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Introduction Many climbers believe that they are stronger in crimp finger position than in open hand position. However, compared to open hand, crimped fingers are associated with higher pulley forces increasing the risk of injuries. Climbing expertise may influence the estimation of strength, i.e., the better the climber, the better the self-assessment. This study therefore aimed to find out whether climbing expertise influences self-assessment of finger flexor strength in half-crimp and in open hand position. Methods Data was collected at the Hands-On Science Booth of the Climbing World Championships in Bern. Participants had to fill out a questionnaire including a self-assessment of their climbing expertise and of their maximum finger strength for both hands as well as both finger positions. Afterwards, maximum finger strength was measured on an instrumented campus board: Participants placed the to-be-measured hand on a self-selected rung (depth of 23 mm) and then tried to transfer as much force as possible from their feet to their fingers. Results The analysis was based on 38 intermediate and 36 advanced climbers. Due to the limited number of participants in the lower grade (n = 0) and elite (n = 2) level, those skill levels were not considered. Advanced climbers generated significantly greater forces than intermediate climbers across all four measured conditions (t-tests, all p < 0.01). For both groups, neither in the dominant nor in the non-dominant hand a significant difference in maximum force was observed, e.g., dominant hand, intermediates: or advanced climbers: . Intermediate climbers did neither over- nor underestimate their strength in half-crimp position compared to open hand (paired t-test, p = 0.91 for dominant, p = 0.077 for non-dominant hand). In contrast to the dominant hand, advanced climbers significantly overestimated their strength in half-crimp position for the non-dominant hand (on average 9%, Cohen’s d 0.64, p < 0.01). Discussion/Conclusion Our results confirm the positive correlation between finger strength and climbing level. We also confirm that on a 23 mm rung, greater forces can be generated with in open hand compared to half-crimp (Winkler et al., 2023). With larger hold depths, force generated in open hand significantly increases (Amca et al., 2012), while for smaller holds, force exerted in half-crimp position exceeds that of open hand (Winkler et al., 2023). Hence, at least for larger holds, we recommend adopting an open hand position as preventive measure against finger injuries. Advanced climbers may tend to inaccurately self-assess their strength due to their greater engagement with peers, potentially leading to the circulation of misinformation. Note that participants were instructed to provide a general self-assessment of their strength rather than for a 23 mm deep rung, i.e., they may have had a smaller hold in mind. References Amca, A. M., Vigouroux, L., Aritan, S., & Berton, E. (2012). Effect of hold depth and grip technique on maximal finger forces in rock climbing. Journal of Sports Sciences, 30(7), 669-677. https://doi.org/10.1080/02640414.2012.658845 Winkler, M., Künzell, S., & Auguste, C. (2023). Competitive performance predictors in speed climbing, bouldering, and lead climbing. Journal of Sport Sciences, 41(8), 736-746. https://doi.org/10.1080/02640414.2023.2239598

Introduction Lower limb muscle strength is an important predictor of sports performance, injury risk and frailty in ageing. The strength of a muscle is determined by its geometry and neuronal factors. Muscle geometry can be subdivided into architecture and morphology. Muscle morphology describes shape characteristics such as anatomical cross-sectional area (ACSA), thickness or volume (Maden-Wilkinson et al., 2021). Muscle architecture is determined by muscle fascicle length and the insertion angle of the muscle fascicles in the aponeuroses and describes the orientation of the muscle fibers relative to their force generation axis (Lieber & Friden, 2000). Muscle geometry is associated to physical performance and strength in humans (Maden-Wilkinson et al., 2021; Werkhausen et al., 2022) and is therefore a main research interest. A cost-effective and participant friendly method to validly and reliably assess muscle geometry is ultrasonography. However, a major limitation of ultrasonography is the subjectivity of image acquisition and the time-consuming image analysis (Ritsche et al., 2021; Ritsche, Wirth, et al., 2022; Ritsche et al., 2023). Moreover, image characteristics are massively influenced by the ultrasonography device used (Ritsche, Schmid, et al., 2022) as well as the muscle region scanned (Monte & Franchi, 2023). This poses constraints on the generalizability of existing automated image analysis approaches. The goal of this series of studies is therefore to optimize the ultrasonography acquisition and data analysis procedures by developing open-source software packages. Secondly, we aim to apply these methods in a sports performance