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Bär S, Nabeta T, Maaniitty T, Saraste A, Bax JJ, Earls JP, Min JK, Knuuti J. Prognostic value of a novel artificial intelligence-based coronary computed tomography angiography-derived ischaemia algorithm for patients with suspected coronary artery disease. Eur Heart J Cardiovasc Imaging 2024; 25:657-667. [PMID: 38084894 PMCID: PMC11057943 DOI: 10.1093/ehjci/jead339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 05/01/2024] Open
Abstract
AIMS Coronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial intelligence-guided quantitative computed tomography ischaemia algorithm (AI-QCTischaemia) aims to identify myocardial ischaemia directly from CTA images and may be helpful to improve risk stratification. The aims were to investigate (i) the prognostic value of AI-QCTischaemia amongst symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA and (ii) the prognostic value of AI-QCTischaemia separately amongst patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis). METHODS AND RESULTS For this cohort study, AI-QCTischaemia was calculated by blinded analysts amongst patients with suspected CAD undergoing coronary CTA. The primary endpoint was the composite of death, myocardial infarction (MI), or unstable angina pectoris (uAP) (median follow-up 6.9 years). A total of 1880/2271 (83%) patients had conclusive AI-QCTischaemia result. Patients with an abnormal AI-QCTischaemia result (n = 509/1880) vs. patients with a normal AI-QCTischaemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint [adjusted hazard ratio (HRadj) 1.96, 95% confidence interval (CI) 1.46-2.63, P < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina]. An abnormal AI-QCTischaemia result was associated with significantly higher crude and adjusted rates of the primary endpoint amongst patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81, 95% CI 1.09-3.00, P = 0.022), but not amongst those with obstructive CAD (n = 474/1847) (HRadj 1.26, 95% CI 0.75-2.12, P = 0.386) (P-interaction = 0.032). CONCLUSION Amongst patients with suspected CAD, an abnormal AI-QCTischaemia result was associated with a two-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischaemia may be useful to improve risk stratification, especially amongst patients with no/non-obstructive CAD on coronary CTA.
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Affiliation(s)
- Sarah Bär
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Department of Cardiology, Bern University Hospital Inselspital, Bern, Switzerland
| | - Takeru Nabeta
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Teemu Maaniitty
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital, Hämeentie 11, 20540 Turku, Finland
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Heart Center, Turku University Hospital, University of Turku, Turku, Finland
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Juhani Knuuti
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital, Hämeentie 11, 20540 Turku, Finland
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2
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Sica G, Rea G, Scaglione M. Editorial for the Special Issue "Cardiothoracic Imaging: Recent Techniques and Applications in Diagnostics". Diagnostics (Basel) 2024; 14:461. [PMID: 38472934 DOI: 10.3390/diagnostics14050461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 03/14/2024] Open
Abstract
Technology is making giant strides and is increasingly improving the diagnostic imaging of both frequent and rare acute and chronic diseases [...].
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Affiliation(s)
- Giacomo Sica
- Department of Radiology, Azienda Ospedaliera dei Colli, Monaldi Hospital, 80131 Naples, Italy
| | - Gaetano Rea
- Department of Radiology, Azienda Ospedaliera dei Colli, Monaldi Hospital, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
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Artesani A, Bruno A, Gelardi F, Chiti A. Empowering PET: harnessing deep learning for improved clinical insight. Eur Radiol Exp 2024; 8:17. [PMID: 38321340 PMCID: PMC10847083 DOI: 10.1186/s41747-023-00413-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 02/08/2024] Open
Abstract
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.
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Affiliation(s)
- Alessia Artesani
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20090, Italy
| | - Alessandro Bruno
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", IULM Libera Università Di Lingue E Comunicazione, Via P. Filargo 38, Milan, 20143, Italy
| | - Fabrizia Gelardi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20090, Italy.
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.
| | - Arturo Chiti
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milan, 20132, Italy
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4
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Williams MC. Machine learning models for positron emission tomography myocardial perfusion imaging. J Nucl Cardiol 2024; 32:101805. [PMID: 38244977 DOI: 10.1016/j.nuclcard.2024.101805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 01/09/2024] [Indexed: 01/22/2024]
Abstract
Machine learning has the potential to improve patient care by automating the assessment of medical imaging. Machine learning models have been developed to identify ischaemia and scar on rest and stress myocardial perfusion imaging from positron emission tomography (PET). Application of these tools could aid reporting of PET by highlighting patients and vessels likely to have abnormalities. How this information should be integrated into clinical practice and the impact on patient management or outcomes is not currently known.
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Affiliation(s)
- Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom.
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5
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Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
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Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
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Besson FL, Treglia G, Bucerius J, Anagnostopoulos C, Buechel RR, Dweck MR, Erba PA, Gaemperli O, Gimelli A, Gheysens O, Glaudemans AWJM, Habib G, Hyafil F, Lubberink M, Rischpler C, Saraste A, Slart RHJA. A systematic review for the evidence of recommendations and guidelines in hybrid nuclear cardiovascular imaging. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06597-x. [PMID: 38221570 DOI: 10.1007/s00259-024-06597-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/01/2024] [Indexed: 01/16/2024]
Abstract
OBJECTIVES This study aimed to evaluate the level of evidence of expert recommendations and guidelines for clinical indications and procedurals in hybrid nuclear cardiovascular imaging. METHODS From inception to August 2023, a PubMed literature analysis of the latest version of guidelines for clinical hybrid cardiovascular imaging techniques including SPECT(/CT), PET(/CT), and PET(/MRI) was performed in two categories: (1) for clinical indications for all-in primary diagnosis; subgroup in prognosis and therapy evaluation; and for (2) imaging procedurals. We surveyed to what degree these followed a standard methodology to collect the data and provide levels of evidence, and for which topic systematic review evidence was executed. RESULTS A total of 76 guidelines, published between 2013 and 2023, were included. The evidence of guidelines was based on systematic reviews in 7.9% of cases, non-systematic reviews in 47.4% of cases, a mix of systematic and non-systematic reviews in 19.7%, and 25% of guidelines did not report any evidence. Search strategy was reported in 36.8% of cases. Strengths of recommendation were clearly reported in 25% of guidelines. The notion of external review was explicitly reported in 23.7% of cases. Finally, the support of a methodologist was reported in 11.8% of the included guidelines. CONCLUSION The use of evidence procedures for developing for evidence-based cardiovascular hybrid imaging recommendations and guidelines is currently suboptimal, highlighting the need for more standardized methodological procedures.
