensitivity, 89%; specificity, 92%; diagnostic odds ratio (DOR), 95; positive likelihood ratio (LR), 11; and negative LR, 0.11. For WBC scan with 111In-oxine, the values were: sensitivity, 92%; specificity, 75%; DOR, 34; positive LR, 3.6; and negative LR, 0.1. For WBC scan with 99mTc-HMPAO, the values were: sensitivity, 91%; specificity, 92%; DOR, 118; positive LR, 12; and negative LR, 0.1. Finally, forMRI, the valueswere: sensitivity, 93%; specificity, 75%; DOR, 37; positive LR, 3.66, and negative LR, 0.10. CONCLUSIONS The various modalities have similar sensitivity, but 18F-FDG-PET and 99mTc-HMPAO-labeled WBC scintigraphy offer the highest specificity. Larger prospective studies with a direct comparison among the different imaging techniques are required.

Published
2017

13. Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium

Author

Arts Assistenten Cardiologie, MMB opleiding Arts microbioloog, MMB Research line 3b, Experimentele Afd. Cardiologie 1, Circulatory Health, Beeldverwerking ISI, Cancer, Brain, Researchgr. Cardiovasculaire Radiologie, Team Medisch, Benjamins, J. W., van Leeuwen, K., Hofstra, L., Rienstra, M., Appelman, Y., Nijhof, W., Verlaat, B., Everts, I., den Ruijter, H. M., Isgum, I., Leiner, T., Vliegenthart, R., Asselbergs, F. W., Juarez-Orozco, L. E., van der Harst, P., Arts Assistenten Cardiologie, MMB opleiding Arts microbioloog, MMB Research line 3b, Experimentele Afd. Cardiologie 1, Circulatory Health, Beeldverwerking ISI, Cancer, Brain, Researchgr. Cardiovasculaire Radiologie, Team Medisch, Benjamins, J. W., van Leeuwen, K., Hofstra, L., Rienstra, M., Appelman, Y., Nijhof, W., Verlaat, B., Everts, I., den Ruijter, H. M., Isgum, I., Leiner, T., Vliegenthart, R., Asselbergs, F. W., Juarez-Orozco, L. E., and van der Harst, P.

Published
2019

16. Absolute Stress Myocardial Blood Flow Determines Ventricular Function and Synchrony Better than Myocardial Perfusion Reserve: a 13N-ammonia PET Study

Author

Juarez-Orozco, L., Alexanderson, E., Dierckx, R. A., Boersma, H. H., Zeebregts, C. J., Prakken, N., Tio, R. A., Slart, R. H. J. A., Molecular Neuroscience and Ageing Research (MOLAR), Vascular Ageing Programme (VAP), Man, Biomaterials and Microbes (MBM), Cardiovascular Centre (CVC), Translational Immunology Groningen (TRIGR), Lifelong Learning, Education & Assessment Research Network (LEARN), and ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE)

Published
2016

17. [18F]-Sodium Fluoride Uptake in Takayasu Arteritis

Author

Alexanderson, Erick, De Guadalupe Monroy Gonzalez, Andrea, Juarez-Orozco, L., Tio, R., Glaudemans, A. W. J. M., Estrada-Cuzcano, Alejandro, Garcia-Vuelta, O., Meave, A., Slart, R. H. J. A., Soto-Lopez, M E, Vascular Ageing Programme (VAP), Translational Immunology Groningen (TRIGR), Cardiovascular Centre (CVC), and ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE)

