Alix Bird
8 Papers
Alix Bird is an academic researcher. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 2, co-authored 3 publications.
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Papers
Artificial Intelligence and Deep Learning for Rheumatologists
Christopher McMaster,Alix Bird,David F L Liew,Russell R C Buchanan,Claire E Owen,Wendy W. Chapman,Douglas E. V. Pires +6 more
TL;DR: It is imperative that rheumatologists appreciate the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development and clinical decision support tools of the future.
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Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.
Luke A. Smith,Lauren Oakden-Rayner,Alix Bird,Minyan Zeng,Minh-Son To,Sutapa Mukherjee,Lyle J. Palmer +6 more
TL;DR: There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores, and more rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility.
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Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
Alix Bird,Lauren Oakden-Rayner,Christopher McMaster,Luke A. Smith,Minyan Zeng,Mihir D. Wechalekar,Shonket Ray,Susanna Proudman,Lyle J. Palmer +8 more
TL;DR: In this paper , the authors argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease.
Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis
Minyan Zeng,Lauren Oakden-Rayner,Alix Bird,L. Smith,Zimu Wu,Rebecca Scroop,Timothy Kleinig,Jim Jannes,Mark Jenkinson,Lyle J. Palmer +9 more
TL;DR: Conventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores.
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Improved Flexibility and Interpretability of Large Vessel Stroke Prognostication Using Image Synthesis and Multi-task Learning
Minyan Zeng,Yutong Xie,Minh-Son To,Lauren Oakden-Rayner,Luke Whitbread,Stephen Bacchi,Alix Bird,Luke Smith,Rebecca Scroop,Timothy Kleinig,Jim Jannes,Lyle J. Palmer,Mark Jenkinson +12 more
TL;DR: This study improves large vessel stroke prognostication using image synthesis and multi-task learning, enabling flexible deployment of deep learning models with or without CTP maps, and enhancing interpretability and clinical trustworthiness.