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Affiliation(s)
- Florent L Besson
- Department of Nuclear Medicine-Molecular Imaging, DMU SMART IMAGING, Hôpitaux Universitaires Paris-Saclay, AP-HP, CHU Bicêtre, Le Kremlin Bicetre, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicetre, France
- Commissariat À L'énergie Atomique Et Aux Énergies Alternatives (CEA), Centre National de La Recherche Scientifique (CNRS), Inserm, BioMaps, Université Paris-Saclay, Le Kremlin-Bicetre, France
| | - Giorgio Treglia
- Division of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6501, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900, Lugano, Switzerland
| | - Jan Bucerius
- Department of Nuclear Medicine, Georg-August University Göttingen, Universitätsmedizin Göttingen, Gottingen, Germany
| | | | - Ronny R Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh Heart Centre, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh, UK
| | - Paula A Erba
- Department of Medicine and Surgery, University of Milan Bicocca, and Nuclear Medicine Unit ASST Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | | | | | - Olivier Gheysens
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc, Institut Roi Albert II, Université Catholique de Louvain, 1200, Brussels, Belgium
| | - Andor W J M Glaudemans
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Gilbert Habib
- Department of Cardiology, APHM, La Timone Hospital, Marseille, France
| | - Fabian Hyafil
- Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F75015, Paris, France
| | - Mark Lubberink
- Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden
| | | | - Antti Saraste
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Riemer H J A Slart
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands.
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7
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Panagiotidis E, Papachristou K, Makridou A, Zoglopitou LA, Paschali A, Kalathas T, Chatzimarkou M, Chatzipavlidou V. Review of artificial intelligence clinical applications in Nuclear Medicine. Nucl Med Commun 2024; 45:24-34. [PMID: 37901920 DOI: 10.1097/mnm.0000000000001786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer's disease and Parkinson's disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.
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Affiliation(s)
| | | | - Anna Makridou
- Medical Physics Department, Cancer Hospital of Thessaloniki 'Theagenio', Thessaloniki, Greece
| | | | - Anna Paschali
- Nuclear Medicine Department, Cancer Hospital of Thessaloniki 'Theagenio' and
| | - Theodoros Kalathas
- Nuclear Medicine Department, Cancer Hospital of Thessaloniki 'Theagenio' and
| | - Michael Chatzimarkou
- Medical Physics Department, Cancer Hospital of Thessaloniki 'Theagenio', Thessaloniki, Greece
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Glaudemans AWJM, Dierckx RAJO, Scheerder B, Niessen WJ, Pruim J, Dewi DEO, Borra RJH, Lammertsma AA, Tsoumpas C, Slart RHJA. The first international network symposium on artificial intelligence and informatics in nuclear medicine: "The bright future of nuclear medicine is illuminated by artificial intelligence". Eur J Nucl Med Mol Imaging 2024; 51:336-339. [PMID: 37962619 DOI: 10.1007/s00259-023-06507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Affiliation(s)
- Andor W J M Glaudemans
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Bart Scheerder
- Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Wiro J Niessen
- Board of Directors, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan Pruim
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Dyah E O Dewi
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Ronald J H Borra
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Adriaan A Lammertsma
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Charalampos Tsoumpas
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Riemer H J A Slart
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging group, University of Twente, Enschede, The Netherlands
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Tingen HSA, van Praagh GD, Nienhuis PH, Tubben A, van Rijsewijk ND, ten Hove D, Mushari NA, Martinez-Lucio TS, Mendoza-Ibañez OI, van Sluis J, Tsoumpas C, Glaudemans AW, Slart RH. The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine. Br J Radiol 2023; 96:20230704. [PMID: 37786997 PMCID: PMC10646628 DOI: 10.1259/bjr.20230704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 10/04/2023] Open
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide and have an increasing impact on society. Precision medicine, in which optimal care is identified for an individual or a group of individuals rather than for the average population, might provide significant health benefits for this patient group and decrease CVD morbidity and mortality. Molecular imaging provides the opportunity to assess biological processes in individuals in addition to anatomical context provided by other imaging modalities and could prove to be essential in the implementation of precision medicine in CVD. New developments in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) systems, combined with rapid innovations in promising and specific radiopharmaceuticals, provide an impressive improvement of diagnostic accuracy and therapy evaluation. This may result in improved health outcomes in CVD patients, thereby reducing societal impact. Furthermore, recent technical advances have led to new possibilities for accurate image quantification, dynamic imaging, and quantification of radiotracer kinetics. This potentially allows for better evaluation of disease activity over time and treatment response monitoring. However, the clinical implementation of these new methods has been slow. This review describes the recent advances in molecular imaging and the clinical value of quantitative PET and SPECT in various fields in cardiovascular molecular imaging, such as atherosclerosis, myocardial perfusion and ischemia, infiltrative cardiomyopathies, systemic vascular diseases, and infectious cardiovascular diseases. Moreover, the challenges that need to be overcome to achieve clinical translation are addressed, and future directions are provided.
