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Martinez-Murcia FJ, Arco JE, Jimenez-Mesa C, Segovia F, Illan IA, Ramirez J, Gorriz JM. Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning. Int J Neural Syst 2024; 34:2450043. [PMID: 38770651 DOI: 10.1142/s0129065724500436] [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] [Indexed: 05/22/2024]
Abstract
Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.
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Affiliation(s)
- Francisco J Martinez-Murcia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Eloy Arco
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Carmen Jimenez-Mesa
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Fermin Segovia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Ignacio A Illan
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
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Carvalho Macruz FBD, Dias ALMP, Andrade CS, Nucci MP, Rimkus CDM, Lucato LT, Rocha AJD, Kitamura FC. The new era of artificial intelligence in neuroradiology: current research and promising tools. ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-12. [PMID: 38565188 PMCID: PMC10987255 DOI: 10.1055/s-0044-1779486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/13/2023] [Indexed: 04/04/2024]
Abstract
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.
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Affiliation(s)
- Fabíola Bezerra de Carvalho Macruz
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
- Academia Nacional de Medicina, Rio de Janeiro RJ, Brazil.
| | | | | | - Mariana Penteado Nucci
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Carolina de Medeiros Rimkus
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Diagnósticos da América SA, São Paulo SP, Brazil.
| | | | - Felipe Campos Kitamura
- Diagnósticos da América SA, São Paulo SP, Brazil.
- Universidade Federal de São Paulo, São Paulo SP, Brazil.
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Mellander H, Hillal A, Ullberg T, Wassélius J. Evaluation of CINA® LVO artificial intelligence software for detection of large vessel occlusion in brain CT angiography. Eur J Radiol Open 2024; 12:100542. [PMID: 38188638 PMCID: PMC10764253 DOI: 10.1016/j.ejro.2023.100542] [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: 08/20/2023] [Revised: 11/17/2023] [Accepted: 12/10/2023] [Indexed: 01/09/2024] Open
Abstract
Objective To systematically evaluate the ability of the CINA® LVO software to detect large vessel occlusions eligible for mechanical thrombectomy on CTA using conventional neuroradiological assessment as gold standard. Methods Retrospectively, two hundred consecutive patients referred for a brain CTA and two hundred patients that had been subject for endovascular thrombectomy, with an accessible preceding CTA, were assessed for large vessel occlusions (LVO) using the CINA® LVO software. The patients were sub-grouped by occlusion site. The original radiology report was used as ground truth and cases with disagreement were reassessed. Two-by-two tables were created and measures for LVO detection were calculated. Results A total of four-hundred patients were included; 221 LVOs were present in 215 patients (54 %). The overall specificity was high for LVOs in the anterior circulation (93 %). The overall sensitivity for LVOs in the anterior circulation was 54 % with the highest sensitivity for the M1 segment of the middle cerebral artery (87 %) and T-type internal carotid occlusions (84 %). The sensitivity was low for occlusions in the M2 segment of the middle cerebral artery (13 % and 0 % for proximal and distal M2 occlusions respectively) and in posterior circulation occlusions (0 %, not included in the intended use of the software). Conclusions LVO detection sensitivity for the CINA® LVO software differs largely depending on the location of the occlusion, with low sensitivity for detection of some LVOs potentially eligible for mechanical thrombectomy. Further development of the software to increase sensitivity to all LVO locations would increase the clinical usefulness.
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Affiliation(s)
- Helena Mellander
- Diagnostic Radiology, Department of Neuroradiology and Odontology, Center for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Amir Hillal
- Diagnostic Radiology, Department of Neuroradiology and Odontology, Center for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Teresa Ullberg
- Diagnostic Radiology, Department of Neuroradiology and Odontology, Center for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Wassélius
- Diagnostic Radiology, Department of Neuroradiology and Odontology, Center for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Lund University, Lund, Sweden
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Rathmann E, Hemkemeier P, Raths S, Grothe M, Mankertz F, Hosten N, Flessa S. Changes in MRI Workflow of Multiple Sclerosis after Introduction of an AI-Software: A Qualitative Study. Healthcare (Basel) 2024; 12:978. [PMID: 38786390 PMCID: PMC11121325 DOI: 10.3390/healthcare12100978] [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: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis.
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Affiliation(s)
- Eiko Rathmann
- Institute of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany or (N.H.)
| | - Pia Hemkemeier
- Department of Business Administration and Health Care Management, Faculty of Law and Economics, University of Greifswald, 17489 Greifswald, Germany (S.F.)
| | - Susan Raths
- Department of Business Administration and Health Care Management, Faculty of Law and Economics, University of Greifswald, 17489 Greifswald, Germany (S.F.)
| | - Matthias Grothe
- Department of Neurology, University Medicine Greifswald, 17475 Greifswald, Germany;
| | - Fiona Mankertz
- Institute of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany or (N.H.)
| | - Norbert Hosten
- Institute of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany or (N.H.)
| | - Steffen Flessa
- Department of Business Administration and Health Care Management, Faculty of Law and Economics, University of Greifswald, 17489 Greifswald, Germany (S.F.)