Published
2015

19. Poster Session 1: Sunday 3 May 2015, 08:30-18:00 * Room: Poster Area

Author

Taniguchi, Y., primary, Takahashi, Y., additional, Toba, T., additional, Yamada, S., additional, Yokoi, K., additional, Kobayashi, S., additional, Okajima, S., additional, Shimane, A., additional, Kawai, H., additional, Yasaka, Y., additional, Smanio, P., additional, Oliveira, M. A., additional, Machado, L., additional, Cestari, P., additional, Medeiros, E., additional, Fukuzawa, S., additional, Okino, S., additional, Ikeda, A., additional, Maekawa, J., additional, Ichikawa, S., additional, Kuroiwa, N., additional, Yamanaka, K., additional, Igarashi, A., additional, Inagaki, M., additional, Patel, K., additional, Mahan, M., additional, Ananthasubramaniam, K., additional, Mouden, M., additional, Yokota, S., additional, Ottervanger, J., additional, Knollema, S., additional, Timmer, J., additional, Jager, P., additional, Padron, K., additional, Peix, A., additional, Cabrera, L., additional, Pena Bofill, V., additional, Valera, D., additional, Rodriguez Nande, L., additional, Carrillo Hernandez, R., additional, Mena Esnard, E., additional, Fernandez Columbie, Y., additional, Bertella, E., additional, Baggiano, A., additional, Mushtaq, S., additional, Segurini, C., additional, Loguercio, M., additional, Conte, E., additional, Beltrama, V., additional, Petulla', M., additional, Andreini, D., additional, Pontone, G., additional, Guzic Salobir, B., additional, Dolenc Novak, M., additional, Jug, B., additional, Kacjan, B., additional, Novak, Z., additional, Vrtovec, M., additional, Volpato, V., additional, Formenti, A., additional, Pepi, M., additional, Ajanovic, R., additional, Husic-Selimovic, A., additional, Zujovic-Ajanovic, A., additional, Mlynarski, R., additional, Mlynarska, A., additional, Golba, K., additional, Sosnowski, M., additional, Ameta, D., additional, Goyal, M., additional, Kumar, D., additional, Chandra, S., additional, Sethi, R., additional, Puri, A., additional, Dwivedi, S. K., additional, Narain, V. S., additional, Saran, R. K., additional, Nekolla, S., additional, Rischpler, C., additional, Nicolosi, S., additional, Langwieser, N., additional, Dirschinger, R., additional, Laugwitz, K., additional, Schwaiger, M., additional, Goral, J. L., additional, Napoli, J., additional, Forcada, P., additional, Zucchiatti, N., additional, Damico, A., additional, Olivieri, D., additional, Lavorato, M., additional, Dubesarsky, E., additional, Montana, O., additional, Salgado, C., additional, Jimenez-Heffernan, A., additional, Ramos-Font, C., additional, Lopez-Martin, J., additional, Sanchez De Mora, E., additional, Lopez-Aguilar, R., additional, Manovel, A., additional, Martinez, A., additional, Rivera, F., additional, Soriano, E., additional, Maroz-Vadalazhskaya, N., additional, Trisvetova, E., additional, Vrublevskaya, O., additional, Abazid, R., additional, Kattea, M., additional, Saqqah, H., additional, Sayed, S., additional, Smettei, O., additional, Winther, S., additional, Svensson, M., additional, Birn, H., additional, Jorgensen, H., additional, Botker, H., additional, Ivarsen, P., additional, Bottcher, M., additional, Maaniitty, T., additional, Stenstrom, I., additional, Saraste, A., additional, Pikkarainen, E., additional, Uusitalo, V., additional, Ukkonen, H., additional, Kajander, S., additional, Bax, J., additional, Knuuti, J., additional, Choi, T., additional, Park, H., additional, Lee, C., additional, Lee, J., additional, Seo, Y., additional, Cho, Y., additional, Hwang, E., additional, Cho, D., additional, Sanchez Enrique, C., additional, Ferrera, C., additional, Olmos, C., additional, Jimenez - Ballve, A., additional, Perez - Castejon, M. J., additional, Fernandez, C., additional, Vivas, D., additional, Vilacosta, I., additional, Nagamachi, S., additional, Onizuka, H., additional, Nishii, R., additional, Mizutani, Y., additional, Kitamura, K., additional, Lo Presti, M., additional, Polizzi, V., additional, Pino, P., additional, Luzi, G., additional, Bellavia, D., additional, Fiorilli, R., additional, Madeo, A., additional, Malouf, J., additional, Buffa, V., additional, Musumeci, F., additional, Rosales, S., additional, Puente, A., additional, Zafrir, N., additional, Shochat, T., additional, Mats, A., additional, Solodky, A., additional, Kornowski, R., additional, Lorber, A., additional, Boemio, A., additional, Pellegrino, T., additional, Paolillo, S., additional, Piscopo, V., additional, Carotenuto, R., additional, Russo, B., additional, Pellegrino, S., additional, De Matteis, G., additional, Perrone-Filardi, P., additional, Cuocolo, A., additional, Petretta, M., additional, Amirov, N., additional, Ibatullin, M., additional, Sadykov A, A., additional, Saifullina, G., additional, Ruano, R., additional, Diego Dominguez, M., additional, Rodriguez Gabella, T., additional, Diego Nieto, A., additional, Diaz Gonzalez, L., additional, Garcia-Talavera, J., additional, Sanchez Fernandez, P., additional, Leen, A., additional, Al