context and describe the relevance of muscle geometry. Methods To streamline the time-consuming and subjective process of image analysis, we developed open-source and user-friendly software packages for muscle geometry analysis in lower limb muscles. We developed a semi-automated algorithm “ACSAuto” for assisted analysis of muscle ACSA using common image filtering processes (Ritsche et al., 2021). Given the limited generalizability and required user input of this approach, we developed two fully automated software applications, “DeepACSA” and “DL_Track_US”, using convolutional neural networks for more time efficient and robust analysis of lower limb muscle geometry (Ritsche et al., 2023; Ritsche, Wirth, et al., 2022; Ritsche et al., in press). We compared the predictions in an unseen test set to the current state-of-the-art, manual analysis, in order to evaluate the performance of our algorithms. To broaden the application of ultrasonography for evaluating muscle geometry in a sports context, we investigated the validity of a low-cost mobile ultrasonography device compared to a high-end counterpart in assessing various muscle architectural parameters in healthy adults (Ritsche, Schmid, et al., 2022).The mobile ultrasonography setup consisted of a smartphone and a portable probe, enabling practitioners high flexibility in the assessment of muscle architecture. We further investigated the link between muscle geometry and performance among soccer players. In one study, we focused on the m. biceps femoris long head in under-13 to under-15 youth players, assessing architecture and morphology at the mid-muscle point and correlating these with their sprint times and maximum velocity (Ritsche et al., 2020). In a further study, we analyzed the mm. vastus lateralis and rectus femoris in both youth and adult players of both sexes, evaluating muscle geometry at various muscle lengths alongside their knee extension strength during isometric and isokinetic conditions (Ritsche et al., in preparation and under review). Results Both ACSAuto and DeepACSA showed high comparability in assessing lower limb muscle ACSA with standard error of measurement lower than one cm2 (SEM ranging from 1.2 to 9.5%; Ritsche et al., 2021; Ritsche, Wirth, et al., 2022). Moreover, DeepACSA provided fast and objective analysis comparable to manual segmentation with no supervision of the analysis process needed. The time needed for analysis was reduced by a factor of 10. DL_Track_US demonstrated high comparability to manual muscle architecture analysis of images and videos, i.e. dynamic situations, (Ritsche et al., 2023; Ritsche et al., in press) and a reduction in the duration of analysis by a factor of 100. The mobile ultrasonography system showed a high degree of reliability and comparability only for m. gastrocnemius medialis architecture assessment, with a standard error of measurement lower than 10% for all architectural parameters (Ritsche, Schmid, et al., 2022). Thus, its reliability and comparability depended on the muscle assessed. We observed relevant correlations between muscle ACSA in young and adult male soccer players as well as in female soccer players and performance (Ritsche et al., 2020; Ritsche et al., unpublished). Moreover, we observed changes in muscle geometry with age and differences between males and females. Specifically, m. biceps femoris ACSA was strongly correlated with 30m sprint times and maximal velocity (r = -0.61 and r = 0.61, respectively), highlighting its importance in athletic performance (Ritsche et al., 2020). M. vastus lateralis ACSA at 50% of muscle length was most frequently related to knee extension strength (r = 0.40 - 0.53), which was observed in both sexes and across several age groups of male soccer players (Ritsche et al., in preparation and under review). Relevant correlations occurred more frequently in older age groups and higher knee extension velocities. Interestingly, we did not observe relevant correlations between muscle architecture and performance in the mm. biceps femoris and vastus lateralis. Discussion/Conclusion The results of this series studies so far led to three main insights. Firstly, the development of the “ACSAuto”, “DeepACSA” and “DL_Track_US” tools, utilizing semi-automated and fully automated analysis techniques applying deep learning algorithms, marked another step forward in overcoming the subjectivity and time consuming image evaluation. In a user-friendly way, these tools enable reproducible and objective analyses of muscle geometry in ultrasonography images. Secondly, with technological advancements, assessing muscle geometry with ultrasonography is possible using a smartphone and a probe, and often gives comparable results to high-end devices (Ritsche, Schmid, et al., 2022). This allows for a broader and more versatile application of muscle geometry assessment. However, our results highlight the need for a selective approach based on the muscle group being assessed and technical improvements of existing devices. Lastly, our findings across several