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Affiliation(s)
- Hendrea Sanne Aletta Tingen
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Pieter H. Nienhuis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Alwin Tubben
- Department of Cardiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - Nick D. van Rijsewijk
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Derk ten Hove
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Nouf A. Mushari
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - T. Samara Martinez-Lucio
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Oscar I. Mendoza-Ibañez
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | | | - Andor W.J.M. Glaudemans
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
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Motwani M. 2022 Artificial intelligence primer for the nuclear cardiologist. J Nucl Cardiol 2023; 30:2441-2453. [PMID: 35854041 DOI: 10.1007/s12350-022-03049-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 06/14/2022] [Indexed: 10/17/2022]
Abstract
Driven by advances in computing power, the past decade has seen rapid developments in artificial intelligence (AI) which now offers potential enhancements to every aspect of nuclear cardiology workflow including acquisition, reconstruction, segmentation, direct image analysis, and interpretation; as well as facilitating clinical and imaging big-data integration for superior personalized risk stratification. To understand the relevance and potential of AI in their field, this review provides a primer for nuclear cardiologists in 2022. The aim is to explain terminology and provide a summary of key current implementations, challenges, and future aspirations of AI-based enhancements to nuclear cardiology.
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Affiliation(s)
- Manish Motwani
- Department of Cardiology, Manchester Heart Institute, Manchester Royal Infirmary, Manchester Heart Centre, Manchester University NHS Foundation Trust, Oxford Road, Manchester, UK.
- Institute of Cardiovascular Science, University of Manchester, Manchester, UK.
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Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:586-596. [PMID: 38223683 PMCID: PMC10781930 DOI: 10.1007/s43657-023-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.
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Affiliation(s)
- Junhao Li
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Guifen Yang
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
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12
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Gao L, Yusufaly TI, Williamson CW, Mell LK. Optimized Atlas-Based Auto-Segmentation of Bony Structures from Whole-Body Computed Tomography. Pract Radiat Oncol 2023; 13:e442-e450. [PMID: 37030539 DOI: 10.1016/j.prro.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 04/09/2023]
Abstract
PURPOSE To develop and test a method for fully automated segmentation of bony structures from whole-body computed tomography (CT) and evaluate its performance compared with manual segmentation. METHODS AND MATERIALS We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation (ABS) method with a postprocessing module (ABSPP) in MIM MAESTRO software. Fifty-two CT scans comprised the training set to build the atlas library, and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement, and relative volume errors between ABSPP and ABS with no postprocessing (ABSNPP) with manual segmentation as the reference (gold standard). RESULTS The ABSPP method resulted in significantly improved segmentation accuracy (DSC range, 0.85-0.98) compared with the ABSNPP method (DSC range, 0.55-0.87; P < .001). Mean distance to agreement results also indicated high agreement between ABSPP and manual reference delineations (range, 0.11-1.56 mm), which was significantly improved compared with ABSNPP (range, 1.00-2.34 mm) for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared with ABSNPP for most bony structures. CONCLUSIONS We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared with manual delineation. The integrated postprocessing module significantly improved workflow performance.
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Affiliation(s)
- Lei Gao
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Tahir I Yusufaly
- Russell H. Morgan Department of Radiology and Radiologic Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland
| | - Casey W Williamson
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, Oregon
| | - Loren K Mell
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
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13
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van Dalen JA, Koenders SS, Metselaar RJ, Vendel BN, Slotman DJ, Mouden M, Slump CH, van Dijk JD. Machine learning based model to diagnose obstructive coronary artery disease using calcium scoring, PET imaging, and clinical data. J Nucl Cardiol 2023; 30:1504-1513. [PMID: 36622542 DOI: 10.1007/s12350-022-03166-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/15/2022] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Accurate risk stratification in patients with suspected stable coronary artery disease is essential for choosing an appropriate treatment strategy. Our aim was to develop and validate a machine learning (ML) based model to diagnose obstructive CAD (oCAD). METHOD We retrospectively have included 1007 patients without a prior history of CAD who underwent CT-based calcium scoring (CACS) and a Rubidium-82 PET scan. The entire dataset was split 4:1 into a training and test dataset. An ML model was developed on the training set using fivefold stratified cross-validation. The test dataset was used to compare the performance of expert readers to the model. The primary endpoint was oCAD on invasive coronary angiography (ICA). RESULTS ROC curve analysis showed an AUC of 0.92 (95% CI 0.90-0.94) for the training dataset and 0.89 (95% CI 0.84-0.93) for the test dataset. The ML model showed no significant differences as compared to the expert readers (p ≥ 0.03) in accuracy (89% vs. 88%), sensitivity (68% vs. 69%), and specificity (92% vs. 90%). CONCLUSION The ML model resulted in a similar diagnostic performance as compared to expert readers, and may be deployed as a risk stratification tool for obstructive CAD. This study showed that utilization of ML is promising in the diagnosis of obstructive CAD.
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Affiliation(s)
- J A van Dalen
- Department of Medical Physics, Isala Hospital, PO Box 10400, 8000 GK, Zwolle, The Netherlands.
