| Yazarlar (10) |
Beyza Tayfur
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Paul Ritsche
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Olivia Sunderlik
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Madison Wheeler
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Eric Ramirez
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Jacob Leuteneker
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Oliver Faude
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Martino V Franchi
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Alexa K Johnson
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Riann Palmieri-Smith
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| Özet |
| ObjectiveDeep learning approaches such as DeepACSA enable automated segmentation of muscle ultrasound cross-sectional area (CSA). Although they provide fast and accurate results, most are developed using data from healthy populations. The changes in muscle size and quality following anterior cruciate ligament (ACL) injury challenges the validity of these automated approaches in the ACL population. Quadriceps muscle CSA is an important outcome following ACL injury; therefore, our aim was to validate DeepACSA, a convolutional neural network (CNN) approach for ACL injury.MethodsQuadriceps panoramic CSA ultrasound images (vastus lateralis [VL] n = 430, rectus femoris [RF] n = 349, and vastus medialis [VM] n = 723) from 124 participants with an ACL injury (age 22.8 ± 7.9 y, 61 females) were used to train CNN models. For VL and RF, combined models included extra images from healthy ... |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayımlanan tam makale |
| Dergi Adı | Ultrasound in Medicine & Biology |
| Dergi Tarandığı Indeksler | |
| Makale Dili | İngilizce |
| Basım Tarihi | 02-2025 |
| Cilt No | 51 |
| Sayı | 2 |
| Sayfalar | 364 / 372 |
