Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning

• URL: http://arxiv.org/abs/2303.09085v1
• Date: Thu, 16 Mar 2023 05:06:06 GMT
• Title: Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning
• Authors: Li-Chin Chen, Jung-Nien Lai, Hung-En Lin, Hsien-Te Chen, Kuo-Hsuan Hung, Yu Tsao
• Abstract summary: Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. There is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning to develop a preoperative assessment tool.
• Score: 8.809304978076305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LBP and sciatica patients. Standard operative assessments, traditional Chinese medicine body constitution assessments, planned surgical approach, and vowel pronunciation recordings were collected and stored in different modalities. Our work provides insights into leveraging modality combinations, multimodals, and fusion strategies. The interpretability of models and correlations between modalities were also inspected. Based on the recruited 105 patients, we found that combining standard operative assessments, body constitution assessments, and planned surgical approach achieved the best performance in 0.81 accuracy. Our approach is effective and can be widely applied in general practice due to simplicity and effective.

Related papers

• Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes [12.493423568689801]
  preoperative cognitive status as measured by the clock drawing test contributed to predicting length of hospital stay, hospital charges, average pain experienced during follow-up. Machine learning models were trained to classify postoperative outcomes in hold-out test sets.
  arXiv  Detail & Related papers  (2024-10-29T23:42:51Z)
• Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers [69.81176740997175]
  We evaluate the performance of transformer-based models in predicting postoperative acute renal failure, pulmonary complications, and postoperative in-hospital mortality. Our results demonstrate that transformer-based models can achieve superior performance in predicting postoperative complications and outperform traditional machine learning models.
  arXiv  Detail & Related papers  (2023-06-01T14:08:05Z)
• Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement [61.28459114068828]
  We propose an intraoperative planning approach for robotic spine surgery that leverages real-time observation for drill path planning based on Safe Deep Reinforcement Learning (DRL) Our approach was capable of achieving 90% bone penetration with respect to the gold standard (GS) drill planning.
  arXiv  Detail & Related papers  (2023-05-09T11:42:53Z)
• Surgical Phase Recognition in Laparoscopic Cholecystectomy [57.929132269036245]
  We propose a Transformer-based method that utilizes calibrated confidence scores for a 2-stage inference pipeline. Our method outperforms the baseline model on the Cholec80 dataset, and can be applied to a variety of action segmentation methods.
  arXiv  Detail & Related papers  (2022-06-14T22:55:31Z)
• Quantification of Robotic Surgeries with Vision-Based Deep Learning [45.165919577877695]
  We propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery. We validated our framework on four video-based datasets of two commonly-encountered types of steps within minimally-invasive robotic surgeries.
  arXiv  Detail & Related papers  (2022-05-06T06:08:35Z)
• CholecTriplet2021: A benchmark challenge for surgical action triplet recognition [66.51610049869393]
  This paper presents CholecTriplet 2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. We present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods and 19 new deep learning algorithms are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%.
  arXiv  Detail & Related papers  (2022-04-10T18:51:55Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• Towards Unified Surgical Skill Assessment [18.601526803020885]
  We propose a unified multi-path framework for automatic surgical skill assessment. We conduct experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries.
  arXiv  Detail & Related papers  (2021-06-02T09:06:43Z)
• Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment [3.523040451502402]
  Current practice for assessing neonatal postoperative pain is subjective, inconsistent, slow and discontinuous. We present a novel multimodal-temporal approach that integrates visual and vocal signals and uses them for assessing neonatal postoperative pain.
  arXiv  Detail & Related papers  (2020-12-03T18:52:35Z)
• Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks [1.7548541038532495]
  Multi-task deep learning models outperform random forest models in predicting postoperative complications. integrated interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust.
  arXiv  Detail & Related papers  (2020-04-27T02:32:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.