| | - S S Koenders
- Department of Nuclear Medicine, Isala Hospital, Zwolle, The Netherlands
- Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - R J Metselaar
- Department of Nuclear Medicine, Isala Hospital, Zwolle, The Netherlands
- Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - B N Vendel
- Department of Nuclear Medicine, Isala Hospital, Zwolle, The Netherlands
| | - D J Slotman
- Department of Radiology, Isala Hospital, Zwolle, The Netherlands
| | - M Mouden
- Department of Cardiology, Isala Hospital, Zwolle, The Netherlands
| | - C H Slump
- Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - J D van Dijk
- Department of Nuclear Medicine, Isala Hospital, Zwolle, The Netherlands
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14
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Pierre K, Haneberg AG, Kwak S, Peters KR, Hochhegger B, Sananmuang T, Tunlayadechanont P, Tighe PJ, Mancuso A, Forghani R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin Roentgenol 2023; 58:158-169. [PMID: 37087136 DOI: 10.1053/j.ro.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 04/24/2023]
Abstract
There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Adam G Haneberg
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Sean Kwak
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL
| | - Keith R Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Thiparom Sananmuang
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Padcha Tunlayadechanont
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Patrick J Tighe
- Departments of Anesthesiology & Orthopaedic Surgery, University of Florida College of Medicine, Gainesville, FL
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
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15
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Ilyushenkova J, Sazonova S, Popov E, Batalov R, Minin S, Romanov A. Radiomic Phenotype of Periatrial Adipose Tissue in the Prognosis of Late Postablation Recurrence of Idiopathic Atrial Fibrillation. Sovrem Tekhnologii Med 2023; 15:48-58. [PMID: 37389017 PMCID: PMC10306967 DOI: 10.17691/stm2023.15.2.05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Indexed: 07/01/2023] Open
Abstract
The aim of the study is to find new predictors of postablation atrial fibrillation (AF) recurrence in patients with lone AF using a texture analysis of the periatrial adipose tissue (PAAT) of the left atrium. Materials and Methods Forty-three patients admitted for lone AF catheter ablation, who had undergone multispiral coronary angiography, were enrolled in the study. PAAT segmentation was performed using 3D Slicer application followed by extraction of 93 radiomic features. At the end of the follow-up period, patients were divided into 2 groups depending on the presence or absence of AF recurrence. Results 12 months of follow-up after catheter ablation, postablation AF recurrence was reported in 19 out of 43 patients. Of 93 extracted radiomic features of PAAT, statistically significant differences were observed for 3 features of the Gray Level Size Zone matrix. At the same time, only one radiomic feature of PAAT, Size Zone Non Uniformity Normalized, was an independent predictor of postablative recurrence of AF after catheter ablation and 12 months of follow-up (McFadden's R2=0.451, OR - 0.506, 95% CI: 0.331‒0.776, p<0.001). Conclusion The radiomic analysis of periatrial adipose tissue may be considered as a promising non-invasive method for predicting adverse outcomes of the catheter treatment, which opens the possibilities for planning and correction of patient management tactics after intervention.
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Affiliation(s)
- J.N. Ilyushenkova
- Senior Researcher, Nuclear Medicine Department; Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 111a Kievskaya St., Tomsk, 634012, Russia
| | - S.I. Sazonova
- Head of Nuclear Medicine Department; Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 111a Kievskaya St., Tomsk, 634012, Russia
| | - E.V. Popov
- PhD Student, Nuclear Medicine Department; Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 111a Kievskaya St., Tomsk, 634012, Russia
| | - R.E. Batalov
- Leading Researcher, Department of Surgical Treatment of Advanced Heart Rhythm Disorders and Pacing; Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 111a Kievskaya St., Tomsk, 634012, Russia
| | - S.M. Minin
- Head of the Nuclear Diagnosis Unit, Department of Radiological and Functional Diagnosis; Meshalkin National Medical Research Center of the Ministry of Health of the Russian Federation, 15 Rechkunovskaya St., 630055, Novosibirsk, Russia
| | - A.B. Romanov
- Deputy Director for Science; Meshalkin National Medical Research Center of the Ministry of Health of the Russian Federation, 15 Rechkunovskaya St., 630055, Novosibirsk, Russia
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16
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Régis C, Benali K, Rouzet F. FDG PET/CT Imaging of Sarcoidosis. Semin Nucl Med 2023; 53:258-272. [PMID: 36870707 DOI: 10.1053/j.semnuclmed.2022.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/11/2022]
Abstract
Sarcoidosis is a multisystemic granulomatous disease of unknown etiology. The diagnostic can be made by histological identification of non-caseous granuloma or by a combination of clinical criteria. Active inflammatory granuloma can lead to fibrotic damage. Although 50% of cases resolve spontaneously, systemic treatments are often necessary to decrease symptoms and avoid permanent organ dysfunction, notably in cardiac sarcoidosis. The course of the disease can be punctuated by exacerbations and relapses and the prognostic depends mainly on affected sites and patient management. FDG-PET/CT along with newer FDG-PET/MR have emerged as key imaging modalities in sarcoidosis, namely for certain diagnostic purposes, staging and biopsy guiding. By identifying with a high sensitivity inflammatory active granuloma, FDG hybrid imaging is a main prognostic tool and therapeutic ally in sarcoidosis. This review aims to highlight the actual critical roles of hybrid PET imaging in sarcoidosis and display a brief perspective for the future which appears to include other radiotracers and artificial intelligence applications.
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Affiliation(s)
- Claudine Régis
- Nuclear medicine department, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France.; Department of Medical Imaging, Institut de Cardiologie de Montréal, Université de Montréal, Montréal, Québec, Canada
| | - Khadija Benali
- Nuclear medicine department, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France.; Université Paris Cité and Inserm U1148, Paris, France
| | - François Rouzet
- Nuclear medicine department, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France.; Université Paris Cité and Inserm U1148, Paris, France..
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17
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Chamberlin JH, Smith C, Schoepf UJ, Nance S, Elojeimy S, O'Doherty J, Baruah D, Burt JR, Varga-Szemes A, Kabakus IM. A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT. Clin Radiol 2023; 78:e368-e376. [PMID: 36863883 DOI: 10.1016/j.crad.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/19/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023]
Abstract
AIM To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked. MATERIALS AND METHODS One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated. RESULTS The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1. CONCLUSION The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.
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Affiliation(s)
- J H Chamberlin
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - C Smith
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - U J Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - S Nance
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - S Elojeimy
- Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - J O'Doherty
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Siemens Medical Solutions, Malvern, PA, USA
| | - D Baruah
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - J R Burt
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - A Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - I M Kabakus
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
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18
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Saboury B, Bradshaw T, Boellaard R, Buvat I, Dutta J, Hatt M, Jha AK, Li Q, Liu C, McMeekin H, Morris MA, Scott PJH, Siegel E, Sunderland JJ, Pandit-Taskar N, Wahl RL, Zuehlsdorff S, Rahmim A. Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem. J Nucl Med 2023; 64:188-196. [PMID: 36522184 PMCID: PMC9902852 DOI: 10.2967/jnumed.121.263703] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.
