Sökning: onr:"swepub:oai:gup.ub.gu.se/306609" > Artificial intellig...
Fältnamn | Indikatorer | Metadata |
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000 | 06175naa a2200697 4500 | |
001 | oai:gup.ub.gu.se/306609 | |
003 | SwePub | |
008 | 240719s2021 | |||||||||||000 ||eng| | |
009 | oai:research.chalmers.se:a8579cf3-00ca-4509-80bf-c8e12cdca426 | |
009 | oai:lup.lub.lu.se:d92f3b81-03cc-40a7-9f32-872248f3d924 | |
024 | 7 | a https://gup.ub.gu.se/publication/3066092 URI |
024 | 7 | a https://doi.org/10.1186/s41747-021-00210-82 DOI |
024 | 7 | a https://research.chalmers.se/publication/5228802 URI |
024 | 7 | a https://lup.lub.lu.se/record/d92f3b81-03cc-40a7-9f32-872248f3d9242 URI |
040 | a (SwePub)gud (SwePub)cthd (SwePub)lu | |
041 | a eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Borrelli, P.u Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital4 aut |
245 | 1 0 | a Artificial intelligence-aided CT segmentation for body composition analysis: a validation study |
264 | c 2021-03-11 | |
264 | 1 | b Springer Science and Business Media LLC,c 2021 |
520 | a BackgroundBody 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-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice. | |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskapx Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi0 (SwePub)303022 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Health Sciencesx Public Health, Global Health, Social Medicine and Epidemiology0 (SwePub)303022 hsv//eng |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Annan medicinteknik0 (SwePub)206992 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Other Medical Engineering0 (SwePub)206992 hsv//eng |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng |
653 | a Body composition | |
653 | a Muscles | |
653 | a Neural networks (computer) | |
653 | a Subcutaneous fat | |
653 | a Tomography (x-ray | |
653 | a computed) | |
653 | a visceral adipose-tissue | |
653 | a tomography | |
653 | a sarcopenia | |
653 | a software | |
653 | a cancer | |
653 | a Radiology | |
653 | a Nuclear Medicine & Medical Imaging | |
653 | a Muscles | |
700 | 1 | a Kaboteh, R.u Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital4 aut |
700 | 1 | a Enqvist, Olof,d 1981u Chalmers University of Technology,Eigenvision AB4 aut0 (Swepub:cth)enolof |
700 | 1 | a Ulén, Johannesu Chalmers University of Technology4 aut0 (Swepub:lu)math-jsu |
700 | 1 | a Trägårdh, Elinu Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Nuclear medicine, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital4 aut0 (Swepub:lu)klin-etr |
700 | 1 | a Kjölhede, Henrik,d 1981u Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för urologi,Institute of Clinical Sciences, Department of Urology,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital4 aut0 (Swepub:lu)med-hrk |
700 | 1 | a Edenbrandt, Lars,d 1957u Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,University of Gothenburg4 aut0 (Swepub:lu)klfy-led |
710 | 2 | a Sahlgrenska universitetssjukhusetb Sahlgrenska University Hospital4 org |
773 | 0 | t European Radiology Experimentald : Springer Science and Business Media LLCg 5:1q 5:1x 2509-9280 |
856 | 4 | u https://eurradiolexp.springeropen.com/track/pdf/10.1186/s41747-021-00210-8 |
856 | 4 | u https://research.chalmers.se/publication/522880/file/522880_Fulltext.pdfx primaryx freey FULLTEXT |
856 | 4 | u http://dx.doi.org/10.1186/s41747-021-00210-8x freey FULLTEXT |
856 | 4 8 | u https://gup.ub.gu.se/publication/306609 |
856 | 4 8 | u https://doi.org/10.1186/s41747-021-00210-8 |
856 | 4 8 | u https://research.chalmers.se/publication/522880 |
856 | 4 8 | u https://lup.lub.lu.se/record/d92f3b81-03cc-40a7-9f32-872248f3d924 |
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