Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision
(2023) In Healthcare (Switzerland) 11(2).- Abstract
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... (More)
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.
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- author
- Berggreen, Johan LU ; Johansson, Anders LU ; Jahr, John LU ; Möller, Sebastian LU and Jansson, Tomas LU
- organization
- publishing date
- 2023-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, deep learning, hip fracture, nerve blocks, ultrasound
- in
- Healthcare (Switzerland)
- volume
- 11
- issue
- 2
- article number
- 184
- publisher
- MDPI AG
- external identifiers
-
- scopus:85146776556
- pmid:36673552
- ISSN
- 2227-9032
- DOI
- 10.3390/healthcare11020184
- language
- English
- LU publication?
- yes
- id
- 182e3260-0e78-442d-b4cf-391332ec4163
- date added to LUP
- 2023-02-13 13:50:08
- date last changed
- 2024-09-20 08:52:39
@article{182e3260-0e78-442d-b4cf-391332ec4163, abstract = {{<p>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.</p>}}, author = {{Berggreen, Johan and Johansson, Anders and Jahr, John and Möller, Sebastian and Jansson, Tomas}}, issn = {{2227-9032}}, keywords = {{artificial intelligence; deep learning; hip fracture; nerve blocks; ultrasound}}, language = {{eng}}, number = {{2}}, publisher = {{MDPI AG}}, series = {{Healthcare (Switzerland)}}, title = {{Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision}}, url = {{http://dx.doi.org/10.3390/healthcare11020184}}, doi = {{10.3390/healthcare11020184}}, volume = {{11}}, year = {{2023}}, }