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Affiliation(s)
- Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland;
| | - Tyler Bradshaw
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Irène Buvat
- Institut Curie, Université PSL, INSERM, Université Paris-Saclay, Orsay, France
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, Massachusetts
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Abhinav K Jha
- Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut
| | - Helena McMeekin
- Department of Clinical Physics, Barts Health NHS Trust, London, United Kingdom
| | - Michael A Morris
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Peter J H Scott
- Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Eliot Siegel
- Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Maryland
| | - John J Sunderland
- Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa
| | - Neeta Pandit-Taskar
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri
| | - Sven Zuehlsdorff
- Siemens Medical Solutions USA, Inc., Hoffman Estates, Illinois; and
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
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Counseller Q, Aboelkassem Y. Recent technologies in cardiac imaging. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:984492. [PMID: 36704232 PMCID: PMC9872125 DOI: 10.3389/fmedt.2022.984492] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023] Open
Abstract
Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.
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Affiliation(s)
- Quinn Counseller
- College of Health Sciences, University of Michigan, Flint, MI, United States
| | - Yasser Aboelkassem
- College of Innovation and Technology, University of Michigan, Flint, MI, United States,Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States,Correspondence: Yasser Aboelkassem
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20
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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21
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Duff L, Scarsbrook AF, Mackie SL, Frood R, Bailey M, Morgan AW, Tsoumpas C. A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis. J Nucl Cardiol 2022; 29:3315-3331. [PMID: 35322380 PMCID: PMC9834376 DOI: 10.1007/s12350-022-02927-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 01/05/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND The aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. METHODS The aorta was manually segmented on FDG PET-CT in 50 patients with aortitis and 25 controls. Radiomic features (RF) (n = 107), including SUV (Standardized Uptake Value) metrics, were extracted from the segmented data and harmonized using the ComBat technique. Individual RFs and groups of RFs (i.e., signatures) were used as input in Machine Learning classifiers. The diagnostic utility of these classifiers was evaluated with area under the receiver operating characteristic curve (AUC) and accuracy using the clinical diagnosis as the ground truth. RESULTS Several RFs had high accuracy, 84% to 86%, and AUC scores 0.83 to 0.97 when used individually. Radiomic signatures performed similarly, AUC 0.80 to 1.00. CONCLUSION A methodological framework for a radiomic-based approach to support diagnosis of aortitis was outlined. Selected RFs, individually or in combination, showed similar performance to the current standard of qualitative assessment in terms of AUC for identifying active aortitis. This framework could support development of a clinical decision-making tool for a more objective and standardized assessment of aortitis.
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Affiliation(s)
- Lisa Duff
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK.
- Institute of Medical and Biological Engineering, University of Leeds, Leeds, UK.
| | - Andrew F Scarsbrook
- Leeds Institute of Medical Research - St James's, University of Leeds, Leeds, UK
- Department of Radiology, St. James University Hospital, Leeds, UK
| | - Sarah L Mackie
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Biomedical Research Centre, NIHR Leeds, Leeds, UK
| | - Russell Frood
- Leeds Institute of Medical Research - St James's, University of Leeds, Leeds, UK
- Department of Radiology, St. James University Hospital, Leeds, UK
| | - Marc Bailey
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds, UK
| | - Ann W Morgan
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- Leeds Teaching Hospitals NHS Trust, Biomedical Research Centre, NIHR Leeds, Leeds, UK
| | - Charalampos Tsoumpas
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- Icahn School of Medicine at Mount Sinai, Biomedical Engineering and Imaging Institute, New York, USA
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center of Groningen, University of Groningen, 9700 RB, Groningen, Netherlands
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22
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Massalha S, Keidar Z. Image fusion: the beauty of the truth from the inside and out. J Nucl Cardiol 2022; 29:3278-3280. [PMID: 35381963 DOI: 10.1007/s12350-022-02955-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/06/2022] [Indexed: 01/14/2023]
Affiliation(s)
- Samia Massalha
- Department of Cardiology, Rambam Health Care Campus, Haifa, Israel
| | - Zohar Keidar
- Department of Nuclear Medicine, Rambam Health Care Campus, Haifa, Israel.
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23
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Wu F, Liu X, Wang Y, Li X, Zhou M. Application of medical clinic system using improved neural network-based image segmentation technique. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07913-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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24
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Zhang X, Sun T, Liu E, Xu W, Wang S, Wang Q. Development and evaluation of a radiomics model of resting 13N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1167. [PMID: 36467349 PMCID: PMC9708489 DOI: 10.21037/atm-22-4692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2023]
Abstract
BACKGROUND Coronary angiography (CAG) is usually performed in patients with coronary heart disease (CHD) to evaluate the coronary artery stenosis. However, patients with iodine allergy and renal dysfunction are not suitable for CAG. We try to develop a radiomics machine learning model based on rest 13N-ammonia (13N-NH3) positron emission tomography (PET) myocardial perfusion imaging (MPI) to predict coronary stenosis. METHODS Eighty-four patients were included with the inclusion criteria: adult patients; suspected CHD; resting MPI and CAG were performed; and complete data. Coronary artery stenosis >75% were considered to be significant stenosis. Patients were randomly divided into a training group and a testing group with a ratio of 1:1. Myocardial blood flow (MBF), perfusion defect extent (EXT), total perfusion deficit (TPD), and summed rest score (SRS) were obtained. Myocardial static images of the left ventricular (LV) coronary segments were segmented, and radiomics features were extracted. In the training set, the conventional parameter (MPI model) and radiomics (Rad model) models were constructed using the machine learning method and were combined to construct a nomogram. The models' performance was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, decision analysis curve (DCA), and calibration curves. Testing and subgroup analysis were performed. RESULTS MPI model was composed of MBF and EXT, and Rad model was composed of 12 radiomics features. In the training set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.795/0.778/0.937/0.511, 0.912/0.825/0.760/0.936 and 0.911/0.865/0.924/0.766 respectively. In the testing set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.798/0.722/0.659/0.841, 0.887/0.810/0.744/0.932 and 0.900/0.849/0.854/0.841 respectively. The AUC of Rad model and nomogram were significantly higher than that of MPI model. The DCA curve also showed that the clinical net benefit of the Rad model and nomogram was similar but greater than that of MPI model. The calibration curve showed good agreement between the observed and predicted values of the Rad model. In the subgroup analysis of Rad model, there was no significant difference in AUC between subgroups. CONCLUSIONS The Rad model is more accurate than the MPI model in predicting coronary stenosis. This noninvasive technique could help improve risk stratification and had good generalization ability.