Younis, I., additional, Zandbergen-Harlaar, S., additional, Verberne, H., additional, Gimelli, A., additional, Veltman, C., additional, Wolterbeek, R., additional, Scholte, A., additional, Mooney, D., additional, Rosenblatt, J., additional, Dunn, T., additional, Vasaiwala, S., additional, Okuda, K., additional, Nakajima, K., additional, Nystrom, K., additional, Edenbrandt, L., additional, Matsuo, S., additional, Wakabayashi, H., additional, Hashimoto, M., additional, Kinuya, S., additional, Iric-Cupic, V., additional, Milanov, S., additional, Davidovic, G., additional, Zdravkovic, V., additional, Ashikaga, K., additional, Yoneyama, K., additional, Akashi, Y., additional, Shugushev, Z., additional, Maximkin, D., additional, Chepurnoy, A., additional, Volkova, O., additional, Baranovich, V., additional, Faibushevich, A., additional, El Tahlawi, M., additional, Elmurr, A., additional, Alzubaidi, S., additional, Sakrana, A., additional, Gouda, M., additional, El Tahlawi, R., additional, Sellem, A., additional, Melki, S., additional, Elajmi, W., additional, Hammami, H., additional, Okano, M., additional, Kato, T., additional, Kimura, M., additional, Funasako, M., additional, Nakane, E., additional, Miyamoto, S., additional, Izumi, T., additional, Haruna, T., additional, Inoko, M., additional, Massardo, T., additional, Swett, E., additional, Fernandez, R., additional, Vera, V., additional, Zhindon, J., additional, Alay, R., additional, Ohshima, S., additional, Nishio, M., additional, Kojima, A., additional, Tamai, S., additional, Kobayashi, T., additional, Murohara, T., additional, Burrell, S., additional, Van Rosendael, A., additional, Van Den Hoogen, I., additional, De Graaf, M., additional, Roelofs, J., additional, Kroft, L., additional, Rjabceva, I., additional, Krumina, G., additional, Kalvelis, A., additional, Chanakhchyan, F., additional, Vakhromeeva, M., additional, Kankiya, E., additional, Koppes, J., additional, Knol, R., additional, Wondergem, M., additional, Van Der Ploeg, T., additional, Van Der Zant, F., additional, Lazarenko, S. V., additional, Bruin, V. S., additional, Pan, X. B., additional, Declerck, J. M., additional, Van Der Zant, F. M., additional, Knol, R. J. J., additional, Juarez-Orozco, L. E., additional, Alexanderson, E., additional, Slart, R., additional, Tio, R., additional, Dierckx, R., additional, Zeebregts, C., additional, Boersma, H., additional, Hillege, H., additional, Martinez-Aguilar, M., additional, Jordan-Rios, A., additional, Christensen, T. E., additional, Ahtarovski, K. A., additional, Bang, L. E., additional, Holmvang, L., additional, Soeholm, H., additional, Ghotbi, A. A., additional, Andersson, H., additional, Ihlemann, N., additional, Kjaer, A., additional, Hasbak, P., additional, Gulya, M., additional, Lishmanov, Y. B., additional, Zavadovskii, K., additional, Lebedev, D., additional, Stahle, M., additional, Hellberg, S., additional, Liljenback, H., additional, Virta, J., additional, Metsala, O., additional, Yla-Herttuala, S., additional, Saukko, P., additional, Roivainen, A., additional, Thackeray, J., additional, Wang, Y., additional, Bankstahl, J., additional, Wollert, K., additional, Bengel, F., additional, Saushkina, Y., additional, Evtushenko, V., additional, Minin, S., additional, Efimova, I., additional, Evtushenko, A., additional, Smishlyaev, K., additional, Lishmanov, Y., additional, Maslov, L., additional, Kirihara, Y., additional, Sugino, S., additional, Taki, J., additional, Ahmadian, A., additional, Berman, J., additional, Govender, P., additional, Ruberg, F., additional, Miller, E., additional, Piriou, N., additional, Pallardy, A., additional, Valette, F., additional, Cahouch, Z., additional, Mathieu, C., additional, Warin-Fresse, K., additional, Gueffet, J., additional, Serfaty, J., additional, Trochu, J., additional, Kraeber-Bodere, F., additional, Van Dijk, J., additional, Van Dalen, J., additional, Ofrk, H., additional, Vaturi, M., additional, Hassid, Y., additional, Belzer, D., additional, Sagie, A., additional, Kaminek, M., additional, Metelkova, I., additional, Budikova, M., additional, Koranda, P., additional, Henzlova, L., additional, Sovova, E., additional, Kincl, V., additional, Drozdova, A., additional, Jordan, M., additional, Shahid, F., additional, Teoh, Y., additional, Thamen, R., additional, Hara, N., additional, Onoguchi, M., additional, Hojyo, O., additional, Kawaguchi, Y., additional, Murai, M., additional, Udaka, F., additional, Matsuzawa, Y., additional, Bulugahapitiya, D. S., additional, Avison, M., additional, Martin, J., additional, Liu, Y.-H., additional, Wu, J., additional, Liu, C., additional, Sinusas, A., additional, Daou, D., additional, Sabbah, R., additional, Bouladhour, H., additional, Coaguila, C., additional, Aguade-Bruix, S., additional, Pizzi, M., additional, Romero-Farina, G., additional, Candell-Riera, J., additional, Castell-Conesa, J., additional, Patchett, N., additional, Sverdlov, A., additional, Boulaamayl El Fatemi, S., additional, Sallam, L., additional, Snipelisky, D., additional, Park, J., additional, Ray, J., additional, Shapiro, B., additional, Kostkiewicz, M., additional, Szot, W., additional, Holcman, K., additional, Lesniak-Sobelga, A., additional, Podolec, P., additional, Clerc, O., additional, Possner, M., additional, Liga, R., additional, Vontobel, J., additional, Mikulicic, F., additional, Graeni, C., additional, Benz, D., additional, Herzog, B., additional, Gaemperli, O., additional, and Kaufmann, P., additional

