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Deep Learning on Ul...
Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision
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- Berggreen, Johan (författare)
- 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 Hospital
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- Johansson, Anders (författare)
- 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
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- Jahr, John (författare)
- Lund University,Lunds universitet,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Möller, Sebastian (författare)
- 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 Skåne
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- Jansson, Tomas (författare)
- 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 Skåne
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(creator_code:org_t)
- 2023-01-07
- 2023
- Engelska.
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Ingår i: Healthcare (Switzerland). - : MDPI AG. - 2227-9032. ; 11:2
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- artificial intelligence
- deep learning
- hip fracture
- nerve blocks
- ultrasound
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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