Endoscope Video RSD Prediction Using Random Temporal Frame Sampling
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Solution Overview
Problem
Existing RSD estimation techniques require manual labeling of frames in the training dataset, which is both labor-intensive and expensive, and unsupervised approaches struggle to accurately predict surgery duration due to the diversity of patients, surgeon's skills and other unpredictable factors.
Innovation Solution
A deep-learning-based real-time RSD prediction system that utilizes a convolutional neural network (CNN) model to predict RSD from endoscope video frames, randomly sampling N−1 additional frames from the elapsed portion of the surgical session to improve prediction accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of frames is used to train the machine learning model, then the prediction accuracy is improved, but the labor cost and time consumption increase significantly
Solution Approach 1:
The system uses the model's own predictions to generate training labels automatically. The machine learning model predicts surgical phase and RSD for video frames, and these predictions are used as training labels without requiring manual annotation. This self-labeling approach eliminates the need for expensive and time-consuming manual frame-by-frame labeling while maintaining prediction accuracy through iterative training and refinement.
2Device complexity
If a single frame is used as input for RSD prediction, then the computational complexity is reduced, but the prediction accuracy deteriorates due to inability to capture temporal context
Solution Approach 1:
The system pre-processes the surgical video by extracting key frames and organizing them into sequences before prediction. By preparing temporal context information in advance and structuring it properly, the system enables the model to effectively utilize historical video data without excessive computational burden during real-time prediction.
Solution Approach 2:
The system extracts only the necessary temporal information from the video sequences - specifically selecting key frames and essential temporal features - rather than processing the entire video stream. This extraction approach captures the critical temporal context needed for accurate RSD prediction while minimizing computational complexity.
3Loss of information
If recurrent neural networks are used to encapsulate previous frames into hidden states, then temporal context is captured, but the RSD prediction accuracy remains poor due to difficulty in representing thousands of frames
Solution Approach 1:
The system extracts only the essential temporal information from lengthy video sequences by selecting key frames and critical temporal features. This extraction approach captures the necessary context for RSD prediction without the burden of processing thousands of frames, directly addressing the limitation of RNNs in handling extensive temporal data effectively.
Solution Approach 2:
Instead of using all available video frames, the system uses a selective subset of frames that are most informative for RSD prediction. This partial action approach - using only necessary temporal information rather than the complete video sequence - achieves accurate predictions while avoiding the dimensional curse that plagues RNNs with thousands of frames.
4Loss of time
If the OR team overestimates the surgery duration, then the waiting time for other OR teams is reduced, but the utilization of expensive OR resources decreases
Solution Approach 1:
The system provides real-time feedback on RSD predictions during surgery, allowing the OR scheduling system to dynamically adjust schedules based on actual surgical progress. This continuous feedback loop enables more accurate OR utilization planning by combining predicted remaining duration with elapsed time, preventing both overestimation and underestimation of surgery duration.
5Productivity
If the surgery duration is underestimated, then the OR resource utilization is improved, but the waiting time for other OR teams and patients increases
Solution Approach 1:
The real-time RSD prediction system continuously monitors surgical progress and provides updated duration estimates, enabling the scheduling system to make informed decisions about OR allocation. This feedback mechanism prevents underestimation by constantly updating the remaining duration based on actual surgical events and progress, thereby avoiding excessive waiting times for subsequent teams.
Data Source
AI summary
In one aspect, the process receives a current frame of the endoscope video at a current time of the live surgical session, wherein the current time is among a sequence of prediction time points for making continuous RSD predictions during the live surgical session. The process next randomly samples additional frames of the endoscope video corresponding to the elapsed portion of the live surgical session. The process then combines the sampled frames and the current frame in the temporal order to obtain a set of N frames. Next, the process feeds the set of N frames into a trained model for the given surgical procedure. The process subsequently outputs a current RSD prediction based on the set of N frames. Other aspects are also described and claimed.