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Alongi P, Arnone A, Vultaggio V, Fraternali A, Versari A, Casali C, Arnone G, DiMeco F, Vetrano IG. Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review. Cancers (Basel) 2024; 16:407. [PMID: 38254896 PMCID: PMC10814838 DOI: 10.3390/cancers16020407] [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: 11/06/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 01/24/2024] Open
Abstract
The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).
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Affiliation(s)
- Pierpaolo Alongi
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Annachiara Arnone
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Viola Vultaggio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Alessandro Fraternali
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Annibale Versari
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Cecilia Casali
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Francesco DiMeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
- Department of Oncology and Onco-Hematology, Università di Milano, 20122 Milan, Italy
- Department of Neurological Surgery, Johns Hopkins Medical School, Baltimore, MD 21218, USA
| | - Ignazio Gaspare Vetrano
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
- Department of Biomedical Sciences for Health, Università di Milano, 20122 Milan, Italy
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Ryan ME, Jaju A. Revolutionizing pediatric neuroimaging: the era of CT, MRI, and beyond. Childs Nerv Syst 2023; 39:2583-2592. [PMID: 37380927 DOI: 10.1007/s00381-023-06041-9] [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: 05/28/2023] [Accepted: 06/17/2023] [Indexed: 06/30/2023]
Abstract
PURPOSE To review the evolution of cross-sectional imaging in pediatric neuroradiology from early developments to current advancements and future directions. METHODS Information was obtained through a PubMed literature search as well as referenced online resources and personal experience from radiologists currently practicing pediatric neuroimaging and those who experienced the era of nascent cross-sectional imaging. RESULTS The advent of computed tomography (CT) and magnetic resonance imaging (MRI) in the 1970s and 1980s brought about a revolutionary shift in the field of medical imaging, neurosurgical and neurological diagnosis. These cross-sectional imaging techniques ushered in a new era by enabling the visualization of soft tissue structures within the brain and spine. Advancements in these imaging modalities have continued at a remarkable pace, now providing not only high high-resolution and 3-dimensional anatomical imaging, but also functional assessment. With each stride forward, CT and MRI have provided clinicians with invaluable insights, improving the accuracy and precision of diagnoses, facilitating the identification of optimal surgical targets, and guiding the selection of appropriate treatment strategies. CONCLUSION This article traces the origins and early developments of CT and MRI, chronicling their journey from pioneering technologies to their current indispensable status in clinical applications and exciting possibilities that lie ahead in the realm of medical imaging and neurologic diagnosis.
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Affiliation(s)
- Maura E Ryan
- Department of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Ave, Chicago, IL, USA.
- Northwestern University Feinberg School of Medicine, 420 East Superior St, Chicago, IL, USA.
| | - Alok Jaju
- Department of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Ave, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, 420 East Superior St, Chicago, IL, USA
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Naji Y, Mahdaoui M, Klevor R, Kissani N. Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review. Cureus 2023; 15:e45412. [PMID: 37854769 PMCID: PMC10581506 DOI: 10.7759/cureus.45412] [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] [Accepted: 09/17/2023] [Indexed: 10/20/2023] Open
Abstract
Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.
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Affiliation(s)
- Yahya Naji
- Neurology Department, REGNE Research Laboratory, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, MAR
- Neurology Department, Agadir University Hospital, Agadir, MAR
| | - Mohamed Mahdaoui
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Raymond Klevor
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Najib Kissani
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
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Ayubcha C, Amanullah A, Patel KH, Teichner E, Gokhale S, Marquez-Valenzuela U, Werner TJ, Alavi A. Stroke and molecular imaging: a focus on FDG-PET. AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2023; 13:51-63. [PMID: 37214267 PMCID: PMC10193198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/23/2023] [Indexed: 05/24/2023]
Abstract
Stroke is the leading cause of disability worldwide, the second most common cause of dementia and the third leading cause of death. Though the etiology of stroke has been explored extensively, there remains open questions in the scientific and clinical study of stroke. Traditional imaging techniques, such as magnetic resonance imaging and computed tomography, have been applied extensively and remain mainstays in clinical practice. Nevertheless, positron emission tomography has proven to be a powerful molecular imaging tool in exploring the scientific aspects of neurological disease, and stroke remains an area of great interest. This review article examines the role of positron emission tomography in the study of stroke including its contributions to elaborating related pathophysiology and delving into possible clinical applications.