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Affiliation(s)
- Xiaochun Zhang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Taotao Sun
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Entao Liu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Weiping Xu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shuxia Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
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25
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Erba PA, Israel O. Preparing for the next vintage in IE. J Nucl Cardiol 2022; 29:2195-2196. [PMID: 34331218 DOI: 10.1007/s12350-021-02746-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 06/30/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Paola Anna Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technology in Medicine, University of Pisa, and Azienda Ospedaliero Universitaria Pisana, Via Savi 10, 56126, Pisa, Italy.
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Centre, University Medical Center Groningen, Groningen, The Netherlands.
| | - Ora Israel
- Rappaport, Faculty of Medicine Technion, Haifa, Israel
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26
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Schwenck J, Kneilling M, Riksen NP, la Fougère C, Mulder DJ, Slart RJHA, Aarntzen EHJG. A role for artificial intelligence in molecular imaging of infection and inflammation. Eur J Hybrid Imaging 2022; 6:17. [PMID: 36045228 PMCID: PMC9433558 DOI: 10.1186/s41824-022-00138-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/16/2022] [Indexed: 12/03/2022] Open
Abstract
The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [18F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.
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27
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Hustinx R, Pruim J, Lassmann M, Visvikis D. An EANM position paper on the application of artificial intelligence in nuclear medicine. Eur J Nucl Med Mol Imaging 2022; 50:61-66. [PMID: 36006443 DOI: 10.1007/s00259-022-05947-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 08/16/2022] [Indexed: 11/04/2022]
Abstract
Artificial intelligence (AI) is coming into the field of nuclear medicine, and it is likely here to stay. As a society, EANM can and must play a central role in the use of AI in nuclear medicine. In this position paper, the EANM explains the preconditions for the implementation of AI in NM and takes position.
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Affiliation(s)
- Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège & GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
| | - Jan Pruim
- Medical Imaging Center, Dept. of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Michael Lassmann
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
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28
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Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Front Cardiovasc Med 2022; 9:890809. [PMID: 35935648 PMCID: PMC9354141 DOI: 10.3389/fcvm.2022.890809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/13/2022] [Indexed: 12/02/2022] Open
Abstract
Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by “learning” medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.
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29
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Visvikis D, Lambin P, Beuschau Mauridsen K, Hustinx R, Lassmann M, Rischpler C, Shi K, Pruim J. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. Eur J Nucl Med Mol Imaging 2022; 49:4452-4463. [PMID: 35809090 PMCID: PMC9606092 DOI: 10.1007/s00259-022-05891-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/25/2022] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.
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Affiliation(s)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands
| | - Kim Beuschau Mauridsen
- Center of Functionally Integrative Neuroscience and MindLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Roland Hustinx
- GIGA-CRC in Vivo Imaging, University of Liège, GIGA, Avenue de l'Hôpital 11, 4000, Liege, Belgium
| | - Michael Lassmann
- Klinik Und Poliklinik Für Nuklearmedizin, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jan Pruim
- Medical Imaging Center, Dept. of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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30
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Papandrianos NI, Feleki A, Papageorgiou EI, Martini C. Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images. J Clin Med 2022; 11:jcm11133918. [PMID: 35807203 PMCID: PMC9267142 DOI: 10.3390/jcm11133918] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 12/12/2022] Open
Abstract
(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model's reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.
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Affiliation(s)
- Nikolaos I. Papandrianos
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece; (A.F.); (E.I.P.)
- Correspondence: ; Tel.: +30-693-6064613
| | - Anna Feleki
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece; (A.F.); (E.I.P.)
| | - Elpiniki I. Papageorgiou
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece; (A.F.); (E.I.P.)
| | - Chiara Martini
- Department of Diagnostic, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy;
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Maggiore Hospital, Via Gramsci 14, 43125 Parma, Italy
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31
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Gargani L, Baldini M, Berchiolli R, Bort IR, Casolo G, Chiappino D, Cosottini M, D'Angelo G, De Santis M, Erba P, Fabiani I, Fabiani P, Gabbriellini I, Galeotti GG, Ghicopulos I, Goncalves I, Lapi S, Masini G, Morizzo C, Napoli V, Nilsson J, Orlandi G, Palombo C, Pieraccini F, Ricci S, Siciliano G, Slart RHJA, De Caterina R. Detecting the vulnerable carotid plaque: the Carotid Artery Multimodality imaging Prognostic study design. J Cardiovasc Med (Hagerstown) 2022; 23:466-473. [PMID: 35763768 DOI: 10.2459/jcm.0000000000001314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
BACKGROUND Carotid artery disease is highly prevalent and a main cause of ischemic stroke and vascular dementia. There is a paucity of information on predictors of serious vascular events. Besides percentage diameter stenosis, international guidelines also recommend the evaluation of qualitative characteristics of carotid artery disease as a guide to treatment, but with no agreement on which qualitative features to assess. This inadequate knowledge leads to a poor ability to identify patients at risk, dispersion of medical resources, and unproven use of expensive and resource-consuming techniques, such as magnetic resonance imaging, positron emission tomography, and computed tomography. OBJECTIVES The Carotid Artery Multimodality imaging Prognostic (CAMP) study will: prospectively determine the best predictors of silent and overt ischemic stroke and vascular dementia in patients with asymptomatic subcritical carotid artery disease by identifying the noninvasive diagnostic features of the 'vulnerable carotid plaque'; assess whether 'smart' use of low-cost diagnostic methods such as ultrasound-based evaluations may yield at least the same level of prospective information as more expensive techniques. STUDY DESIGN We will compare the prognostic/predictive value of all proposed techniques with regard to silent or clinically manifest ischemic stroke and vascular dementia. The study will include ≥300 patients with asymptomatic, unilateral, intermediate degree (40-60% diameter) common or internal carotid artery stenosis detected at carotid ultrasound, with a 2-year follow-up. The study design has been registered on Clinicaltrial.gov on December 17, 2020 (ID number NCT04679727).