investigations reveal a relevant positive correlation between muscle ACSA and performance metrics such as sprint times and knee extension strength (Ritsche et al., 2020; Ritsche et al., unpublished), corroborating previous research (Maden-Wilkinson et al., 2021; Monte & Franchi, 2023). The relationship was more pronounced in older age groups, suggesting that muscle geometry's influence on performance may amplify with athletic maturity. Apart from that, we observed the relationship in the m. vastus lateralis to be region- and contraction velocity-dependent. In agreement with Werkhausen et al. (2022), no relation of muscle architecture with strength when assessed in a static resting position was observed. This highlights the need for a potential shift towards assessing changes in muscle geometry during contraction rather than in static situations when evaluating the relation between muscle geometry and performance. Finally, remaining challenges include the comparability of muscle geometry assessment in the literature, the analysis methods used and the low generalizability of available automated analysis approaches (ours included). There is a clear need for methodological consensus on the assessment of muscle geometry when using ultrasonography, and more versatile analysis approaches are needed to enable an easy, generalizable and reproducible analysis of images and videos. Therefore, future works should target to establish assessment and analysis guidelines of muscle geometry in ultrasonography images to increase the comparability and reproducibility of results. Moreover, assessing changes in muscle geometry during contraction rather than during rest should be focused. References Lieber, R. L., & Friden, J. (2000). Functional and clinical significance of skeletal muscle architecture. Muscle Nerve, 23(11), 1647–1666. https://doi.org/10.1002/1097-4598(200011)23:11%3C1647::aid-mus1%3E3.0.co;2-m Maden-Wilkinson, T. M., Balshaw, T. G., Massey, G. J., & Folland, J. P. (2021). Muscle architecture and morphology as determinants of explosive strength. European Journal of Applied Physiology, 121(4), 1099–1110. https://doi.org/10.1007/s00421-020-04585-1 Monte, A., & Franchi, M. V. (2023). Regional muscle features and their association with knee extensors force production at a single joint angle. European Journal of Applied Physiology, 123, 2239-2248. https://doi.org/10.1007/s00421-023-05237-w Ritsche, P., Bernhard, T., Roth, R., Lichtenstein, E., Keller, M., Zingg, S., Franchi, M. V., & Faude, O. (2020). M. biceps femoris long head architecture and sprint ability in youth soccer players. International Journal of Sports Physiology and Performance, 16(11), 1616-1624. https://doi.org/10.1123/ijspp.2020-0726 Ritsche, P., Schmid, R., Franchi, M. V., & Faude, O. (2022). Agreement and reliability of lower limb muscle architecture measurements using a portable ultrasound device. Frontiers in Physiology, 13, Article 981862. https://doi.org/10.3389/fphys.2022.981862 Ritsche, P., Seynnes, O., & Cronin, N. (2023). DL_Track_US: A python package to analyse muscleultrasonography images. Journal of Open Source Software, 8(85), Article 5206. https://doi.org/10.21105/joss.05206 Ritsche, P., Wirth, P., Cronin, N. J., Sarto, F., Narici, M. V., Faude, O., & Franchi, M. V. (2022). DeepACSA: Automatic segmentation of cross-sectional area in ultrasound images of lower limb muscles using deep learning. Medicine & Science in Sports & Exercise, 54(12), 2188-2195. https://doi.org/10.1249/MSS.0000000000003010 Ritsche, P., Wirth, P., Franchi, M. V., & Faude, O. (2021). ACSAuto-semi-automatic assessment of human vastus lateralis and rectus femoris cross-sectional area in ultrasound images. Scientific Reports, 11, Article 13042. https://doi.org/10.1038/s41598-021-92387-6 Werkhausen, A., Gløersen, Ø., Nordez, A., Paulsen, G., Bojsen-Møller, J., & Seynnes, O. R. (2022). Rate of force development relationships to muscle architecture and contractile behavior in the human vastus lateralis. Scientific Reports, 12, Article 21816. https://doi.org/10.1038/s41598-022-26379-5

B-mode ultrasound is commonly used to image musculoskeletal tissues, but one major bottleneck is data analysis. Manual analysis is commonly deployed for assessment of muscle thickness, pennation angle and fascicle length in muscle ultrasonography images. However, manual analysis is somewhat subjective, laborious and requires thorough experience. We provide an openly available algorithm (DL_Track) to automatically analyze muscle architectural parameters in ultrasonography images or videos of human lower limb muscles. We trained two different neural networks (classic U-net [Ronneberger et al., 2021] and U-net with VGG16 [Simonyan & Zisserman, 2015] pretrained encoder) one to detect muscle fascicles and another to detect muscle aponeuroses using a set of labelled musculoskeletal ultrasound images. We included images from four different devices of the vastus lateralis, gastrocnemius medialis, tibilias anterior and soleus. In total, we included 310 images for the fascicle model and 570 images