9 DAILY MICCAI Tuesday newcomers can slow down the decision-making process. “An example of this is you have a testing set, which is all nice and neat, and then a new patient comes into the clinic, and you want to know, how is this patient different?” Joshua poses. “What am I certain about? What am I uncertain about? Sometimes, it doesn’t matter if you’re uncertain. If they’re going to do very well on one drug, you can give them that one. But sometimes it does matter, and you’d like to knowthat.” What sets Joshua’s work apart is its applicability across various fields of medicine. It offers a versatile framework that can be adapted to different medical domains. While he has demonstrated its capabilities in the context of multiple sclerosis research – an area marked by its heterogeneity, availability of drugs, and absence of a known cure – it also holds promise in fields like depression. Even for general triage, it could streamline the initial assessment of patients, helping healthcare professionals quickly determine their risk levels. “The most challenging part of the work was the validation,” he tells us. “Making counterfactual predictions is very well studied, but making them with uncertainty, and then validating the uncertainty when you don’t have ground truth about what’s going to happen, is difficult. On top of that, we wanted a clinical focus. We looked at some causal inference metrics with uncertainty, and we said, when the metrics improve, that’s great, but what does that mean when an individual comes in andyou’re trying to treat them? We have a better area under the uplift curve, which means we’re correctly identifying the right responders, but that’s not important to the individual. They want to know, how does this help me?” Joshua used computer vision techniques to build and analyze deep learning models for MRI inputs. While other aspects of clinical information were considered, such as patient interviews and physical examinations, the real breakthrough came from integrating MRI data. In a Joshua Durso-Finley
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