Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective

• URL: http://arxiv.org/abs/2210.00589v1
• Date: Sun, 2 Oct 2022 17:54:54 GMT
• Title: Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
• Authors: Michael Dohopolski (1), Kai Wang (1), Biling Wang (1), Ti Bai (1), Dan Nguyen (1), David Sher (1), Steve Jiang (1), Jing Wang (1) ((1) Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, UT Southwestern Medical Center, Dallas TX 75235, USA)
• Abstract summary: There are several deep learning-inspired uncertainty estimation techniques, but few are implemented on medical datasets. We compared dropout variational inference (DO), test-time augmentation (TTA), conformal predictions, and single deterministic methods for estimating uncertainty. It may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prediction uncertainty estimation has clinical significance as it can potentially quantify prediction reliability. Clinicians may trust 'blackbox' models more if robust reliability information is available, which may lead to more models being adopted into clinical practice. There are several deep learning-inspired uncertainty estimation techniques, but few are implemented on medical datasets -- fewer on single institutional datasets/models. We sought to compare dropout variational inference (DO), test-time augmentation (TTA), conformal predictions, and single deterministic methods for estimating uncertainty using our model trained to predict feeding tube placement for 271 head and neck cancer patients treated with radiation. We compared the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) trends for each method at various cutoffs that sought to stratify patients into 'certain' and 'uncertain' cohorts. These cutoffs were obtained by calculating the percentile "uncertainty" within the validation cohort and applied to the testing cohort. Broadly, the AUC, sensitivity, and NPV increased as the predictions were more 'certain' -- i.e., lower uncertainty estimates. However, when a majority vote (implementing 2/3 criteria: DO, TTA, conformal predictions) or a stricter approach (3/3 criteria) were used, AUC, sensitivity, and NPV improved without a notable loss in specificity or PPV. Especially for smaller, single institutional datasets, it may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice.

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