Published
2015
Full Text
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22. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Author

Oliver Gaemperli, Paola Anna Erba, Antti Saraste, Michelle C. Williams, Alessia Gimelli, Piotr J. Slomka, Christoph Rischpler, Roland Hustinx, Marc R. Dweck, Hein J. Verberne, Andor W. J. M. Glaudemans, Bernard Cosyns, Márton Kolossváry, Panagiotis Georgoulias, Luis Eduardo Juarez-Orozco, Ivana Išgum, Gilbert Habib, Mark Lubberink, Riemer H. J. A. Slart, Olivier Gheysens, Dimitris Visvikis, Fabien Hyafil, ​Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Translational Immunology Groningen (TRIGR), Cardiovascular Centre (CVC), IvI Research (FNWI), UCL - SSS/IREC/SLUC - Pôle St.-Luc, UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de médecine nucléaire, Clinical sciences, Cardio-vascular diseases, Cardiology, Slart, R, Williams, M, Juarez-Orozco, L, Rischpler, C, Dweck, M, Glaudemans, A, Gimelli, A, Georgoulias, P, Gheysens, O, Gaemperli, O, Habib, G, Hustinx, R, Cosyns, B, Verberne, H, Hyafil, F, Erba, P, Lubberink, M, Slomka, P, Isgum, I, Visvikis, D, Kolossvary, M, Saraste, A, University Medical Center Groningen [Groningen] (UMCG), University of Twente, University of Edinburgh, Utrecht University [Utrecht], University of Groningen [Groningen], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Fondazione Toscana Gabriele Monasterio, University Hospital of Larissa, Cliniques Universitaires Saint-Luc [Bruxelles], Université Catholique de Louvain = Catholic University of Louvain (UCL), Hirslanden Medical Center, Microbes évolution phylogénie et infections (MEPHI), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Timone [CHU - APHM] (TIMONE), Institut Hospitalier Universitaire Méditerranée Infection (IHU Marseille), GIGA [Université Liège], Université de Liège, Universitair Ziekenhuis [Brussels, Belgium], University of Amsterdam [Amsterdam] (UvA), 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), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), 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)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), University of Pisa - Università di Pisa, Uppsala University, Uppsala University Hospital, Cedars-Sinai Medical Center, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Semmelweis University [Budapest], University of Turku, Turku University Hospital (TYKS), Radiology and Nuclear Medicine, ACS - Amsterdam Cardiovascular Sciences, Biomedical Engineering and Physics, ACS - Atherosclerosis & ischemic syndromes, ANS - Brain Imaging, and ACS - Heart failure & arrhythmias

