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11. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI

Author

Sounderajah, Viknesh, Ashrafian, Hutan, Rose, Sherri, Shah, Nigam H., Ghassemi, Marzyeh, Golub, Robert, Kahn, Jr, Charles E., Esteva, Andre, Karthikesalingam, Alan, Mateen, Bilal, Webster, Dale, Milea, Dan, Ting, Daniel, Treanor, Darren, Cushnan, Dominic, King, Dominic, McPherson, Duncan, Glocker, Ben, Greaves, Felix, Harling, Leanne, Ordish, Johan, Cohen, Jérémie F., Deeks, Jon, Leeflang, Mariska, Diamond, Matthew, McInnes, Matthew D. F., McCradden, Melissa, Abràmoff, Michael D., Normahani, Pasha, Markar, Sheraz R., Chang, Stephanie, Liu, Xiaoxuan, Mallett, Susan, Shetty, Shravya, Denniston, Alastair, Collins, Gary S., Moher, David, Whiting, Penny, Bossuyt, Patrick M., and Darzi, Ara

Published
2021
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26. Study protocol for a multicentre comparative diagnostic accuracy study of tools to establish the presence and severity of peripheral arterial disease in people with diabetes mellitus: the DM PAD study

Author

Normahani, Pasha, primary, Burgess, Laura, additional, Norrie, John, additional, Epstein, David Mark, additional, Kandiyil, Neghal, additional, Saratzis, Athanasios, additional, Smith, Sasha, additional, Khunti, Kamlesh, additional, Edmonds, M, additional, Ahluwalia, Raju, additional, Coward, Trusha, additional, Hartshorne, Tim, additional, Ashwell, Simon, additional, Shalhoub, Joseph, additional, Pigott, Elizabeth, additional, Davies, Alun H, additional, and Jaffer, Usman, additional

Published
2022
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30. Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation.

Author

Poushpas, Sepideh, Normahani, Pasha, Kisil, Ilya, Szubert, Ben, Mandic, Danilo P., and Jaffer, Usman

Subjects
*ARTERIOVENOUS fistula, *MACHINE learning, *DOPPLER ultrasonography, *STENOSIS, *RECEIVER operating characteristic curves, *BLOOD flow
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2023
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37. sj-pdf-1-vmj-10.1177_1358863X221105113 – Supplemental material for Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study

Author

Normahani, Pasha, Sounderajah, Viknesh, Mandic, Danilo, and Jaffer, Usman

Subjects
FOS: Clinical medicine, Cardiology, 110323 Surgery
Abstract

Supplemental material, sj-pdf-1-vmj-10.1177_1358863X221105113 for Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study by Pasha Normahani, Viknesh Sounderajah, Danilo Mandic and Usman Jaffer in Vascular Medicine

Published
2022
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42. Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study.

Author

Normahani, Pasha, Sounderajah, Viknesh, Mandic, Danilo, and Jaffer, Usman

Subjects
*PERIPHERAL vascular diseases, *RECEIVER operating characteristic curves, *FEATURE extraction, *SUPPORT vector machines
Abstract

Background: Point-of-care duplex ultrasound has emerged as a promising test for the diagnosis of peripheral artery disease (PAD). However, the interpretation of morphologically diverse Doppler arterial spectral waveforms is challenging and associated with wide inter-observer variation. The aim of this study is to evaluate the utility of machine learning techniques for the diagnosis of PAD from Doppler arterial spectral waveforms sampled at the level of the ankle in patients with diabetes. Methods: In two centres, 590 Doppler arterial spectral waveform images (PAD 369, no-PAD 221) from 305 patients were prospectively collected. Doppler arterial spectral waveform signals were reconstructed. Blinded full lower-limb reference duplex ultrasound results were used to label waveform according to PAD status (i.e., PAD, no-PAD). Statistical metrics and multiscale wavelet variance were extracted as discriminatory features. A long short-term memory (LSTM) network was used for the classification of raw signals, and logistic regression (LR) and support vector machines (SVM) were used for classification of extracted features. Signals and feature vectors were randomly divided into training (80%) and testing (20%) sets. Results: The highest overall accuracy was achieved using a logistic regression model with a combination of statistical and multiscale wavelet variance features, with 88% accuracy, 92% sensitivity, and 82% specificity. The area under the receiver operating characteristics curve (AUC) was 0.93. Conclusion: We have constructed a machine learning algorithm with high discriminatory ability for the diagnosis of PAD using Doppler arterial spectral waveforms sampled at the ankle vessels. [ABSTRACT FROM AUTHOR]

Published
2022
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45. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

Author

Sounderajah, Viknesh, primary, Ashrafian, Hutan, additional, Golub, Robert M, additional, Shetty, Shravya, additional, De Fauw, Jeffrey, additional, Hooft, Lotty, additional, Moons, Karel, additional, Collins, Gary, additional, Moher, David, additional, Bossuyt, Patrick M, additional, Darzi, Ara, additional, Karthikesalingam, Alan, additional, Denniston, Alastair K, additional, Mateen, Bilal Akhter, additional, Ting, Daniel, additional, Treanor, Darren, additional, King, Dominic, additional, Greaves, Felix, additional, Godwin, Jonathan, additional, Pearson-Stuttard, Jonathan, additional, Harling, Leanne, additional, McInnes, Matthew, additional, Rifai, Nader, additional, Tomasev, Nenad, additional, Normahani, Pasha, additional, Whiting, Penny, additional, Aggarwal, Ravi, additional, Vollmer, Sebastian, additional, Markar, Sheraz R, additional, Panch, Trishan, additional, and Liu, Xiaoxuan, additional

Published
2021
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47. The Reliability and Quality of YouTube Videos as a Source of Public Health Information Regarding COVID-19 Vaccination: Cross-sectional Study (Preprint)

Author

Chan, Calvin, primary, Sounderajah, Viknesh, additional, Daniels, Elisabeth, additional, Acharya, Amish, additional, Clarke, Jonathan, additional, Yalamanchili, Seema, additional, Normahani, Pasha, additional, Markar, Sheraz, additional, Ashrafian, Hutan, additional, and Darzi, Ara, additional

Published
2021
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49. Factors associated with infrainguinal bypass graft patency at 1-year; a retrospective analysis of a single centre experience

Author

Normahani, Pasha, primary, Anwar, Ismail Yusuf, additional, Courtney, Alona, additional, Acharya, Amish, additional, Sounderajah, Viknesh, additional, Mustafa, Chira, additional, Jaffer, Usman, additional, Shalhoub, Joseph, additional, Riga, Celia, additional, Gibbs, Richard, additional, Jenkins, Michael, additional, Bicknell, Colin, additional, Davies, Alun H, additional, Nott, David, additional, Aylwin, Christopher, additional, and Standfield, Nigel J, additional

Published
2021
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