Evidence map›Paper›PMID 39585389›Full record

ArticleClinical neuroradiology2025

Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.

Jan Boriesosdick, Iram Shahzadi, Long Xie, Bogdan Georgescu, Eli Gibson, Lynn Johann Frohwein, Saher Saeed, Nina P Haag, Sebastian Horstmeier, Christoph Moenninghoff and 4 more

Abstract read
PubMed Publisher
In one paragraph

Article in Clinical neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Jan BoriesosdickDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Iram ShahzadiDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany. iram.shahzadi@siemens-healthineers.com.
Long XieDigital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
Bogdan GeorgescuDigital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
Eli GibsonDigital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
Lynn Johann FrohweinSiemens Healthineers AG, Erlangen, Germany.
Saher SaeedDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Nina P HaagDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Sebastian HorstmeierDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Christoph MoenninghoffDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Julius Henning NiehoffDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Alexey SurovDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Jan BorggrefeDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Jan Robert KroegerDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDeep learning (DL) methods for detecting large vessel occlusion (LVO) in acute ischemic stroke (AIS) show promise, but the effect of computed tomography angiography (CTA) image quality on DL performance is unclear. Our study investigates the impact of improved image quality from Photon Counting Computed Tomography (PCCT) on LVO detection in AIS using a DL-based software prototype developed by a commercial vendor, which incorporates a novel deep learning architecture. MATERIALS AND

methods443 cases that underwent stroke diagnostics with CTA were included. Positive cases featured vascular occlusions in the Internal Carotid Artery (ICA), M1, and M2 segments of the Middle Cerebral Artery (MCA). Negative cases showed no vessel occlusion on CTA. The performance of the DL-based LVO detection software prototype was assessed using Syngo.via version VB80.

resultsOur study included 267 non-occlusion cases and 176 cases. Among them, 150 cases were scanned via PCCT (no occlusion = 100, ICA and M1 = 41, M2 = 9), while 293 cases were scanned using conventional CT (no occlusion = 167, ICA and M1 = 89, M2 = 37). Independent of scanner type, the algorithm showed sensitivity and specificity of 70.5 and 98.9% for the detection of all occlusions. DL algorithm showed improved performance after excluding M2 occlusions (sensitivity 86.2%). After stratification by scanner type, the algorithm showed significantly a trend towards better performance (p = 0.013) on PCCT CTA images for the detection of all occlusions (sensitivity 84.0%, specificity 99%) compared to CTA images from conventional CT scanner (sensitivity 65.1%, specificity 98.8%). The detection of M2 occlusions was also better on PCCT CTA images (sensitivity 55.6%) compared to conventional scanner CTA images (sensitivity 18.9%), but the sample size for M2 occlusions was limited, and further research is needed to confirm these findings.

conclusionOur study suggests that PCCT CTA images may offer improved detection of large vessel occlusion, particularly for M2 occlusions. However further research is needed to confirm these findings. One of the limitations of our study is the inability to exclude the presence of a perfusion deficit, despite ruling out vascular occlusion, due to the lack of CT perfusion (CTP) imaging data. Future research may investigate CNNs by leveraging both CTA and CTP images from PCCT for improved performance.

Indexed as

Cerebral AngiographyComputed Tomography AngiographyDeep LearningIschemic StrokeMultidetector Computed TomographyAgedAged, 80 and overFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedSensitivity and SpecificityComputed tomography angiographyDeep learningLarge vessel occlusionPhoton counting computed tomography

Identifiers

PMID39585389

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.