Search: onr:"swepub:oai:lup.lub.lu.se:182e3260-0e78-442d-b4cf-391332ec4163" > Deep Learning on Ul...
Fältnamn | Indikatorer | Metadata |
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000 | 04867naa a2200397 4500 | |
001 | oai:lup.lub.lu.se:182e3260-0e78-442d-b4cf-391332ec4163 | |
003 | SwePub | |
008 | 230213s2023 | |||||||||||000 ||eng| | |
024 | 7 | a https://lup.lub.lu.se/record/182e3260-0e78-442d-b4cf-391332ec41632 URI |
024 | 7 | a https://doi.org/10.3390/healthcare110201842 DOI |
040 | a (SwePub)lu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a art2 swepub-publicationtype |
072 | 7 | a ref2 swepub-contenttype |
100 | 1 | a Berggreen, Johanu Lund University,Lunds universitet,Medicinsk teknik, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LTH profilområde: Teknik för hälsa,LTH profilområden,Lunds Tekniska Högskola,Biomedical Engineering, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH,Skåne University Hospital4 aut0 (Swepub:lu)jo0624be |
245 | 1 0 | a Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision |
264 | c 2023-01-07 | |
264 | 1 | b MDPI AG,c 2023 |
520 | a The number of hip fractures per year worldwide is estimated to reach 6 million by the year 2050. Despite the many advantages of regional blockades when managing pain from such a fracture, these are used to a lesser extent than general analgesia. One reason is that the opportunities for training and obtaining clinical experience in applying nerve blocks can be a challenge in many clinical settings. Ultrasound image guidance based on artificial intelligence may be one way to increase nerve block success rate. We propose an approach using a deep learning semantic segmentation model with U-net architecture to identify the femoral nerve in ultrasound images. The dataset consisted of 1410 ultrasound images that were collected from 48 patients. The images were manually annotated by a clinical professional and a segmentation model was trained. After training the model for 350 epochs, the results were validated with a 10-fold cross-validation. This showed a mean Intersection over Union of 74%, with an interquartile range of 0.66–0.81. | |
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 |
653 | a artificial intelligence | |
653 | a deep learning | |
653 | a hip fracture | |
653 | a nerve blocks | |
653 | a ultrasound | |
700 | 1 | a Johansson, Andersu Lund University,Lunds universitet,Medicinsk teknik, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LTH profilområde: Teknik för hälsa,LTH profilområden,Lunds Tekniska Högskola,Biomedical Engineering, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH4 aut0 (Swepub:lu)anes-ajo |
700 | 1 | a Jahr, Johnu Lund University,Lunds universitet,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Department of Clinical Sciences, Lund,Faculty of Medicine4 aut0 (Swepub:lu)anes-jja |
700 | 1 | a Möller, Sebastianu Lund University,Lunds universitet,Medicinsk teknik, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LTH profilområde: Teknik för hälsa,LTH profilområden,Lunds Tekniska Högskola,LU profilområde: Ljus och material,Lunds universitets profilområden,Biomedical Engineering, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Light and Materials,Lund University Profile areas,Region Skåne4 aut0 (Swepub:lu)se6152st |
700 | 1 | a Jansson, Tomasu Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Medicinsk teknik, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LTH profilområde: Teknik för hälsa,LTH profilområden,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Biomedical Engineering, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH,Region Skåne4 aut0 (Swepub:lu)elma-tja |
710 | 2 | a Medicinsk teknik, Lundb Sektion V4 org |
773 | 0 | t Healthcare (Switzerland)d : MDPI AGg 11:2q 11:2x 2227-9032 |
856 | 4 | u http://dx.doi.org/10.3390/healthcare11020184x freey FULLTEXT |
856 | 4 8 | u https://lup.lub.lu.se/record/182e3260-0e78-442d-b4cf-391332ec4163 |
856 | 4 8 | u https://doi.org/10.3390/healthcare11020184 |
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