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Berggreen J, Johansson A, Jahr J, Möller S, Jansson T
Healthcare (Basel) 11 (2) - [2023-01-07; online 2023-01-07]
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.
PubMed 36673552
DOI 10.3390/healthcare11020184
Crossref 10.3390/healthcare11020184
pmc: PMC9859453
pii: healthcare11020184