Cargando…

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

BACKGROUND: Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intell...

Descripción completa

Detalles Bibliográficos
Autores principales:	Borrelli, Pablo, Kaboteh, Reza, Enqvist, Olof, Ulén, Johannes, Trägårdh, Elin, Kjölhede, Henrik, Edenbrandt, Lars
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer International Publishing 2021
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947128/ https://www.ncbi.nlm.nih.gov/pubmed/33694046 http://dx.doi.org/10.1186/s41747-021-00210-8

Ejemplares similares

Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
por: Ying, Thomas, et al.
Publicado: (2021)

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
por: Trägårdh, Elin, et al.
Publicado: (2020)

AI-based detection of lung lesions in [(18)F]FDG PET-CT from lung cancer patients
por: Borrelli, Pablo, et al.
Publicado: (2021)

Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer
por: Borrelli, Pablo, et al.
Publicado: (2022)

Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians
por: Trägårdh, Elin, et al.
Publicado: (2022)

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival
por: Polymeri, Eirini, et al.
Publicado: (2019)

Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [(18)F]-PSMA-1007 PET-CT
por: Trägårdh, Elin, et al.
Publicado: (2022)

Automated classification of PET‐CT lesions in lung cancer: An independent validation study
por: Borrelli, Pablo, et al.
Publicado: (2022)

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
por: Sadik, May, et al.
Publicado: (2021)

Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin’s Lymphoma Patients Staged with [(18)F]FDG PET/CT—a Retrospective Study
por: Sadik, May, et al.
Publicado: (2022)

Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging
por: Saito, Shintaro, et al.
Publicado: (2021)

Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth
por: Sartor, Hanna, et al.
Publicado: (2020)

Common carotid segmentation in (18)F‐sodium fluoride PET/CT scans: Head‐to‐head comparison of artificial intelligence‐based and manual method
por: Piri, Reza, et al.
Publicado: (2022)

Application of an artificial intelligence-based tool in [(18)F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma
por: Sachpekidis, Christos, et al.
Publicado: (2023)

3D skeletal uptake of (18)F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer
por: Lindgren Belal, Sarah, et al.
Publicado: (2017)

Correction to: 3D skeletal uptake of (18)F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer
por: Belal, Sarah Lindgren, et al.
Publicado: (2019)

PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence‐based segmentation
por: Piri, Reza, et al.
Publicado: (2022)

Bone Scan Index as a prognostic imaging biomarker during androgen deprivation therapy
por: Reza, Mariana, et al.
Publicado: (2014)

Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index
por: Lindgren Belal, Sarah, et al.
Publicado: (2023)

AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT
por: Gålne, Anni, et al.
Publicado: (2023)

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies
por: Molnar, David, et al.
Publicado: (2021)

The use of a proposed updated EARL harmonization of (18)F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations
por: Ly, John, et al.
Publicado: (2019)

Adding attenuation corrected images in myocardial perfusion imaging reduces the need for a rest study
por: Trägårdh, Elin, et al.
Publicado: (2013)

Prognosis of patients without perfusion defects with and without rest study in myocardial perfusion scintigraphy
por: Edenbrandt, Lars, et al.
Publicado: (2013)

Small average differences in attenuation corrected images between men and women in myocardial perfusion scintigraphy: a novel normal stress database
por: Trägårdh, Elin, et al.
Publicado: (2011)

A prospective study to evaluate the intra-individual reproducibility of bone scans for quantitative assessment in patients with metastatic prostate cancer
por: Reza, Mariana, et al.
Publicado: (2018)

Nuclear medicine technologists are able to accurately determine when a myocardial perfusion rest study is necessary
por: Trägårdh, Elin, et al.
Publicado: (2012)

Referring physicians underestimate the extent of abnormalities in final reports from myocardial perfusion imaging
por: Trägårdh, Elin, et al.
Publicado: (2012)

Detection of lymph node metastases in patients with prostate cancer: Comparing conventional and digital [(18)F]‐fluorocholine PET‐CT using histopathology as a reference
por: Bjöersdorff, Mimmi, et al.
Publicado: (2022)

Progression of bone metastases in patients with prostate cancer - automated detection of new lesions and calculation of bone scan index
por: Kaboteh, Reza, et al.
Publicado: (2013)

Perfusion vector—a new method to quantify myocardial perfusion scintigraphy images: a simulation study with validation in patients
por: Minarik, David, et al.
Publicado: (2015)

Bone Scan Index: a prognostic imaging biomarker for high-risk prostate cancer patients receiving primary hormonal therapy
por: Kaboteh, Reza, et al.
Publicado: (2013)

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation
por: Arsalan, Muhammad, et al.
Publicado: (2019)

Design of Travel Route Identification and Scheduling System Based on Artificial Intelligence-Aided Image Segmentation
por: Hou, Tianchen
Publicado: (2022)

Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome
por: Herrmann, Peter, et al.
Publicado: (2021)

Evaluation of inter-departmental variability of ejection fraction and cardiac volumes in myocardial perfusion scintigraphy using simulated data
por: Trägårdh, Elin, et al.
Publicado: (2015)

Value assessment of artificial intelligence in medical imaging: a scoping review
por: Fasterholdt, Iben, et al.
Publicado: (2022)

Correction: Value assessment of artificial intelligence in medical imaging: a scoping review
por: Fasterholdt, Iben, et al.
Publicado: (2023)

Cervical cancer screening aided by artificial intelligence, China
por: Zhu, Xingce, et al.
Publicado: (2023)

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening
por: Spadaccini, Marco, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_p21qtor6s0ps8lk5tad4mnf91j