arXiv subjects

Chris Bradley

Publications and source records attributed to Chris Bradley.

3 recordsLinked to original sources

arXiv2026

T$_2$* and Susceptibility Mapping as Indicators of Placental Health

Objective(s): T$_2$* and susceptibility ($χ$) MRI mapping provide complimentary measures of the haemodynamic environment in the placenta. The aims of this work were to use these simultaneously obtained measures to investigate the role of oxygen distribution on the well-established reduction of T$_2$* with gestational age found in healthy pregnancies and explore differences in both measures in compromised placentas. Methods: T$_2$* and $χ$ were measured simultaneously from a double echo, echo planar scan of the whole placenta, across a range of gestational ages and pregnancy complications. Regional variations across the placenta were investigated. Results: Whole placental mean T$_2$* was more correlated with standard deviation of $χ$ than mean $χ$ indicating it is more driven by increasing local inhomogeneities rather than bulk deoxygenation with healthy gestation. Compromised placentas also showed increased standard deviation of $χ$ as well as lower mean T$_2$* suggesting flow/uptake mismatch and reduced oxygenation. Regionally, the susceptibility was lowest (most oxygenated) and least variable in the central region of the placenta indicating good mixing and refreshment of blood in this area. The susceptibility was highest (most deoxygenated) and most variable at the fetal side, suggesting less effective perfusion in this region. Compromised cases showed the greatest difference on the fetal side for both mean and standard deviation of $χ$. T$_2$* was lowest at the fetal side for healthy and compromised cases but the maternal and central regions better distinguished between the two groups. Conclusion(s): T$_2$* and susceptibility can be mapped simultaneously from a single MRI scan and provide complimentary information about the function of the placenta across healthy gestational development, and as a potential indicator of placental compromise.

physics.med-ph↗

arXiv2024

Explainable AI Enhances Glaucoma Referrals, Yet the Human-AI Team Still Falls Short of the AI Alone

Primary care providers are vital for initial triage and referrals to specialty care. In glaucoma, asymptomatic and fast progression can lead to vision loss, necessitating timely referrals to specialists. However, primary eye care providers may not identify urgent cases, potentially delaying care. Artificial Intelligence (AI) offering explanations could enhance their referral decisions. We investigate how various AI explanations help providers distinguish between patients needing immediate or non-urgent specialist referrals. We built explainable AI algorithms to predict glaucoma surgery needs from routine eyecare data as a proxy for identifying high-risk patients. We incorporated intrinsic and post-hoc explainability and conducted an online study with optometrists to assess human-AI team performance, measuring referral accuracy and analyzing interactions with AI, including agreement rates, task time, and user experience perceptions. AI support enhanced referral accuracy among 87 participants (59.9%/50.8% with/without AI), though Human-AI teams underperformed compared to AI alone. Participants believed they included AI advice more when using the intrinsic model, and perceived it more useful and promising. Without explanations, deviations from AI recommendations increased. AI support did not increase workload, confidence, and trust, but reduced challenges. On a separate test set, our black-box and intrinsic models achieved an accuracy of 77% and 71%, respectively, in predicting surgical outcomes. We identify opportunities of human-AI teaming for glaucoma management in primary eye care, noting that while AI enhances referral accuracy, it also shows a performance gap compared to AI alone, even with explanations. Human involvement remains essential in medical decision making, underscoring the need for future research to optimize collaboration, ensuring positive experiences and safe AI use.

cs.HC↗

arXiv2018

Towards realistic HPC models of the neuromuscular system

Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment, which is based on the MegaMol environment capable of handling large amounts of simulated data. It offers a platform for fast visualization prototyping, distributed rendering, and advanced visualization techniques. We present results of a variety of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieved good scalability on up to 768 cores, where the previous implementation used only 4 cores.

cs.CE↗