TY - JOUR
T1 - Decreasing false-positive detection of intracranial hemorrhage (ICH) using RAPID ICH 3
AU - Sreekrishnan, Anirudh
AU - Giurgiutiu, Dan Victor
AU - Kitamura, Felipe
AU - Martinelli, Carlos
AU - Abdala, Nitamar
AU - Haerian, Hafez
AU - Dehkharghani, Seena
AU - Kwok, Keith
AU - Yedavalli, Vivek
AU - Heit, Jeremy J.
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/12
Y1 - 2023/12
N2 - Introduction: The prompt detection of intracranial hemorrhage (ICH) on a non-contrast head CT (NCCT) is critical for the appropriate triage of patients, particularly in high volume/high acuity settings. Several automated ICH detection tools have been introduced; however, at present, most suffer from suboptimal specificity leading to false-positive notifications. Methods: NCCT scans from 4 large databases were evaluated for the presence of an ICH (IPH, IVH, SAH or SDH) of >0.4 ml using fully-automated RAPID ICH 3.0 as compared to consensus detection from at least two neuroradiology experts. Scans were excluded for (1) severe CT artifacts, (2) prior neurosurgical procedures, or (3) recent intravenous contrast. ICH detection accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios by were determined. Results: A total of 881 studies were included. The automated software correctly identified 453/463 ICH-positive cases and 416/418 ICH-negative cases, resulting in a sensitivity of 97.84% and specificity 99.52%, positive predictive value 99.56%, and negative predictive value 97.65% for ICH detection. The positive and negative likelihood ratios for ICH detection were similarly favorable at 204.49 and 0.02 respectively. Mean processing time was <40 seconds. Conclusions: In this large data set of nearly 900 patients, the automated software demonstrated high sensitivity and specificity for ICH detection, with rare false-positives.
AB - Introduction: The prompt detection of intracranial hemorrhage (ICH) on a non-contrast head CT (NCCT) is critical for the appropriate triage of patients, particularly in high volume/high acuity settings. Several automated ICH detection tools have been introduced; however, at present, most suffer from suboptimal specificity leading to false-positive notifications. Methods: NCCT scans from 4 large databases were evaluated for the presence of an ICH (IPH, IVH, SAH or SDH) of >0.4 ml using fully-automated RAPID ICH 3.0 as compared to consensus detection from at least two neuroradiology experts. Scans were excluded for (1) severe CT artifacts, (2) prior neurosurgical procedures, or (3) recent intravenous contrast. ICH detection accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios by were determined. Results: A total of 881 studies were included. The automated software correctly identified 453/463 ICH-positive cases and 416/418 ICH-negative cases, resulting in a sensitivity of 97.84% and specificity 99.52%, positive predictive value 99.56%, and negative predictive value 97.65% for ICH detection. The positive and negative likelihood ratios for ICH detection were similarly favorable at 204.49 and 0.02 respectively. Mean processing time was <40 seconds. Conclusions: In this large data set of nearly 900 patients, the automated software demonstrated high sensitivity and specificity for ICH detection, with rare false-positives.
KW - Automated detection
KW - CT Imaging
KW - ICH
KW - RAPID
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U2 - 10.1016/j.jstrokecerebrovasdis.2023.107396
DO - 10.1016/j.jstrokecerebrovasdis.2023.107396
M3 - Article
C2 - 37883825
AN - SCOPUS:85174506991
SN - 1052-3057
VL - 32
JO - Journal of Stroke and Cerebrovascular Diseases
JF - Journal of Stroke and Cerebrovascular Diseases
IS - 12
M1 - 107396
ER -