Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm

Ji Young Lee, Jong Soo Kim, Tae Yoon Kim, Young Soo Kim

Scientific Reports 2020 article cited by 119

Source Open-access PDF More in Medicine & Health

importance 7/10 ai assisted Intracerebral and Subarachnoid Hemorrhage Research emergency neuroradiology stage: research

Problem

Detecting intracranial haemorrhage and classifying its subtypes on CT images is difficult and needs accurate automated methods.

Why it matters

Fast, accurate haemorrhage detection on CT can speed emergency diagnosis and treatment decisions.

What the AI did

Applied a novel deep-learning ANN algorithm (not back-propagation, no CNN) to CT images to detect intracranial haemorrhage and classify subtypes.

Results

Detection AUC 0.859 (sensitivity 78.0%, specificity 80.0%); best subdivision AUC 0.903 (sensitivity 82.5%, specificity 84.1%).

Impact

Peer-reviewed feasibility study with concrete metrics; no clinical deployment or external validation stated.

Record

Venue	Scientific Reports
Published	2020
Work type	article
Authors	Ji Young Lee, Jong Soo Kim, Tae Yoon Kim, Young Soo Kim
Citations	119 (OpenAlex, at import time)
References	16
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-020-77441-z
Open access	gold
Keywords	deep learning, intracranial haemorrhage, CT imaging, medical diagnosis, classification

Importance rating

7 / 10

significant — thousands to millions affected

ICH is a life-threatening emergency; abstract claims reduced diagnosis time in emergencies but states no scale; feasibility metrics only, no deployment.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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