@phdthesis{Kundisch2021,
  author    = {Almut Kundisch},
  title     = {Verl{\"a}sslichkeit eines kommerziellen Deep-Learning Algorithmus zur Erkennung intrakranieller Blutungen in notfallm{\"a}{\"s}igen Computertomographien},
  journal    = {Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:9-opus-63852},
  pages     = {55},
  year      = {2021},
  abstract  = {Background: Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services. Methods: In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors. Results: 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1\%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3\%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4\% of ICH and overcalled 1.9\%; RRs missed 10.9\% of ICHs and overcalled 0.2\%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4\%) and under the calvaria (48.5\%). 85\% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3\%), beam-hardening artifacts (18\%), tumors (15.7\%), and blood vessels (7.9\%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results. Conclusion: Complementing human expertise with AI resulted in a 12.2\% increase in ICH detection. The AI algorithm overcalled 1.9\% HCT. Trial registration: German Clinical Trials Register (DRKS-ID: DRKS00023593).},
  language  = {de}
}