MOTION MODELLING & ANALYSIS GROUP
Artificial Intelligence for Medical Image Analysis
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About us
About us
The Motion Modelling and Analysis Group (MMAG) is an academic research group based within the School of Biomedical Engineering and Imaging Sciences of King's College London. The group is based at St. Thomas' Hospital in central London. The MMAG includes researchers from a diverse range of backgrounds and is proud to foster an inclusive research environment to allow all to thrive and reach their potential.
The MMAG has worked in the past on the imaging, modelling and estimation of repetitive motion. Currently, the group works more widely on the use of artificial intelligence (specifically machine and deep learning) for the analysis of imaging data with the aim of extracting clinically useful information. Cardiology is one of the main application areas but the group is also involved in developing machine learning solutions for other clinical application areas, such as radiotherapy and vocal tract imaging. The aim is to develop novel but clinically-driven machine learning solutions and translate them into equitable patient benefit.
Latest news
Latest news
5 November 2025: Three MICCAI FAIMI papers now available online:
A. Achara, E. Puyol-Antón, A. Hammers, A. P. King, "Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-Based Alzheimer’s Disease Classification", Proceedings MICCAI FAIMI, 2025. (paper)
P. Shah, D. Sankhe, M. Rashid, Z. Khaled, E. Puyol-Antón, T. Lee, M. Alqarni, S. Rai, A. P. King, "The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models", Proceedings MICCAI FAIMI, 2025. (paper)
T. Lee, E. Puyol-Antón, B. Ruijsink, M. Shi, A. P. King, "Does a Rising Tide Lift All Boats? Bias Mitigation for AI-Based CMR Segmentation", Proceedings MICCAI FAIMI, 2025. (paper)
29 September 2025: Tiarna Lee's PhD thesis is now available online!
T. Lee, "Fairness in AI for Cardiac Magnetic Resonance Image Segmentation", PhD thesis, King's College London, 2025. (thesis)
29 August 2025: The group is a partner in the new EU-funded SkincAIr project, which will work with multiple African partners to develop a fair and robust AI-driven mobile app for detection of skin-related neglected tropical diseases. Check out the project web site: https://skincair.health/
15 June 2025: Andrew King will deliver a keynote speech on "Learning to Trust AI in Medical Imaging" at the Medical Image Understanding and Analysis (MIUA) conference on July 15th 2025.
11 June 2025: New journal paper by Zhen Yuan now available:
Z. Yuan, D. Stojanowski, L. Li, A. Gomez, H. Jogeesvaran, E. Puyol-Antón, B. Inusa, A. P. King, "DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation From 2D Ultrasound Images", Medical Image Analysis, 2025. (open access paper)
Please also see the associated dataset of realistic synthetic spleen ultrasound images with ground truth volume data
10 March 2025: Read the press release about Tiarna Lee's important research into investigating the causes of race bias in AI-based CMR analysis.
4 March 2025: Watch this video about Maram Alqarni's scholarship journey as one of the first female biomedical engineering PhD students from Saudia Arabia.
27 February 2025: New journal paper by Tiarna Lee now available:
T. Lee, E. Puyol-Antón, B. Ruijsink, S. Roujol, T. Barfoot, S. Ogbomo-Harmitt, M. Shi, A. P. King, "An Investigation Into the Causes of Race Bias in AI-based Cine CMR Segmentation", European Heart Journal Digital Health, 2025. (paper)
4 February 2025: Tareen Dawood's PhD thesis is now available online!
T. Dawood, "Improving Uncertainty Calibration of Artificial Intelligence Classification Models in Cardiology", PhD thesis, King's College London, 2024. (thesis)
13 October 2024: New journal paper by Laia Humbert-Vidan now available:
L. Humbert-Vidan, V. Patel, A. P. King, T. Guerrero-Urbano, "Comparison of Deep-learning Multimodality Data Fusion Strategies in Mandibular Osteoradionecrosis NTCP Modelling Using Clinical Variables and Radiation Dose Distribution Volumes", Physics in Medicine and Biology, 2024. (paper)
29 August 2024: Check out Andrew King's talk on "Bias and Fairness in AI for Medical Imaging" from the RISE-MICCAI/FAIMI Summer School, 2024.
2 January 2024: New blog post on the future for Fair AI by Andrew King.
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