Predicting future occlusion or stenosis of lower extremity bypass grafts using artificial intelligence to simultaneously analyze all flow velocities collected in current and previous ultrasound examinations

Q3 Medicine JVS-vascular science Pub Date : 2024-01-01 DOI:10.1016/j.jvssci.2024.100192
Xiao Luo PhD , Fattah Muhammad Tahabi BS , Dave M. Rollins RVT , Alan P. Sawchuk MD
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引用次数: 0

Abstract

Objective

Routine surveillance with duplex ultrasound (DUS) examination is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts with various intervals postoperatively. The presently used methodology to analyze bypass graft DUS examination does not use all the available data and has been shown to have a significant rate for missing impending bypass graft failure. The objective of this research is to investigate recurrent neural networks (RNNs) to predict future bypass graft occlusion or stenosis.

Methods

This study includes DUS examinations of 663 patients who had bypass graft operations done between January 2009 and June 2022. Only examinations without missing values were included. We developed two RNNs (a bidirectional long short-term memory unit and a bidirectional gated recurrent unit) to predict bypass graft occlusion and stenosis based on peak systolic velocities collected in the 2 to 5 previous DUS examinations. We excluded the examinations with missing values and split our data into training and test sets. Then, we applied 10-fold cross-validation on training to optimize the hyperparameters and compared models using the test data.

Results

The bidirectional long short-term memory unit model can gain an overall sensitivity of 0.939, specificity of 0.963, and area under the curve of 0.950 on the prediction of bypass graft occlusion, and an overall sensitivity of 0.915, specificity of 0.909, and area under the curve of 0.912 predicting the development of a future critical stenosis. The results on different bypass types show that the system performs differently on different types. The results on subcohorts based on gender, smoking status, and comorbidities show that the performance on current smokers is lower than the never smoker.

Conclusions

We found that RNNs can gain good sensitivity, specificity, and accuracy for the detection of impending bypass graft occlusion or the future development of a critical bypass graft stenosis using all the available peak systolic velocity data in the present and previous bypass graft DUS examinations. Integrating clinical data, including demographics, social determinants, medication, and other risk factors, together with the DUS examination may result in further improvements.

Clinical Relevance

Detecting bypass graft failure before it occurs is important clinically to prevent amputations, salvage limbs, and save lives. Current methods evaluating screening duplex ultrasound examinations have a significant failure rate for detecting a bypass graft at risk for failure. Artificial intelligence using recurrent neural networks has the potential to improve the detection of at-risk bypass graft before they fail. Additionally, artificial intelligence is in the news and is being applied to many fields. Vascular surgeons need to know its potential to improve vascular outcomes.

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利用人工智能同时分析当前和以往超声检查中收集的所有血流速度,预测下肢旁路移植血管未来的闭塞或狭窄情况
目的建议在股-腘静脉和股-胫-腓静脉旁路移植术后的不同时间间隔内使用双工超声(DUS)进行常规监测。目前使用的旁路移植术 DUS 检查分析方法并未使用所有可用数据,而且已被证明有很大可能遗漏即将发生的旁路移植术失败。本研究的目的是研究用递归神经网络(RNN)来预测未来旁路移植管闭塞或狭窄的情况。方法本研究包括对 2009 年 1 月至 2022 年 6 月间接受旁路移植手术的 663 名患者进行的 DUS 检查。仅纳入了无缺失值的检查。我们开发了两个 RNN(双向长短期记忆单元和双向门控复发单元),根据之前 2 到 5 次 DUS 检查中收集的收缩速度峰值预测旁路移植闭塞和狭窄。我们排除了有缺失值的检查,并将数据分成训练集和测试集。结果双向长短期记忆单元模型在预测旁路移植闭塞方面的总体灵敏度为 0.939,特异性为 0.963,曲线下面积为 0.950;在预测未来发生临界狭窄方面的总体灵敏度为 0.915,特异性为 0.909,曲线下面积为 0.912。不同分流类型的结果表明,该系统对不同类型的分流有不同的表现。基于性别、吸烟状况和合并症的亚群结果显示,当前吸烟者的表现低于从未吸烟者。结论我们发现,RNN 可以利用当前和之前旁路移植 DUS 检查中所有可用的收缩压峰值速度数据,获得良好的灵敏度、特异性和准确性,用于检测即将发生的旁路移植闭塞或未来发展为临界旁路移植狭窄。将临床数据(包括人口统计学、社会决定因素、药物和其他风险因素)与 DUS 检查结合起来可能会带来进一步的改进。临床意义在旁路移植失败发生之前进行检测对于防止截肢、抢救肢体和挽救生命具有重要的临床意义。目前评估筛查双相超声检查的方法在检测有失效风险的旁路移植方面有很大的失败率。使用递归神经网络的人工智能有可能在搭桥术失败前提高对高风险搭桥术的检测率。此外,人工智能已成为新闻,并被应用于许多领域。血管外科医生需要了解人工智能在改善血管治疗效果方面的潜力。
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审稿时长
28 weeks
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