U.S. Board-Certified Neurologist — Stroke Data Annotation Project (SHS00305)

We are recruiting U.S.-based, board-certified Neurologists to participate in a multi-reader, multi-case (MRMC) study evaluating diagnostic performance in ASPECTS scoring of acute ischemic stroke (AIS) cases using non-contrast CT (NCCT) imaging.

This research assesses clinician performance with and without AI support developed by the sponsor. As a participating neurologist, you will independently interpret stroke CT studies in two structured reading phases—one AI-aided and one unaided—using a secure, web-based platform (RedBrick AI).

Your clinical expertise in acute stroke care will directly contribute to a controlled evaluation of AI-assisted decision support, helping shape how emerging technologies integrate into time-sensitive neurological decision-making.

Study Design & Workload:

• 75 NCCT stroke cases
• Each case read twice (once AI-aided, once unaided)
• 150 total reads
• Cases released in batches of up to 10 (aided or unaided per batch)
• Reading modes randomized and counterbalanced

Time Commitment:

• ~8–10 minutes per case
• ~20–26 total hours (~10–13 hours per phase)

Timeline & Workflow:

• Start: Early March
• Two 4-week reading phases
• 4-week washout period between phases
• 1-week initial training and 1-week refresher training
• Fully remote and asynchronous via RedBrick AI

• Complete assigned case sets within agreed timelines
• Review 75 NCCT stroke studies per phase (150 total reads across two phases)
• Identify affected hemisphere(s)
• Score all 10 ASPECTS regions
• Classify each region as: Affected, Unaffected or Cannot be evaluated.
• Exclude chronic infarcts according to ASPECTS guidelines
• Maintain consistency and anatomical rigor across all cases
• Follow study protocol requirements precisely
• Perform all work independently

• U.S.-based Board-certified Neurologist
• 1–10 years of post-residency experience
• Active clinical involvement in acute stroke care
• Experience interpreting stroke-related neuroimaging
• Ability to work independently in an asynchronous environment
• Comfortable adhering to a structured, protocol-driven research workflow