John Mccabe | Satsuma

Exploring Fairness and Performance Drivers Across State-of-the-Art Pulmonary Nodule Detection Algorithms

Mon, 04 May 2026 00:00:00 +0000

Researchers from Satsuma Lab have evaluated the fairness of deep learning based computer aided detection (CADe) systems for lung nodule detection in screening CT scans. Using data from a SUMMIT (London-based cohort), the study found that model performance remains consistent across sex and ethnic groups, despite imbalances in the training data. Results suggest that detection accuracy is driven more by nodule characteristics than by demographic factors. These findings support the equitable deployment of AI tools in future UK lung cancer screening programmes.

Further details can be found in John McCabe et al., 2025.

Expanded detection of early fibrotic phenotypes using lobar traction bronchiolectasis in lung cancer screening

Tue, 10 Mar 2026 00:00:00 +0000

A computationally frugal, open-source chest CT foundation model for thoracic disease detection in lung cancer screening programmes

Wed, 04 Feb 2026 00:00:00 +0000

Exploring Fairness and Performance Drivers Across State-of-the-Art Pulmonary Nodule Detection Algorithms

Sun, 21 Dec 2025 00:00:00 +0000

Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review

Sun, 01 Sep 2024 00:00:00 +0000