{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:50:07Z","timestamp":1747216207906,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,22]]},"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3233\/shti240476","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:34:02Z","timestamp":1724405642000},"source":"Crossref","is-referenced-by-count":0,"title":["Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4000-4199","authenticated-orcid":false,"given":"Jean-Philippe","family":"Goldman","sequence":"first","affiliation":[{"name":"Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland"},{"name":"Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland"}]},{"given":"Pablo","family":"Jan\u00e9","sequence":"additional","affiliation":[{"name":"Division of NuclearMedicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland"},{"name":"NIMTLab, Faculty of Medicine, University of Geneva, Center of biomedical imaging (CIBM), Geneva, Switzerland"}]},{"given":"Jamil","family":"Zaghir","sequence":"additional","affiliation":[{"name":"Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland"},{"name":"Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland"}]},{"given":"Eliluane","family":"Pirazzo Andrade Teixeira","sequence":"additional","affiliation":[{"name":"Division of NuclearMedicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland"}]},{"given":"D\u00e9bora Elisa","family":"Peretti","sequence":"additional","affiliation":[{"name":"NIMTLab, Faculty of Medicine, University of Geneva, Center of biomedical imaging (CIBM), Geneva, Switzerland"}]},{"given":"Valentina","family":"Garibotto","sequence":"additional","affiliation":[{"name":"Division of NuclearMedicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland"},{"name":"NIMTLab, Faculty of Medicine, University of Geneva, Center of biomedical imaging (CIBM), Geneva, Switzerland"}]},{"given":"Christian","family":"Lovis","sequence":"additional","affiliation":[{"name":"Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland"},{"name":"Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:34:03Z","timestamp":1724405643000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240476","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}