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Affiliation(s)
- Cyrus Ayubcha
- Harvard Medical SchoolBoston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public HealthBoston, MA, USA
| | - Aamir Amanullah
- Department of Radiology, Hospital of University of PennsylvaniaPhiladelphia, PA, USA
- Lewis Katz School of Medicine at Temple UniversityPhiladelphia, PA, USA
| | | | - Eric Teichner
- Department of Radiology, Hospital of University of PennsylvaniaPhiladelphia, PA, USA
- Thomas Jefferson UniversityPhiladelphia, PA, USA
| | - Saket Gokhale
- Department of Radiology, Hospital of University of PennsylvaniaPhiladelphia, PA, USA
- Thomas Jefferson UniversityPhiladelphia, PA, USA
| | | | - Thomas J Werner
- Department of Radiology, Hospital of University of PennsylvaniaPhiladelphia, PA, USA
| | - Abass Alavi
- Department of Radiology, Hospital of University of PennsylvaniaPhiladelphia, PA, USA
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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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10
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Duong MT, Rudie JD, Mohan S. Neuroimaging Patterns of Intracranial Infections: Meningitis, Cerebritis, and Their Complications. Neuroimaging Clin N Am 2023; 33:11-41. [PMID: 36404039 PMCID: PMC10904173 DOI: 10.1016/j.nic.2022.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neuroimaging provides rapid, noninvasive visualization of central nervous system infections for optimal diagnosis and management. Generalizable and characteristic imaging patterns help radiologists distinguish different types of intracranial infections including meningitis and cerebritis from a variety of bacterial, viral, fungal, and/or parasitic causes. Here, we describe key radiologic patterns of meningeal enhancement and diffusion restriction through profiles of meningitis, cerebritis, abscess, and ventriculitis. We discuss various imaging modalities and recent diagnostic advances such as deep learning through a survey of intracranial pathogens and their radiographic findings. Moreover, we explore critical complications and differential diagnoses of intracranial infections.
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Affiliation(s)
- Michael Tran Duong
- Division of Neuroradiology, Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Jeffrey D Rudie
- Department of Radiology, Scripps Clinic and University of California San Diego, 10666 Torrey Pines Road, La Jolla, CA 92037, USA
| | - Suyash Mohan
- Division of Neuroradiology, Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
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11
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López-Ojeda W, Hurley RA. Medical Metaverse, Part 2: Artificial Intelligence Algorithms and Large Language Models in Psychiatry and Clinical Neurosciences. J Neuropsychiatry Clin Neurosci 2023; 35:316-320. [PMID: 37840258 DOI: 10.1176/appi.neuropsych.20230117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Affiliation(s)
- Wilfredo López-Ojeda
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
| | - Robin A Hurley
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
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Anaya-Isaza A, Mera-Jiménez L, Verdugo-Alejo L, Sarasti L. Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network. Eur J Radiol Open 2023; 10:100484. [PMID: 36950474 PMCID: PMC10027502 DOI: 10.1016/j.ejro.2023.100484] [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: 08/25/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
Early detection and diagnosis of brain tumors are crucial to taking adequate preventive measures, as with most cancers. On the other hand, artificial intelligence (AI) has grown exponentially, even in such complex environments as medicine. Here it's proposed a framework to explore state-of-the-art deep learning architectures for brain tumor classification and detection. An own development called Cross-Transformer is also included, which consists of three scalar products that combine self-care model keys, queries, and values. Initially, we focused on the classification of three types of tumors: glioma, meningioma, and pituitary. With the Figshare brain tumor dataset was trained the InceptionResNetV2, InceptionV3, DenseNet121, Xception, ResNet50V2, VGG19, and EfficientNetB7 networks. Over 97 % of classifications were accurate in this experiment, which provided a network's performance overview. Subsequently, we focused on tumor detection using the Brain MRI Images for Brain Tumor Detection and The Cancer Genome Atlas Low-Grade Glioma database. The development encompasses learning transfer, data augmentation, as well as image acquisition sequences; T1-weighted images (T1WI), T1-weighted post-gadolinium (T1-Gd), and Fluid-Attenuated Inversion Recovery (FLAIR). Based on the results, using learning transfer and data augmentation increased accuracy by up to 6 %, with a p-value below the significance level of 0.05. As well, the FLAIR sequence was the most efficient for detection. As an alternative, our proposed model proved to be the most effective in terms of training time, using approximately half the time of the second fastest network.
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Ong W, Zhu L, Zhang W, Kuah T, Lim DSW, Low XZ, Thian YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers (Basel) 2022; 14:4025. [PMID: 36011018 PMCID: PMC9406500 DOI: 10.3390/cancers14164025] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/10/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:171-182. [PMID: 34862541 DOI: 10.1007/978-3-030-85292-4_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.
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Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
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Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
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