Academic Journal
Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment
| Τίτλος: | Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment |
|---|---|
| Συγγραφείς: | Goldman, Jean-Philippe, Jane Soler, Pablo, Zaghir, Jamil, Andrade Teixeira, Eliluane Perazio, Peretti, Debora, Garibotto, Valentina, Lovis, Christian |
| Πηγή: | ISSN: 0926-9630 ; Studies in health technology and informatics, vol. 316 (2024) p. 560-564. |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | Université de Genève: Archive ouverte UNIGE |
| Θεματικοί όροι: | info:eu-repo/classification/ddc/616.0757, 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 |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/39176804; unige:180154 |
| Διαθεσιμότητα: | https://archive-ouverte.unige.ch/unige:180154 |
| Rights: | info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsbas.81883433 |
| Βάση Δεδομένων: | BASE |
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