A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.
This study sought to quantify, through a multi-reader study, whether AI assistance improves diagnostic accuracy across experience levels, reduces bidirectional errors, and enhances inter-reader consensus in suspected pituitary microadenoma diagnosis. To this end, we developed and validated a stacking model integrating clinical, radiomics, and deep learning features on non-contrast T1COR MRI.
This study sought to quantify, through a multi-reader study, whether AI assistance improves diagnostic accuracy across experience levels, reduces bidirectional errors, and enhances inter-reader consensus in suspected pituitary microadenoma diagnosis. To this end, we developed and validated a stacking model integrating clinical, radiomics, and deep learning features on non-contrast T1COR MRI.
What the study was
- Study design
- Cohort/Observational Study
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- Neuroradiology
Why it surfaced
Matched watchlist topic 'AI/ML in clinical diagnostics and imaging'. Study design: Cohort/Observational Study. Score: 8/10 (N:2, R:3, D:1, P:2).
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