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Lower Extremity Bypass Surveillance and Peak Systolic Velocities Value Prediction Using Recurrent Neural Networks
Xiao Luo1,2, Fattah Muhammad Tahabi1, Dave M Rollins3
1Department of Management Science and Information Systems, Oklahoma State University, Oklahoma, USA.
Insights
This study uses recurrent neural networks to predict lower extremity bypass graft occlusion using peak systolic velocities (PSVs) from duplex ultrasound exams. The BiGRU model improved prediction accuracy, suggesting more data enhances graft surveillance.
Area of Science:
- Vascular Surgery
- Biomedical Engineering
- Machine Learning
Background:
- Routine duplex ultrasound surveillance is crucial for monitoring lower extremity bypass grafts.
- Current methods lack a systematic approach for analyzing peak systolic velocities (PSVs) to predict graft status.
Purpose of the Study:
- To explore the use of recurrent neural networks (RNNs) for predicting future PSVs and identifying bypass graft occlusion.
- To develop and compare RNN models for predicting stenosis and occlusion based on historical PSV data.
Main Methods:
- Developed sequence-to-sequence RNN models, including BiGRU and BiLSTM, to forecast PSVs.
- Utilized 5-fold cross-validation to evaluate model performance based on one to three prior PSV sets.
- Assessed the impact of increasing duplex ultrasound exam data on prediction accuracy.
Main Results:
- The BiGRU model demonstrated superior performance over BiLSTM when using two or more PSV sets.
- Prediction accuracy improved, and error rates decreased with the inclusion of more historical PSV data.
- The study highlights the potential of RNNs in predicting graft occlusion and stenosis.
Conclusions:
- Recurrent neural networks show promise for enhancing lower extremity bypass graft surveillance.
- Integrating PSVs with clinical data can further improve predictive capabilities for graft health.
- This approach offers a systematic method for analyzing duplex ultrasound data to detect early signs of graft failure.
Abstract:
Routine duplex ultrasound surveillance is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts at various post-operative intervals. Currently, there is no systematic method for bypass graft surveillance using a set of peak systolic velocities (PSVs) collected during these exams. This research aims to explore the use of recurrent neural networks to predict the next set of PSVs, which can then indicate occlusion status. Recurrent neural network models were developed to predict occlusion and stenosis based on one to three prior sets of PSVs, with a sequence-to-sequence model utilized to forecast future PSVs within the stent graft and nearby arteries. The study employed 5-fold cross-validation for model performance comparison, revealing that the BiGRU model outperformed BiLSTM when two or more sets of PSVs were included, demonstrating that increasing duplex ultrasound exams improve prediction accuracy and reduces error rates. This work establishes a basis for integrating comprehensive clinical data, including demographics, comorbidities, symptoms, and other risk factors, with PSVs to enhance lower extremity bypass graft surveillance predictions.
