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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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Tafuri B, De Blasi R, Nigro S, Logroscino G. Explainable machine learning radiomics model for Primary Progressive Aphasia classification. Front Syst Neurosci 2024; 18:1324437. [PMID: 38562661 PMCID: PMC10982515 DOI: 10.3389/fnsys.2024.1324437] [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: 10/19/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants. Diagnosing and classifying PPA patients represents a complex challenge that requires the integration of multimodal information, including clinical, biological, and radiological features. Structural neuroimaging can play a crucial role in aiding the differential diagnosis of PPA and constructing diagnostic support systems. Methods In this study, we conducted a white matter texture analysis on T1-weighted images, including 56 patients with PPA (31 svPPA and 25 nfvPPA), and 53 age- and sex-matched controls. We trained a tree-based algorithm over combined clinical/radiomics measures and used Shapley Additive Explanations (SHAP) model to extract the greater impactful measures in distinguishing svPPA and nfvPPA patients from controls and each other. Results Radiomics-integrated classification models demonstrated an accuracy of 95% in distinguishing svPPA patients from controls and of 93.7% in distinguishing svPPA from nfvPPA. An accuracy of 93.7% was observed in differentiating nfvPPA patients from controls. Moreover, Shapley values showed the strong involvement of the white matter near left entorhinal cortex in patients classification models. Discussion Our study provides new evidence for the usefulness of radiomics features in classifying patients with svPPA and nfvPPA, demonstrating the effectiveness of an explainable machine learning approach in extracting the most impactful features for assessing PPA.
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Affiliation(s)
- Benedetta Tafuri
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Roberto De Blasi
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Salvatore Nigro
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Giancarlo Logroscino
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
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Krishnamurthy K, Pradhan RK. Emerging perspectives of synaptic biomarkers in ALS and FTD. Front Mol Neurosci 2024; 16:1279999. [PMID: 38249293 PMCID: PMC10796791 DOI: 10.3389/fnmol.2023.1279999] [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: 08/19/2023] [Accepted: 12/01/2023] [Indexed: 01/23/2024] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenerative diseases with shared pathological features like transactive response DNA-binding protein of 43 kDa (TDP-43) inclusions and genetic mutations. Both diseases involve synaptic dysfunction, contributing to their clinical features. Synaptic biomarkers, representing proteins associated with synaptic function or structure, offer insights into disease mechanisms, progression, and treatment responses. These biomarkers can detect disease early, track its progression, and evaluate therapeutic efficacy. ALS is characterized by elevated neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF) and blood, correlating with disease progression. TDP-43 is another key ALS biomarker, its mislocalization linked to synaptic dysfunction. In FTD, TDP-43 and tau proteins are studied as biomarkers. Synaptic biomarkers like neuronal pentraxins (NPs), including neuronal pentraxin 2 (NPTX2), and neuronal pentraxin receptor (NPTXR), offer insights into FTD pathology and cognitive decline. Advanced technologies, like machine learning (ML) and artificial intelligence (AI), aid biomarker discovery and drug development. Challenges in this research include technological limitations in detection, variability across patients, and translating findings from animal models. ML/AI can accelerate discovery by analyzing complex data and predicting disease outcomes. Synaptic biomarkers offer early disease detection, personalized treatment strategies, and insights into disease mechanisms. While challenges persist, technological advancements and interdisciplinary efforts promise to revolutionize the understanding and management of ALS and FTD. This review will explore the present comprehension of synaptic biomarkers in ALS and FTD and discuss their significance and emphasize the prospects and obstacles.
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Affiliation(s)
- Karrthik Krishnamurthy
- Department of Neuroscience, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
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McMackin R, Bede P, Ingre C, Malaspina A, Hardiman O. Biomarkers in amyotrophic lateral sclerosis: current status and future prospects. Nat Rev Neurol 2023; 19:754-768. [PMID: 37949994 DOI: 10.1038/s41582-023-00891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2023] [Indexed: 11/12/2023]
Abstract
Disease heterogeneity in amyotrophic lateral sclerosis poses a substantial challenge in drug development. Categorization based on clinical features alone can help us predict the disease course and survival, but quantitative measures are also needed that can enhance the sensitivity of the clinical categorization. In this Review, we describe the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers in amyotrophic lateral sclerosis and their place in the rapidly evolving landscape of new therapeutics. Fluid-based markers from cerebrospinal fluid, blood and urine are emerging as useful diagnostic, pharmacodynamic and predictive biomarkers. Combinations of imaging measures have the potential to provide important diagnostic and prognostic information, and neurophysiological methods, including various electromyography-based measures and quantitative EEG-magnetoencephalography-evoked responses and corticomuscular coherence, are generating useful diagnostic, categorical and prognostic markers. Although none of these biomarker technologies has been fully incorporated into clinical practice or clinical trials as a primary outcome measure, strong evidence is accumulating to support their clinical utility.
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Affiliation(s)
- Roisin McMackin
- Discipline of Physiology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Peter Bede
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland
- Computational Neuroimaging Group, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin, Ireland
- Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Caroline Ingre
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Andrea Malaspina
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Orla Hardiman
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland.
- Department of Neurology, Beaumont Hospital, Dublin, Ireland.
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