Transfer Learning of a Pretrained Preclinical Neural Network for Robotic Surgical Assessment on Limited Clinical Data
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 5
- 试验地点
- 1
- 主要终点
- Accuracy of action recognition using clinical data from scratch
研究概览
简要总结
The goal of this observational study is to explore how pretrained artificial intelligence (AI) models, trained on preclinical data, can improve the accuracy of action recognition and skills assessment in robot-assisted surgery (RAS) in urological patients by the use of transfer learning. The main questions it aims to answer are:
- Can pretrained AI models accurately assess action recognition and skills assessment in clinical surgeries?
- How do different training approaches of transfer learning affect the performance of the AI models? A baseline model developed from scratch using clinical data will be compared to pretrained models that are (1) directly applied to clinical data (2) fine-tuned by training only some layers of the AI model, and (3) fully retrained to see if these approaches improve performance.
Participants who are robot surgeons will:
- Undergo RAS procedures on patients, with no intervention, where video data will be collected for later action recognition and skills assessment.
- Contribute to model training and evaluation through clinical dataset integration.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Robot surgeons who are experienced with more than 100 cases.
- •Robot surgical fellows with less than 100 cases.
- •Robot surgeons who worked at the urological department of Aalborg University Hospital.
排除标准
- 未提供
结局指标
主要结局
Accuracy of action recognition using clinical data from scratch
时间窗: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
Accuracy of the deep learning algorithm for action recognition, when training the model from scratch using clinical data from robot surgical procedures.
Accuracy of skills assessment using clinical data from scratch
时间窗: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
Accuracy of the deep learning algorithm for skills assessment, when training the model from scratch using clinical data from robot surgical procedures.
Accuracy of action recognition using the pretrained network directly on clinical data
时间窗: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
Accuracy of the pretrained deep learning algorithm for action recognition, when using the model directly on clinical data from robot surgical procedures.
Accuracy of skills assessment using the pretrained model directly on clinical data
时间窗: From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment.
Accuracy of the pretrained deep learning algorithm for skills assessment, when using the model directly on clinical data from robot surgical procedures.
K fold accuracies for action recognition and skills assessment for the complete retraining of the pretrained network.
时间窗: From the start to the end of the clinical procedures.
K fold cross-validation accuracies when retraining the complete pretrained model on the clinical data for both action recognition and skills assessment.
K fold accuracies for action recognition and skills assessment for the partial retraining of the pretrained network.
时间窗: From the start to the end of the clinical procedures.
K fold cross validation accuracies for action recognition and skills assessment for the retraining of the LSTM and dense layers of the pretrained network using clinical data.
次要结局
- Weighted recall/sensitivity, precision and F1 score for action recognition of the clinical network trained from scratch(From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.)
- Weighted recall/sensitivity, precision and F1 score for Skills Assessment of the clinical network trained from scratch(From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment..)
- Predictive certainty of the action recognition and skills assessment of the network trained from scratch on the clinical data.(From the start to the end of the clinical procedures.)
- Predictive certainty of the action recognition and skills assessment of the network partially retrained network.(From the start to the end of the clinical procedures.)
研究者
Nasseh Hashemi
Medical doctor
Aalborg University