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Affiliation(s)
- Luna Gargani
- Institute of Clinical Physiology, National Research Council
| | | | - Raffaella Berchiolli
- Vascular Surgery Unit, Cardio Thoracic and Vascular Department, University of Pisa
| | | | | | | | | | | | - Mariella De Santis
- Cardiology Unit, Cardio-Thoracic and Vascular Department, University of Pisa, Pisa, Italy
| | - Paola Erba
- Department of Nuclear Medicine, University of Pisa, Pisa, Italy
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Plinio Fabiani
- Internal Medicine, S.M. Annunziata Hospital, Florence, Italy
| | - Ilaria Gabbriellini
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Gian Giacomo Galeotti
- Cardiology Unit, Cardio-Thoracic and Vascular Department, University of Pisa, Pisa, Italy
| | - Irene Ghicopulos
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Isabel Goncalves
- Department of Clinical Sciences - Malmö University Hospital, University of Lund, Malmö, Sweden
| | - Simone Lapi
- BMS Multispecialistic Biobank-Biobank Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Gabriele Masini
- Cardiology Unit, Cardio-Thoracic and Vascular Department, University of Pisa, Pisa, Italy
| | - Carmela Morizzo
- Cardiology Unit, Cardio-Thoracic and Vascular Department, University of Pisa, Pisa, Italy
| | - Vinicio Napoli
- Diagnostic and Interventional Radiology, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Jan Nilsson
- Department of Clinical Sciences - Malmö University Hospital, University of Lund, Malmö, Sweden
| | - Giovanni Orlandi
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Carlo Palombo
- Cardiology Unit, Cardio-Thoracic and Vascular Department, University of Pisa, Pisa, Italy
| | | | - Stefano Ricci
- Department of Information Engineering (DINFO), University of Florence, Florence, Italy
| | - Gabriele Siciliano
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands
| | - Raffaele De Caterina
- Cardiology Unit, Cardio-Thoracic and Vascular Department, University of Pisa, Pisa, Italy
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32
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Cardiovascular Computed Tomography in Pediatric Congenital Heart Disease: A State of the Art Review. J Cardiovasc Comput Tomogr 2022; 16:467-482. [DOI: 10.1016/j.jcct.2022.04.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 01/04/2023]
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33
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Teuho J, Schultz J, Klén R, Knuuti J, Saraste A, Ono N, Kanaya S. Classification of ischemia from myocardial polar maps in 15O-H 2O cardiac perfusion imaging using a convolutional neural network. Sci Rep 2022; 12:2839. [PMID: 35181681 PMCID: PMC8857225 DOI: 10.1038/s41598-022-06604-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 02/03/2022] [Indexed: 12/02/2022] Open
Abstract
We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from 15O–H2O stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation. The classification accuracy was evaluated with: accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE) and precision (PRE). The CNN had a median ACC of 0.8261, AUC of 0.8058, F1S of 0.7647, SEN of 0.6500, SPE of 0.9615 and PRE of 0.9286. In comparison, clinical interpretation had ACC of 0.8696, AUC of 0.8558, F1S of 0.8333, SEN of 0.7500, SPE of 0.9615 and PRE of 0.9375. The CNN classified only 2 cases differently than the clinical interpretation. The clinical interpretation and CNN had similar accuracy in classifying false positives and true negatives. Classification of ischemia is feasible in 15O–H2O stress perfusion imaging using JPEG polar maps alone with a custom CNN and may be useful for the detection of obstructive CAD.
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Affiliation(s)
- Jarmo Teuho
- Data Science Center, Nara University of Science and Technology, Nara, Japan. .,Turku PET Centre, University of Turku, Turku, Finland. .,Turku PET Centre, Turku University Hospital, Turku, Finland.
| | - Jussi Schultz
- Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Juhani Knuuti
- Turku PET Centre, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, Turku, Finland.,Heart Centre, Turku University Hospital and University of Turku, Turku, Finland
| | - Naoaki Ono
- Data Science Center, Nara University of Science and Technology, Nara, Japan.,Department of Science and Technology, Nara University of Science and Technology, Nara, Japan
| | - Shigehiko Kanaya
- Department of Science and Technology, Nara University of Science and Technology, Nara, Japan
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34
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Juarez-Orozco LE, Klén R, Niemi M, Ruijsink B, Daquarti G, van Es R, Benjamins JW, Yeung MW, van der Harst P, Knuuti J. Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies. Curr Cardiol Rep 2022; 24:307-316. [PMID: 35171443 PMCID: PMC8852880 DOI: 10.1007/s11886-022-01649-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/17/2021] [Indexed: 12/28/2022]
Abstract
Purpose of Review As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. Recent Findings and Summary There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. Graphical Abstract AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines ![]()
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Affiliation(s)
- Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.,Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Mikael Niemi
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Bram Ruijsink
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas' Hospital, London, UK
| | - Gustavo Daquarti
- Department of Artificial Intelligence, UMA-Health, Buenos Aires, Argentina
| | - Rene van Es
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jan-Walter Benjamins
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ming Wai Yeung
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.
