Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment

Article Article scientifique Article Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment eng 2024-08-22 author Goldman Jean-Philippe Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland. 98472 Département de radiologie et informatique médicale (RADIO) author Jane Soler Pablo Division of NuclearMedicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland.,NIMTLab, Faculty of Medicine, University of Geneva, Center of biomedical imaging (CIBM), Geneva, Switzerland. 956759 author Zaghir Jamil 0000-0002-8209-6098 Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland. 956784 Département de radiologie et informatique médicale (RADIO) author Andrade Teixeira Eliluane Perazio Division of NuclearMedicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland. 960496 author Peretti Debora NIMTLab, Faculty of Medicine, University of Geneva, Center of biomedical imaging (CIBM), Geneva, Switzerland. 975652 Département de radiologie et informatique médicale (RADIO) author Garibotto Valentina 0000-0003-2422-698X Division of NuclearMedicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland.,NIMTLab, Faculty of Medicine, University of Geneva, Center of biomedical imaging (CIBM), Geneva, Switzerland. 704817 Département de radiologie et informatique médicale (RADIO) author Lovis Christian 0000-0002-2681-8076 Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland. 29016 Département de radiologie et informatique médicale (RADIO) 560-564 10.3233/SHTI240476 39176804 0926-9630 https://ebooks.iospress.nl/doi/10.3233/SHTI240476 The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer's Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research. Nuclear Medicine Brain molecular imaging reports Text classification Humans Positron-Emission Tomography Cognitive Dysfunction / diagnostic imaging Cognitive Dysfunction / classification Brain / diagnostic imaging Machine Learning Alzheimer Disease / diagnostic imaging Alzheimer Disease / classification Natural Language Processing Support Vector Machine Sensitivity and Specificity Switzerland Reproducibility of Results Dewey 616.0757 62 Département de radiologie et informatique médicale 227 false 344 Interfaces Homme-machine en milieu clinique 610 1222 Laboratoire de neuroimagerie et de traceurs moléculaires innovants 984 Studies in health technology and informatics 316 7a80ae64-9c8d-484d-9f43-4ae6bf5a381d Article (Published version) Public Article_scientifique_from_unpaywall.pdf 214556 application/pdf CC BY-NC-4.0 80c0e7868fdeba1b765c569e36435872d32931c16c0e5f2f385c6315a9f88313 openAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

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Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment	Studies in health technology and informatics			2024	36	10

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