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Articles by Andre Altmann
Imaging-based biological age estimation predicts competing mortality risk in lung cancer screening
Abstract Lung cancer screening (LCS) participants have a high competing risk of non-lung cancer related death, which limits screening benefit. We hypothesized that imaging-based biological age can identify individuals at higher risk of non-lung cancer mortality. Low dose computed tomography scans from a large LCS trial were retrospectively analyzed.
Profile – Professor Andre Altmann, University College London
Name: Andre Altmann Job title: Professor of Biomedical Data Science and AI Place of work / study: University College London Area of Research: Analyzing imaging and genetic data using statistics and AI to better understand disease onset and progression How is your work funded: Engineering and Physical Sciences Research Council (EPSRC), Bright Focus Foundation Tell us a little about yourself: After studying Computer Science with a focus on Machine Learning and Natural Language Processing, I...
Understanding pre-training data effects in retinal foundation models using two large fundus cohorts - Nature Communications
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Abstract Medical foundation models, pre-trained on large-scale unlabelled data, show strong performance and data efficiency when adapted to various clinically relevant applications. However, how pre-training data shape the generalisability and fairness of these models remains unexplored. Here we address this using two cohorts from Moorfields Eye Hospital (UK) and the Shanghai Diabetes Prevention Program (China), each containing 904,170 fundus photographs for model pre-training.
By Yukun Zhou, Zheyuan Wang, Yilan Wu, Ariel Yuhan Ong, Siegfried Wagner, Eden Ruffell, Mark Chia, Zhouyu Guan, Lie Ju, Justin Engelmann, David A. Merle, Tingyao Li, Jia Shu, Paul Nderitu, Ke Zou, Qingshan Hou, Xiaoxuan Liu, Yaxing Wang, Yih Chung Tham, Andre Altmann, Carol Cheung, Daniel Alexander, Eric J. Topol, Alastair K Denniston, Pearse A. Keane
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Nature
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A computationally frugal, open-source chest CT foundation model for thoracic disease detection in lung cancer screening programmes - Communications Medicine
Abstract Low-dose computed tomography (LDCT) employed in lung cancer screening (LCS) programmes is increasing in uptake worldwide. LCS programmes herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease, yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis.
Incorporating Multi-threshold Polygenic Risk in Hippocampal-based Normative Models Improves Cognitive Decline Prediction
Abstract Predicting cognitive decline using MRI measures of brain volume is challenging. Normative models capture the evolution of biomarkers over time in a healthy group, quantifying an individuals deviation from that norm. Here, we improve the precision of normative models for hippocampal volume, a key cognitive biomarker, by integrating multi-threshold polygenic scores (PGS) that capture genetic predisposition to its variability.
Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations
Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations Suhnyoung Jun, Andre Altmann, Sepideh Sadaghiani Journal of Neuroscience 5 March 2025, 45 (10) e1939242025; DOI: 10.1523/JNEUROSCI.1939-24.2025
Modulatory neurotransmitter genotypes shape dynamic functional connectome reconfigurations
Abstract Dynamic reconfigurations of the functional connectome across different connectivity states are highly heritable, predictive of cognitive abilities, and linked to mental health. Despite their established heritability, the specific polymorphisms that shape connectome dynamics are largely unknown.
Normative models reveal distinct cortical abnormalities to dimensions of psychopathologies in preadolescents
Abstract Background: Evidence suggests a non-specific mapping between psychiatric disorders and underlying neurobiological substrates. A dimensional psychopathology framework may prove useful for organizing observed neurobiological alterations along broad psychopathological dimensions.
A foundation model for generalizable disease detection from retinal images - Nature
Abstract Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2.
Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies
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Nomograms of Human Hippocampal Volume Shifted by Polygenic Scores
Abstract Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal Volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients.
A systems‐level analysis highlights microglial activation as a modifying factor in common epilepsies
ORIGINAL ARTICLE Andre Altmann, orcid.org/0000-0002-9265-2393 Centre for Medical Image Computing, University College London, London, UK Search for more papers by this author Mina Ryten, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK Search for more papers by this author Martina Di Nunzio, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy Search for more papers by this author Teresa Ravizza, Department...
Analysis of brain atrophy and local gene expression implicates astrocytes in Frontotemporal dementia
Analysis of brain atrophy and local gene expression implicates astrocytes in Frontotemporal dementia Andre Altmann, David M Cash, Martina Bocchetta, Carolin Heller, Regina H Reynolds, Katrina Moore, Rhian S Convery, David Thomas, John C van Swieten, Fermin Moreno, Raquel Sanchez-Valle, Barbara Borroni, Robert Laforce, Mario Masellis, Carmela Tartaglia, Caroline Graff, Daniela Galimberti, James Rowe, Elizabeth Finger, Matthis Synofzik, Rik Vandenberghe, Alexandre de Mendonca, Fabrizio...
Evidence for Bias of Genetic Ancestry in Resting State Functional MRI
ABSTRACT Resting state functional magnetic resonance imaging (rs-fMRI) is a popular imaging modality for mapping the functional connectivity of the brain. Rs-fMRI is, just like other neuroimaging modalities, subject to a series of technical and subject level biases that change the inferred connectivity pattern. In this work we predicted genetic ancestry from rs-fMRI connectivity data at very high performance (area under the ROC curve of 0.93).
Susceptibility of brain atrophy to TRIB3 in Alzheimer’s disease, evidence from functional prioritization in imaging genetics
Looking to draw attention to the bleak prospects for coral reefs, a growing number of artists are working at the intersection of coral art, activism, and, to some extent, ecology.
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