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Journal Club: Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study
Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study, Background
The financial burden and the uncertain response of the anti-vascular endothelial growth factor (anti-VEGF) treatment often cause hesitation among patients with neovascular age-related macular degeneration (nAMD), highlighting the need for a reliable method to predict treatment outcomes. We aimed to develop and validate a deep learning model that can predict the visual and anatomical prognosis of patients with nAMD undergoing anti-VEGF therapy.
Methods
This prospective, nationwide, multicentre study involved 18 tertiary referral hospitals from 12 provinces across China. A large dataset of patients (aged 50–85 years) with nAMD treated with anti-VEGF therapy (Conbercept, 0·5 mg/0·05 mL, Chengdu, China) under a 3+PRN regimen was established. All participants underwent…
Event interval: Single day event. Online Meeting Link: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1. Campus room: F107, 750 Republican St. Seattle 98109. Accessibility Contact: imds@uw.edu. Event Types: Academics. Meetings. Lectures/Seminars. Target Audience: Data Scientists and Medical Data Scientists.
Monday, September 21, 2026, 1:00 PM – 2:00 PM.
For more info visit washington.zoom.us. Journal Club: Position: Epistemic uncertainty estimation methods are fundamentally incomplete
Position: Epistemic uncertainty estimation methods are fundamentally incomplete
Identifying and disentangling sources of predictive uncertainty is essential for trustworthy supervised learning. We argue that widely used second-order methods that disentangle aleatoric and epistemic uncertainty are fundamentally incomplete. First, we show that unaccounted bias contaminates uncertainty estimates by overestimating aleatoric (data-related) uncertainty and underestimating the epistemic (model-related) counterpart, leading to incorrect uncertainty quantification. Second, we demonstrate that existing methods capture only partial contributions to the variance-driven part of epistemic uncertainty; different approaches account for different variance sources, yielding estimates that are incomplete and difficult to interpret. Together, these results highlight that current epistemic uncertainty estimates can only be used in safety-critical and high-stakes decision-making when limitations are fully understood by end users…
Event interval: Single day event. Online Meeting Link: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1. Campus room: F107, 750 Republican St. Seattle 98109. Accessibility Contact: imds@uw.edu. Event Types: Academics. Meetings. Lectures/Seminars. Target Audience: Data Scientists and Medical Data Scientists.
Monday, October 12, 2026, 1:00 PM – 2:00 PM.
For more info visit washington.zoom.us.