Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation

• Published in: PloS one Vol. 18; no. 7; p. e0286952
• Main Authors: Poushpas, Sepideh, Normahani, Pasha, Kisil, Ilya, Szubert, Ben, Mandic, Danilo P., Jaffer, Usman
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.07.2023
• Subjects: Analysis; Anastomosis; Biology and Life Sciences; Blood flow; Blood vessels; Classification; Classifiers; Complications and side effects; Computer and Information Sciences; Decomposition; Diagnosis; Dialysis; Evaluation; Fistula; Fistulae; Flow velocity; Hemodialysis; Image processing; Learning algorithms; Long short-term memory; Machine learning; Mathematical analysis; Medicine and Health Sciences; Neural networks; Patient outcomes; Patients; Prognosis; Research and Analysis Methods; Stenosis; Surgical anastomosis; Surveillance; Tensors; Ultrasonic imaging; Ultrasound; Video; Waveforms; United Kingdom; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0286952

Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to evaluate the feasibility of detecting AVF stenosis using a novel method combining tensor-decomposition of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow, anastomosis, and venous outflow of the AVFs were analysed. Tensor decompositions were computed from both the ‘full-frame’ (whole-image) videos and ‘cropped’ videos (to include areas of blood flow only). The resulting output were labelled for the presence of stenosis, as per the DUS findings, and used as a set of features for classification using a Long Short-Term Memory (LSTM) neural network. A total of 61 out of 66 available videos were used for analysis. The whole-image classifier failed to beat random guessing, achieving a mean area under the receiver operating characteristics (AUROC) value of 0.49 (CI 0.48 to 0.50). In contrast, the ‘cropped’ video classifier performed better with a mean AUROC of 0.82 (CI 0.66 to 0.96), showing promising predictive power despite the small size of the dataset. The combined application of tensor decomposition and machine learning are promising for the detection of AVF stenosis and warrant further investigation.