SwePub - search: onr:"swepub:oai:lup.lub.lu.se:...

Enumeration	Reference	Cover	Find
1.	Berggreen, Johan, et al. (author) Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision 2023 In: Healthcare (Switzerland). - : MDPI AG. - 2227-9032. ; 11:2 Journal article (peer-reviewed)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 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.

Berggreen, Johan, et al. (author)
Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision
2023
In: Healthcare (Switzerland). - : MDPI AG. - 2227-9032. ; 11:2
Journal article (peer-reviewed)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 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.

Träfflista för sökning "onr:"swepub:oai:lup.lub.lu.se:182e3260-0e78-442d-b4cf-391332ec4163" "