Tesi etd-01032025-142928 |
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Tipo di tesi
Tesi di specializzazione (4 anni)
Autore
DE VIETRO, FABRIZIO
URN
etd-01032025-142928
Titolo
AI-supported Approaches for Mammography Single and Double Reading: A Controlled Multireader Study
Dipartimento
RICERCA TRASLAZIONALE E DELLE NUOVE TECNOLOGIE IN MEDICINA E CHIRURGIA
Corso di studi
RADIODIAGNOSTICA
Relatori
relatore Prof. Neri, Emanuele
Parole chiave
- artificial intelligence
- breast neoplasms
- mammography
- radiologists
- sensitivity and specificity
Data inizio appello
27/01/2025
Consultabilità
Non consultabile
Data di rilascio
27/01/2095
Riassunto
Key Finding: AI support significantly improved sensitivity across all mammography reading approaches, especially benefiting radiologists with lower baseline sensitivity, which rose from 56.7% in the human single reading to 89.5% (p<0.001) in the human-AI double reading. In simulated double reading, AI as an independent second reader further improved sensitivity without affecting specificity.
Importance: This finding emphasizes the potential of AI to improve mammography reading diagnostic performance, particularly supporting radiologists with comparatively worse baseline sensitivity.
Importance: This finding emphasizes the potential of AI to improve mammography reading diagnostic performance, particularly supporting radiologists with comparatively worse baseline sensitivity.
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