Deep Learning for Predicting Surgical Outcomes from Preoperative Imaging

EasyChair Preprint 13836

Abstract

Background and Objective: The prediction of surgical outcomes is critical for preoperative planning, patient counseling, and optimizing resource allocation in healthcare. Traditional methods for predicting surgical outcomes rely heavily on clinical expertise and statistical models that often fall short in handling the complexity and variability of medical imaging data. Recent advances in deep learning (DL) provide an unprecedented opportunity to leverage large-scale preoperative imaging datasets to predict surgical outcomes with higher accuracy. This research aims to develop and validate a deep learning framework to predict surgical outcomes from preoperative imaging, focusing on its application in various surgical specialties.

Methods: A comprehensive deep learning pipeline will be designed and implemented, encompassing data collection, preprocessing, model training, validation, and testing. The study will utilize a large dataset of preoperative imaging (e.g., MRI, CT scans) coupled with patient demographic and clinical data obtained from electronic health records (EHRs). Key steps include:

Keyphrases: Convolutional Neural Networks, deep learning, healthcare optimization., precision medicine, predictive modeling, preoperative imaging, surgical outcomes

@booklet{EasyChair:13836,
  author    = {John Owen},
  title     = {Deep Learning for Predicting Surgical Outcomes from Preoperative Imaging},
  howpublished = {EasyChair Preprint 13836},
  year      = {EasyChair, 2024}}