An artificial intelligence method for predicting postoperative urinary incontinence based on multiple anatomic parameters of MRI.

ABSTRACT:

Background

Deep learning methods are increasingly applied in the medical field; however, their lack of interpretability remains a challenge. Captum is a tool that can be used to interpret neural network models by computing feature importance weights. Although Captum is an interpretable model, it is rarely used to study medical problems, and there is a scarcity of data regarding MRI anatomical measurements for patients with prostate cancer after undergoing Robotic-Assisted Radical Prostatectomy (RARP). Consequently, predictive models for continence that use multiple types of anatomical MRI measurements are limited.

Methods

We explored the energy efficiency of deep learning models for predicting continence by analyzing MRI measurements. We analyzed and compared various statist

SUBMITTER: Li J 

PROVIDER: S-EPMC10520312 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature