Cerebral angiography under artificial intelligence algorithm in the design of nursing cooperation plan for intracranial aneurysm patients in craniotomy clipping
Open accessAbstract
This research was to investigate the value of indocyanine green angiography (ICGA) based on maximum interclass variance (Otsu) method in the nursing plan of intracranial aneurysm clipping (ICAC) for intracranial aneurysm patients. An Otsu algorithm was selected to optimize the original images with the optimal threshold. In addition, the algorithm was applied to ICGA images of 86 patients with intracranial aneurysms, who were randomly divided into an experimental group (using ICGA + ICAC+ perioperative nursing) and a control group (ICAC + conventional nursing), to observe the clinical indicators, treatment, complications, nursing satisfaction, and quality of life of patients in two groups. The results showed that the mean square error (MSE), structural similarity (SSIM), and shape error (SE) were 3.71, 0.84, and 0.47, respectively. The length of hospital stay in the experimental group ( 19.9 ± 3.5 days) was significantly shorter than that in the control group ( 23.2 ± 3.0 days), the rate of excellent treatment was significantly higher than that in the control group, and the incidence of complications was lower. WHOQOL-BREF scores of the two groups after nursing intervention were higher than before, and the score in the experimental group was higher than the control group. In addition, the nursing satisfaction was also significantly higher in the experimental group, and the difference was statistically significant ( P < 0.05 ). In conclusion, ICGA based on the Otsu method could effectively evaluate the cerebrovascular morphology during craniotomy and ICAP and improve the surgical efficacy. Combined with perioperative nursing intervention, it could greatly reduce the incidence of postoperative complications, improve the treatment effect and quality of life, and enhance the long-term prognosis.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Xu et al. (46) integrated the Otsu method algorithm into indocyanine green angiography (ICGA) for diagnosing intracranial aneurysms, which significantly improved diagnostic precision, reduced complications, and enhanced patient satisfaction.
“Similarly, Xu et al. (46) integrated the Otsu method algorithm into indocyanine green angiography (ICGA) for diagnosing intracranial aneurysms, which significantly improved diagnostic precision, reduced complications, and enhanced patient satisfaction.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Manual for the Glasgow Outcome Scale-Extended Interview
Wilson, L., Boase, K., Nelson, L. D., Temkin, N. R., Giacino, J. T., Markowitz, A. J., Maas, A. I. R., Menon, D. K., Teasdale, G. M., & Manley, G. T. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2020.7527 · Cited by 347 · see citing works
Nardi, P. D., Elmore, U., Maggi, G., et al. (2019) · Surgical Endoscopy · doi:10.1007/s00464-019-06730-0 · Cited by 321 · see citing works
Medical image fusion method by deep learning
Li, Y., Zhao, J., Lv, Z., & Li, J. (2021) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2020.12.004 · Cited by 280 · see citing works
Analysis of healthcare big data
Lv, Z., & Qiao, L. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.03.039 · Cited by 267 · see citing works
Multi-Disease Prediction Based on Deep Learning: A Survey
Xie, S., Yu, Z., & Lv, Z. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2021.016728 · Cited by 240 · see citing works
Mocco, J., Brown, R. D., Torner, J. C., Capuano, A. W., Fargen, K. M., Raghavan, M. L., Piepgras, D. G., Meissner, I., Huston, J., & on behalf of the International Study of Unruptured Intracranial Aneurysms Investigators (2017) · Neurosurgery · doi:10.1093/neuros/nyx226 · Cited by 142 · see citing works
Chancellor, B., Raz, E., Shapiro, M., et al. (2019) · Neurosurgery · doi:10.1093/neuros/nyz345 · Cited by 106 · see citing works
Park, Y. K., Yi, H.-J., Choi, K.-S., et al. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2018.03.040 · Cited by 62 · see citing works
Imaging the Choroid: From Indocyanine Green Angiography to Optical Coherence Tomography Angiography
Invernizzi, A., Pellegrini, M., Cornish, E. E., et al. (2020) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000307 · Cited by 59 · see citing works
Medical image segmentation automatic quality control: A multi-dimensional approach
Fournel, J., Bartoli, A., Bendahan, D., Guye, M., Bernard, M., Rauseo, E., Khanji, M. Y., Petersen, S. E., Jacquier, A., & Ghattas, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102213 · Cited by 45 · see citing works
Lin, Y., Diao, Y., Du, Y., Zhang, J., Li, L., & Liu, P. (2021) · Microscopy Research and Technique · doi:10.1002/jemt.23893 · Cited by 35 · see citing works
Deshpande, N. M., Gite, S., Pradhan, B., Kotecha, K. V., & Alamri, A. M. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022093 · Cited by 35 · see citing works
Hao, X., & Wei, D. (2019) · Medicine · doi:10.1097/md.0000000000015970 · Cited by 33 · see citing works
An interactive medical image segmentation framework using iterative refinement
Kalshetti, P., Bundele, M., Rahangdale, P., et al. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.02.002 · Cited by 26 · see citing works
Non-coding RNAs role in intracranial aneurysm: General principles with focus on inflammation
Rikhtegar, R., Mosimann, P. J., Rothaupt, J., Mirza‐Aghazadeh‐Attari, M., Hallaj, S., Yousefi, M., Amiri, A., Farashi, E., Kheyrollahiyan, A., & Dolati, S. (2021) · Life Sciences · doi:10.1016/j.lfs.2021.119617 · Cited by 24 · see citing works
Validation of the WHOQOL-Bref instrument in Brazilian sign language (Libras)
Duarte, S. B. R., Chaveiro, N., de Freitas, A. R., Barbosa, M. A., Camey, S. A., Fleck, M. P. D. A., Porto, C. C., Rodrigues, C. L., & Rodríguez‐Martín, D. (2020) · Quality of Life Research · doi:10.1007/s11136-020-02611-5 · Cited by 22 · see citing works
Content Validity of a Psychotherapeutic Intervention Model in Nursing: A Modified e-Delphi Study
Sampaio, F. M. C., Sequeira, C., & Lluch‐Canut, T. (2016) · Archives of Psychiatric Nursing · doi:10.1016/j.apnu.2016.09.007 · Cited by 21 · see citing works
Xu, Y., Wang, R., & Zhao, Y. (2020) · International Journal of Clinical Practice · doi:10.1111/ijcp.13761 · Cited by 21 · see citing works
Sato, H., Kamide, T., Kikkawa, Y., et al. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.01.072 · Cited by 19 · see citing works
The development and understanding of intracranial aneurysm based on rabbit model
Tang, H., Lu, Z., Xue, G., et al. (2020) · Neuroradiology · doi:10.1007/s00234-020-02475-z · Cited by 11 · see citing works
Matas, M., Tokmadžić, V. S., Kozmar, A., Likić, R., & Sekulić, A. (2019) · Medicine · doi:10.1097/md.0000000000017450 · Cited by 9 · see citing works
Zhou, Y., & Li, X. (2021) · Medicine · doi:10.1097/md.0000000000026867 · Cited by 9 · see citing works
Seo, D., Jo, H., Jeong, H., et al. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.11.045 · Cited by 8 · see citing works
Park, J., Kwak, Y., Kim, C., et al. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.03.296 · Cited by 7 · see citing works
Zhou, D.-Z., Wei, D., Xing, W., et al. (2021) · PubMed · Cited by 7 · see citing works
Wilson, J., Screven, R., Volk, J. M., et al. (2012) · Operative Neurosurgery · doi:10.1227/neu.0b013e318257d0c6 · Cited by 6 · see citing works
Sato, T., Bakhit, M. S., Suzuki, K., et al. (2018) · Cerebrovascular Diseases Extra · doi:10.1159/000490872 · Cited by 6 · see citing works
Thanabalasundaram, G., Soon, W. C., Ponnampalam, A., et al. (2021) · British Journal of Neurosurgery · doi:10.1080/02688697.2021.2005771 · Cited by 3 · see citing works
[Prognosis in patients underwent craniotomy for aneurysm clipping with cardiovascular diseases].
Chen, N., Li, R., Dai, S., et al. (2019) · PubMed · doi:10.11817/j.issn.1672-7347.2019.01.007 · Cited by 2 · see citing works
Qingqing, W. A. N. G., Xiaoning, S. H. I., ZHANG, H., et al. (2021) · Minerva Medica · doi:10.23736/s0026-4806.20.06623-9 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence in nursing: an integrative review of clinical and operational impacts
Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1552372 · Cited by 134 · see citing works
- Authors
- Xu, W., Xie, Y., Zhang, X., & Li, W.
- Affiliations
- Zhejiang Chinese Medical University (CN)
- Anesthesia Operation Center, Hainan Hospital of PLA General Hospital, Sanya, 572013 Hainan, China
- Zhejiang Provincial People's Hospital (CN)
- Corresponding author
- Li, Wei
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2022-07-11
- Volume
- 2022
- Pages
- 1-11
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC9293491
- OpenAlex
- W4285035996
- Cited by
- 6
- References
- 30
- Citations by year
- 2025: 2 · 2023: 3 · 2022: 1
- FWCI
- 0.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Neurology › Intracranial Aneurysms: Treatment and Complications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cerebrovascular and Carotid Artery Diseases
- Keywords
- intracranial aneurysm, indocyanine green angiography, Otsu thresholding, perioperative nursing, nursing care plan, postoperative complications, quality of life, WHOQOL-BREF, image segmentation, length of hospital stay
- MeSH terms
- Algorithms, Artificial Intelligence, Intracranial Aneurysm, Cerebral Angiography, Craniotomy, Humans, Indocyanine Green, Quality of Life, Treatment Outcome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
