Tesi etd-09072023-150447 |
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Tipo di tesi
Tesi di laurea magistrale LM6
Autore
FILIDEI, TOMMASO
URN
etd-09072023-150447
Titolo
AI-Driven Prediction of Parkinson's and Atypical Parkinsonian Disorders using [18F]FDG PET/CT Regional Metabolism and Deep Radiomics
Dipartimento
RICERCA TRASLAZIONALE E DELLE NUOVE TECNOLOGIE IN MEDICINA E CHIRURGIA
Corso di studi
MEDICINA E CHIRURGIA
Relatori
relatore Prof. Volterrani, Duccio
correlatore Dott.ssa Aghakhanyan, Gayane
correlatore Dott.ssa Aghakhanyan, Gayane
Parole chiave
- AI
- Deep Radiomics
- FDG PET
- Parkinson's disease
- PD
- APS
- Atypical parkinsonian disorders
- DLR
Data inizio appello
26/09/2023
Consultabilità
Non consultabile
Data di rilascio
26/09/2093
Riassunto
Parkinson’s Disease (PD) and Atypical Parkinsonian Syndromes (APS) share many phenotypic manifestations, especially in the early stages of the disease, creating clinical challenges. The ability to precisely classify observations is extremely valuable and the accurate differential diagnosis of parkinsonism has foremost therapeutic and prognostic importance.
A relevant role in the differential diagnosis of PD and APS is played by [18F]FDG PET, as metabolic patterns of regional glucose metabolism of these nosological entities are different and disease-specific. Automated artificial intelligence (AI)-based deep learning Radiomics (DLR) framework might represent an optimal imaging-biomarker that might be able to classify PD, APS and HC individuals based on [18F]FDG PET data, particularly in the early stages of the disease, when no clear metabolic patterns are detectable by qualitative and/or semi-quantitative [18F]FDG PET assessment.
A relevant role in the differential diagnosis of PD and APS is played by [18F]FDG PET, as metabolic patterns of regional glucose metabolism of these nosological entities are different and disease-specific. Automated artificial intelligence (AI)-based deep learning Radiomics (DLR) framework might represent an optimal imaging-biomarker that might be able to classify PD, APS and HC individuals based on [18F]FDG PET data, particularly in the early stages of the disease, when no clear metabolic patterns are detectable by qualitative and/or semi-quantitative [18F]FDG PET assessment.
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