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35
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Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
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Decuyper M, Maebe J, Van Holen R, Vandenberghe S. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 2021; 8:81. [PMID: 34897550 PMCID: PMC8665861 DOI: 10.1186/s40658-021-00426-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/19/2021] [Indexed: 12/19/2022] Open
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
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Affiliation(s)
- Milan Decuyper
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Maebe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Roel Van Holen
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
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Estimation of Nuclear Medicine Exposure Measures Based on Intelligent Computer Processing. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4102183. [PMID: 34616531 PMCID: PMC8490043 DOI: 10.1155/2021/4102183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 11/18/2022]
Abstract
This paper provides an in-depth discussion and analysis of the estimation of nuclear medicine exposure measurements using computerized intelligent processing. The focus is on the study of energy extraction algorithms to obtain a high energy resolution with the lowest possible ADC sampling rate and thus reduce the amount of data. This paper focuses on the direct pulse peak extraction algorithm, polynomial curve fitting algorithm, double exponential function curve fitting algorithm, and pulse area calculation algorithm. The detector output waveforms are obtained with an oscilloscope, and the analysis module is designed in MATLAB. Based on these algorithms, the data obtained from six different lower sampling rates are analyzed and compared with the results of the high sampling rate direct pulse peak extraction algorithm and the pulse area calculation algorithm, respectively. The correctness of the compartment model was checked, and the results were found to be realistic and reliable, which can be used for the analysis of internal exposure data in radiation occupational health management, estimation of internal exposure dose for nuclear emergency groups, and estimation of accidental internal exposure dose. The results of the compartment model of the respiratory tract and the compartment model of the digestive tract can be used to calculate the distribution and retention patterns of radionuclides and their compounds in the body, which can be used to assess the damage of radionuclide internal contamination and guide the implementation of medical treatment.
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Wang J, Fan X, Qin S, Shi K, Zhang H, Yu F. Exploration of the efficacy of radiomics applied to left ventricular tomograms obtained from D-SPECT MPI for the auxiliary diagnosis of myocardial ischemia in CAD. Int J Cardiovasc Imaging 2021; 38:465-472. [PMID: 34591200 DOI: 10.1007/s10554-021-02413-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/09/2021] [Indexed: 11/26/2022]
Abstract
To explore the feasibility and efficacy of radiomics with left ventricular tomograms obtained from D-SPECT myocardial perfusion imaging (MPI) for auxiliary diagnosis of myocardial ischemia in coronary artery disease (CAD). The images of 103 patients with CAD myocardial ischemia between September 2020 and April 2021 were retrospectively selected. After information desensitization processing, format conversion, annotation using the Labelme tool on an open-source platform, lesion classification, and establishment of a database, the images were cropped for analysis. The ResNet18 model was used to automate two steps (classification and segmentation) with five randomization, training and validation steps. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, Youden's index, agreement rate, and kappa value were calculated as evaluation indexes of the classification results for each training-validation step; then, receiver operating characteristics (ROC) curves were drawn, and the areas under the curve (AUCs) were calculated. The Dice coefficient, intersection over union, and Hausdorff distance were calculated as evaluation indexes of the segmentation results for each training-validation step; then, the predicted images were exported. Under the existing conditions, the radiomics model used in this study had an AUC above 0.95 in identifying the presence or absence of myocardial ischemia; in the prediction of the extent of myocardial ischemia, its evaluation index distribution is also close to that of the gold standard. Radiomics can be feasibly applied to left ventricular tomograms obtained from D-SPECT MPI for auxiliary diagnosis. With more in-depth research and the development of technology, adding this method to the existing auxiliary diagnosis will likely further improve the diagnostic accuracy and efficiency, and patients will therefore benefit.
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Affiliation(s)
- Junpeng Wang
- Medical College, Anhui University of Science and Technology, Taifeng RD. 168, Huainan, 232001, People's Republic of China
| | - Xin Fan
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Yanchang RD. 301, Shanghai, 200072, People's Republic of China
| | - ShanShan Qin
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Yanchang RD. 301, Shanghai, 200072, People's Republic of China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Han Zhang
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Yanchang RD. 301, Shanghai, 200072, People's Republic of China.
| | - Fei Yu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Yanchang RD. 301, Shanghai, 200072, People's Republic of China.
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Garcia EV. Artificial intelligence in nuclear cardiology: Preparing for the fifth industrial revolution. J Nucl Cardiol 2021; 28:1199-1202. [PMID: 34342863 DOI: 10.1007/s12350-021-02671-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 05/05/2021] [Indexed: 01/28/2023]
Affiliation(s)
- Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 101 Woodruff Circle, Room 1203, Atlanta, GA, 30322, USA.
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Automatic Diagnosis of Coronary Artery Disease in SPECT Myocardial Perfusion Imaging Employing Deep Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146362] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbia S Series) myocardial perfusion imaging (MPI) scans and investigates the capabilities of deep learning and convolutional neural networks. Considering the wide applicability of deep learning in medical image classification, a robust CNN model whose architecture was previously determined in nuclear image analysis is introduced to recognize myocardial perfusion images by extracting the insightful features of an image and use them to classify it correctly. In addition, a deep learning classification approach using transfer learning is implemented to classify cardiovascular images as normal or abnormal (ischemia or infarction) from SPECT MPI scans. The present work is differentiated from other studies in nuclear cardiology as it utilizes SPECT MPI images. To address the two-class classification problem of CAD diagnosis, achieving adequate accuracy, simple, fast and efficient CNN architectures were built based on a CNN exploration process. They were then employed to identify the category of CAD diagnosis, presenting its generalization capabilities. The results revealed that the applied methods are sufficiently accurate and able to differentiate the infarction or ischemia from healthy patients (overall classification accuracy = 93.47% ± 2.81%, AUC score = 0.936). To strengthen the findings of this study, the proposed deep learning approaches were compared with other popular state-of-the-art CNN architectures for the specific dataset. The prediction results show the efficacy of new deep learning architecture applied for CAD diagnosis using SPECT MPI scans over the existing ones in nuclear medicine.
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