Subjects
medicine.medical_specialty, Medizin, 030204 cardiovascular system & hematology, Guidelines, Cardiovascular, Multimodality imaging, 030218 nuclear medicine & medical imaging, Multimodality, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Artificial Intelligence, Positron Emission Tomography Computed Tomography, Machine learning, medicine, Humans, [SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitology, Radiology, Nuclear Medicine and imaging, Medical physics, Position paper, Deep learning, Positron-Emission Tomography, Tomography, Emission-Computed, Single-Photon, Tomography, X-Ray Computed, Nuclear Medicine, Tomography, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, PET-CT, medicine.diagnostic_test, business.industry, Coronary computed tomography angiography, General Medicine, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, X-Ray Computed, Functional imaging, Positron emission tomography, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Radiologi och bildbehandling, Applications of artificial intelligence, Emission-Computed, Cardiology and Cardiovascular Medicine, business, Emission computed tomography, Radiology, Nuclear Medicine and Medical Imaging, Single-Photon
Abstract

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Published
2021
Full Text
View/download PDF

23. Detection of osteomyelitis in the diabetic foot by imaging techniques: A systematic review and meta-analysis comparing mri, white blood cell scintigraphy, and FDG-PET

Author

Benjamin A. Lipsky, Menno Tamminga, Alberto Signore, Andor W. J. M. Glaudemans, Paola Anna Erba, Maarten Joost IJzerman, Paul C Jutte, Riemer H. J. A. Slart, Luis Juarez Orozco, Chiara Lauri, Lauri, C, Tamminga, M, Glaudemans, A, Juarez Orozco, L, Erba, P, Jutte, P, Lipsky, B, Ijzerman, M, Signore, A, Slart, R, Health Technology & Services Research, and Biomedical Photonic Imaging

Subjects
Databases, Factual, IMPACT, Endocrinology, Diabetes and Metabolism, Scintigraphy, Likelihood ratios in diagnostic testing, 030218 nuclear medicine & medical imaging, PRIOR MYOCARDIAL-INFARCTION, MELLITUS, Endocrinology, 0302 clinical medicine, Leukocytes, Prospective cohort study, RISK, medicine.diagnostic_test, Osteomyelitis, Magnetic Resonance Imaging, Diabetic Foot, Diabetes and Metabolism, CARDIOVASCULAR-DISEASE, Positron emission tomography, factual, Radiology, INTERVENTION, Bone and Bone, Human, medicine.medical_specialty, databases, 030209 endocrinology & metabolism, bone and bones, databases, factual, diabetic foot, humans, osteomyelitis, sensitivity and specificity, leukocytes, magnetic resonance imaging, positron-emission tomography, radionuclide imaging, Sensitivity and Specificity, Bone and Bones, 03 medical and health sciences, Internal Medicine, Advanced and Specialized Nursing, medicine, Osteomyeliti, Humans, CORONARY-HEART-DISEASE, Radionuclide Imaging, business.industry, MORTALITY, Magnetic resonance imaging, Leukocyte, medicine.disease, RANDOMIZED-TRIALS, 22/4 OA procedure, Diabetic foot, Positron-Emission Tomography, Diagnostic odds ratio, FOLLOW-UP, business
Abstract

