Confidence estimation of AI model output for bladder cancer treatment response assessment in CT urography

We are developing methods to estimate the ML/AI model output confidence in treatment response assessment for bladder cancer in CT urography. The model output confidence was estimated by an ensemble of ML/AI models, each incorporating a different lesion segmentation algorithm. The cases were then split into “easy” group with smaller variability and “difficult” group with larger variability in the outputs of the model ensemble. The AUC of the “difficult” cases was lower (AUC range: 0.58-0.80) compared to the AUC of the “easy” cases (AUC range: 0.85-0.92) for the radiomics model. The trend was consistent for the different methods of variability estimation. This indicates the feasibility of using the proposed methods for the estimation of model output confidence.