Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography.

Tom Finck¹, Julia Moosbauer², Monika Probst¹, Sarah Schlaeger¹, Madeleine Schuberth¹, David Schinz¹, Mehmet Yiğitsoy², Sebastian Byas², Claus Zimmer¹, Franz Pfister², Benedikt Wiestler¹

¹Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany.

²DeepC GmbH, Atelierstraße 29, 81671 Munich, Germany.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed to…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.