OBJECTIVE Diagnosing bone infection in the diabetic foot is challenging and often requires several diagnostic procedures, including advanced imaging. We compared the diagnostic performances of MRI, radiolabeled white blood cell (WBC) scintigraphy (either with 99mTc-hexamethylpropyleneamineoxime [HMPAO] or 111In-oxine), and [18F]fluorodeoxyglucose positron emission tomography (18F-FDG–PET)/computed tomography. RESEARCH DESIGN AND METHODS We searched Medline and Embase as of August 2016 for studies of diagnostic tests on patients known or suspected to have diabetes and a foot infection. We performed a systematic review using criteria recommended by the Cochrane Review of a database that included prospective and retrospective diagnostic studies performed on patients with diabetes in whom there was a clinical suspicion of osteomyelitis of the foot. The preferred reference standard was bone biopsy and subsequent pathological (or microbiological) examination. RESULTS Our review found 6,649 articles; 3,894 in Medline and 2,755 in Embase. A total of 27 full articles and 2 posters was selected for inclusion in the analysis. The performance characteristics for the 18F-FDG–PET were: sensitivity, 89%; specificity, 92%; diagnostic odds ratio (DOR), 95; positive likelihood ratio (LR), 11; and negative LR, 0.11. For WBC scan with 111In-oxine, the values were: sensitivity, 92%; specificity, 75%; DOR, 34; positive LR, 3.6; and negative LR, 0.1. For WBC scan with 99mTc-HMPAO, the values were: sensitivity, 91%; specificity, 92%; DOR, 118; positive LR, 12; and negative LR, 0.1. Finally, for MRI, the values were: sensitivity, 93%; specificity, 75%; DOR, 37; positive LR, 3.66, and negative LR, 0.10. CONCLUSIONS The various modalities have similar sensitivity, but 18F-FDG–PET and 99mTc-HMPAO–labeled WBC scintigraphy offer the highest specificity. Larger prospective studies with a direct comparison among the different imaging techniques are required.

Published
2017

24. Prehospital risk stratification in patients with chest pain.

Author

Sagel D, Vlaar PJ, van Roosmalen R, Waardenburg I, Nieuwland W, Lettinga R, van Barneveld R, Jorna E, Kijlstra R, van Well C, Oomen A, Bartels L, Anthonio R, Hagens V, Hofma S, Gu Y, Drenth D, Addink R, van Asselt T, van der Meer P, Lipsic E, Juarez Orozco L, and van der Harst P

Subjects
Aged, Area Under Curve, Chest Pain complications, Chest Pain epidemiology, Emergency Medical Services, Emergency Service, Hospital organization & administration, Emergency Service, Hospital statistics & numerical data, Female, Humans, Male, Middle Aged, Netherlands epidemiology, Prospective Studies, ROC Curve, Risk Assessment methods, Risk Factors, Risk Management statistics & numerical data, Chest Pain therapy, Risk Management methods
Abstract

Objectives: The History, ECG, Age, Risk Factors and Troponin (HEART) Score is a decision support tool applied by physicians in the emergency department developed to risk stratify low-risk patients presenting with chest pain. We assessed the potential value of this tool in prehospital setting, when applied by emergency medical services (EMS), and derived and validated a tool adapted to the prehospital setting in order to determine if it could assist with decisions regarding conveyance to a hospital., Methods: In 2017, EMS personnel prospectively determined the HEART Score, including point-of-care (POC) troponin measurements, in patients presenting with chest pain, in the north of the Netherlands. The primary endpoint was a major adverse cardiac event (MACE), consisting of acute myocardial infarction or death, within 3 days. The components of the HEART Score were evaluated for their discriminatory value, cut-offs were calibrated for the prehospital setting and sex was substituted for cardiac risk factors to develop a prehospital HEART (preHEART) Score. This score was validated in an independent prospective cohort of 435 patients in 2018., Results: Among 1208 patients prospectively recruited in the first cohort, 123 patients (10.2%) developed a MACE. The HEART Score had a negative predictive value (NPV) of 98.4% (96.4-99.3), a positive predictive value (PPV) of 35.5% (31.8-39.3) and an area under the receiver operating characteristic curve (AUC) of 0.81 (0.78-0.85). The preHEART Score had an NPV of 99.3% (98.1-99.8), a PPV of 49.4% (42.0-56.9) and an AUC of 0.85 (0.82-0.88), outperforming the HEART Score or POC troponin measurements on their own. Similar results were found in a validation cohort., Conclusions: The HEART Score can be used in the prehospital setting to assist with conveyance decisions and choice of hospitals; however, the preHEART Score outperforms both the HEART Score and single POC troponin measurements when applied by EMS personnel in the prehospital setting., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Published
2021
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