Explainable AI for the Operating Theater

Rudzicz, Frank; Joshi, Shalmali

doi:10.1007/978-3-030-49100-0_25

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Furthermore, although ML models have historically been limited due to poor explainability, several approaches have recently been developed under the umbrella of "explainable artificial intelligence" (XAI) 6 . These explainable models not only determine the probability of the outcome, but also highlight the variables that drive these predictions.…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence to predict the risk of side-specific extraprostatic extension in pre-prostatectomy patients

Kwong

Khondker²,

Tran³

et al. 2022

CUAJ

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Introduction: We aimed to develop an explainable machine learning (ML) model to predict side-specific extraprostatic extension (ssEPE) to identify patients who can safely undergo nerve-sparing radical prostatectomy using preoperative clinicopathological variables. Methods: A retrospective sample of clinicopathological data from 900 prostatic lobes at our institution was used as the training cohort. Primary outcome was the presence of ssEPE. The baseline model for comparison had the highest performance out of current biopsy-derived predictive models for ssEPE. A separate logistic regression (LR) model was built using the same variables as the ML model. All models were externally validated using a testing cohort of 122 lobes from another institution. Models were assessed by area under receiver operating characteristic (AUROC), precision-recall (AUPRC), calibration, and decision curve analysis. Model predictions were explained using Shapley Additive exPlanations. This tool was deployed as a publicly available web application. Results: Incidence of ssEPE in the training and testing cohorts were 30.7 and 41.8%, respectively. The ML model achieved AUROC 0.81 (LR 0.78, baseline 0.74) and AUPRC 0.69 (LR 0.64, baseline 0.59) on the training cohort. On the testing cohort, the ML model achieved AUROC 0.81 (LR 0.76, baseline 0.75) and AUPRC 0.78 (LR 0.75, baseline 0.70). The ML model was explainable, well-calibrated, and achieved the highest net benefit for clinically relevant cutoffs of 10–30%. Conclusions: We developed a user-friendly application that enables physicians without prior ML experience to assess ssEPE risk and understand factors driving these predictions to aid surgical planning and patient counselling (www.ssepe.ml).

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Section: Introductionmentioning

confidence: 99%