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Congress: ECR25
Poster Number: C-24137
Type: Poster: EPOS Radiologist (scientific)
Authorblock: D. Männle, M. Langhals, N. Santhanam, C. G. Cho, H. Wenz, C. Groden, F. Siegel, M. E. Maros; Mannheim/DE
Disclosures:
David Männle: Nothing to disclose
Martina Langhals: Nothing to disclose
Nandhini Santhanam: Nothing to disclose
Chang Gyu Cho: Nothing to disclose
Holger Wenz: Nothing to disclose
Christoph Groden: Nothing to disclose
Fabian Siegel: Nothing to disclose
Máté Elöd Maros: Consultant: Non-related consultancy EppData GmbH Consultant: Non-related consultancy Siemens Healthineers AG
Keywords: Artificial Intelligence, Computer applications, Neuroradiology brain, CT, CT-Angiography, RIS, Computer Applications-General, Technology assessment, Ischaemia / Infarction
Methods and materials

For this study a random cohort of 206 German stroke CT reports was retrieved from local RIS/PACS (01/2015-12/2023). A stratified random split (90%-10%) using the ASPECT score [y|n], CTA/CTP [y|n] and sex[F|M] was performed to create training-validation (n=185) and test sets (n=21). The former was further stratified into training (n=160) and validation (n=25) sets. ICL-approaches were compared using 10 runs of a 0-shot- (validation fit/run only), 1-, 5-, and 10-shot disjunct training examples (n_runs=1+30;n_sum_examples=160). State-of-the-art OS-LLMs (n=8) including gemma2[:2b;9b], llama3.1[:8b;70b]|-3.2[3b], mistral[:7b;-small:22b] and mixtral[:8x7b] were tasked with summarising findings to the corresponding impressions. System-level role was provided in English and German (n=2). Models with top-3 validation performance were evaluated on the test set once. To ensure human interpretability, the cosine similarity index (CSI; range:0-1)was calculated instead of using comparisons in various LLM-embedding spaces.