for the aponeuroses model, which we augmented to about 1,700 images per set. Each dataset was randomly split into a training and test set for model training, using a common 80/20 train/test split. We determined the best performing model based on intersection-over-union and loss metrics calculated during model training. We compared neural network predictions on an unseen test set consisting of 35 images to those obtained via manual analysis and two existing semi/automated analysis approaches (SMA and Ultratrack). Across the set of 35 unseen images, the mean differences between DL_Track and manual analysis were for fascicle length -2.4 mm (95% compatibility interval (CI) = -3.7 to -1.2), for pennation angle 0.6° (-0.2 to 1.4), and for muscle thickness -0.6 mm (-1.2 to 0.002). The corresponding values comparing DL_Track with SMA were for fascicle length 5.2 mm (1.3 to 9.0), for pennation angle -1.4° (-2.6 to -0.4) and for muscle thickness -0.9 mm (-1.5 to -0.3) respectively. ICC values between DL_Track and Ultratrack were 0.19 (0.00 to 0.35) for medial gastrocnemius passive contraction, 0.79 (0.77 to 0.81) for medial gastrocnemius maximal voluntary contraction, 0.88 (0.87 to 0.89) for calf raise, 0.67 (0.07 to 0.86) for medial gastrocnemius during walking, 0.80 (0.79 to 0.82) for tibialis passive plantar and dorsiflexion, and 0.85 (0.83 to 0.86) for tibialis anterior maximum voluntary contraction. Our method is fully automated and can estimate fascicle length, pennation angle and muscle thickness from single images or videos in multiple superficial muscles. For single images, the method gave results that are in agreement with those produced by SMA or manual analysis. Similarly, for videos, there was overlap between the results produced with Ultratrack and our method. In contrast to Ultratrack, DL_Track analyzes each frame independently of the previous frames, which might explain the observerd variability. References Ronneberger, O., Fischer, P., & Brox, T. (2021). U-Net: Convolutional networks for biomedical image segmentation. arXiv. https://doi.org/10.48550/arXiv.1505.04597 Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556

Although ultrasonography is commonly used to assess muscle architecture, high-end ultrasonography devices lack in portability and are expensive. Recently, ultrasonography devices consisting solely of a probe, an app, and/or a mobile display were introduced. These devices have lower acquisition costs than high-end devices and demonstrate higher portability. Portable systems would allow for cost-effective, on-site athlete or patient screenings of muscle architecture as well as diagnosis of pathologies or injury. Furthermore, portable systems can be easily transported to allow for uncomplicated assessment of athletes belonging to different teams or patients in different hospital units. Nonetheless, the reliability of portable systems and the comparability to high-end devices for muscle architecture assessment have not been investigated. We investigated the agreement and reliability of a portable and handheld ultrasonography system for muscle architecture measurements in human lower limb muscles. We acquired images of the rectus femoris (RF), vastus lateralis (VL) and gastrocnemius medialis (GM) in 36 active healthy participants (15 female, 21 male) at 50% of muscle length using the handheld Lumify (L12-4, linear array 37 mm, Philips Healthcare, Amsterdam, Netherlands) and a high-end laboratory device (ACUSON Juniper, linear-array 54 mm, 12L3, SIEMENS Healthineers, Erlangen, Germany). Muscle length was determined as distance between the proximal and distal muscle tendon junction. We compared measurements of muscle fascicle length, pennation angle and thickness. To assess inter-session reliability of the Lumify system, participants were measured twice within 1 week. Comparing RF architecture measurements of both devices resulted in intra-class correlations (ICCs) ranging from 0.46–0.82 and standardized mean difference (SMDs) ranging from −0.45–0.05. For VL, ICCs ranged from 0.60–0.89 and SMDs ranged from −0.11–0.13. ICCs and SMDs for the GM ranged from 0.82–0.86 and −0.07–0.07. Calculating inter-session reliability for RF resulted in ICCs ranging from 0.44–0.76 and SMDs ranging from −0.38–0.15. For VL, ICCs and SMDs ranged from 0.57–0.75 and −0.13–0.02. ICCs for GM ranged from 0.75–0.92 and SMDs ranged from −0.15–0.16. The highest agreement (ICC ≥ 0.82) and reliability (ICC ≥ 0.75) across all muscles was demonstrated for the measurement of muscle thickness. The Lumify system was comparable to a high-end device and reliable for GM measurements. Nevertheless, agreement and reliability were lower for the RF and VL. Thus, when interpreting longitudinal observed changes in muscle architecture assessed with the Lumify system, measurement errors should be considered. Future investigations should consider including different participant populations, comparing the reliability of different portable systems for muscle architecture assessment as well as including different muscle groups.