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Paul Ritsche,1 Ralf Roth,1 Thomas Bernhard,2 Lukas Nebiker,1 Eric Lichtenstein,1 Martino Franchi,3 Jörg Spörri,4,5 Oliver Faude1 1Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland; 2FC Basel 1893, Basel, Switzerland; 3Human Neuromuscular Physiology Lab, Department of Biomedical Sciences, University of Padua, Padova, Italy; 4Sports Medical Research Group, Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; 5University Centre for Prevention and Sports Medicine, Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, SwitzerlandCorrespondence: Paul Ritsche, University of Basel, Department of Sport, Exercise and Health, Grosse Allee 6, Basel, 4052, Switzerland, Email Paul.ritsche@unibas.chPurpose: Adolescent soccer players experience distinct physiological changes due to chronological and biological maturation, impacting their soccer performance. Here, we explored age-related variations and associations between quadriceps geometry and strength in male national-level adolescent soccer players.Patients and Methods: We used ultrasonography to examine the regional architecture and morphology of the rectus femoris (RF) and vastus lateralis (VL) muscles, and we assessed knee extension strength by isometric and isokinetic dynamometry. Players were categorized into four age groups: under (U) 15 (n=18, age=13.7± 0.5 years), U16 (n=15, age=14.7± 0.5), U17 (n=19, age=15.7± 0.5), U18 (n=18, age=16.7± 0.5) and U21 (n=25, age=18.5± 0.5).Results: The absolute and relative strengths were higher in the U16 compared to U15 by 12– 15% and 6– 8%, 11– 12% and 6– 7% in the U17 compared to U16, 5– 7% and − 1– 2% in the U18 compared to U17 and 0– 15% and − 1– 11% in the U21 compared to U18 age groups, respectively. VL architecture did not change relevantly between the age groups. The muscle anatomical cross-sectional area (ACSA) of the VL and RF differed non-uniformly and muscle region-specific by 10– 36%, with highest values in the U21 age group. Moderate correlations between the VL architecture and knee extension strength in both legs were observed only in the U16 age group. The quadriceps ACSA showed age-specific correlations with knee extension strength.Conclusion: Our findings highlight non-uniform differences in quadriceps muscle morphology and absolute and relative strength among male national-level adolescent soccer players in different age groups. The correlations observed between muscle morphology or architecture and strength were muscle, muscle region, leg and age dependent.Keywords: strength, youth, muscle architecture, vastus lateralis, rectus femoris

High end ultrasonography devices lack in portability and are expensive. We investigated the agreement and reliability of a handheld and portable ultrasound system for human lower limb muscle architecture measurements. We captured ultrasound 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). 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. Measurement of muscle thickness demonstrated the highest agreement (ICC ≥0.82) and reliability (ICC ≥0.75) across all muscles. The Lumify system was comparable to a high-end device and reliable for GM measurements. However, agreement and reliability were lower for the RF and VL. Of all evaluated architectural parameters, muscle thickness exhibited highest agreement and reliability.

Abstract Open-access scripts to perform muscle anatomical cross-sectional area (ACSA) evaluation in ultrasound images are currently unavailable. This study presents a novel semi-automatic ImageJ script (named “ACSAuto”) for quantifying the ACSA of lower limb muscles. We compared manual ACSA measurements from 180 ultrasound scans of vastus lateralis (VL) and rectus femoris (RF) muscles to measurements assessed by the ACSAuto script. We investigated inter- and intra-investigator reliability of the script. Consecutive-pairwise intra-class correlations (ICC) and standard error of measurement (SEM) with 95% compatibility interval were calculated. Bland–Altman analyses were employed to test the agreement between measurements. Comparing manual and ACSAuto measurements, ICCs and SEMs ranged from 0.96 to 0.999 and 0.12 to 0.96 cm2 (1.2–5.9%) and mean bias was smaller than 0.5 cm2 (4.3%). Inter-investigator comparison revealed ICCs, SEMs and mean bias ranging from 0.85 to 0.999, 0.07 to 1.16 cm2 (0.9–7.6%) and − 0.16 to 0.66 cm2 (− 0.6 to 3.2%). Intra-investigator comparison revealed ICCs, SEMs and mean bias between 0.883–0.998, 0.07–0.93 cm2 (1.1–7.6%) and − 0.80 to 0.15 cm2 (− 3.4 to 1.8%). Image quality needs to be high for efficient and accurate ACSAuto analyses. Taken together, the ACSAuto script represents a reliable tool to measure RF and VL ACSA, is comparable to manual analysis and can reduce time needed to evaluate ultrasound images.

Baleen whales (Mysticeti) are known to use low frequencies (LF; 200 Hz and below) and infrasound ( 20 kHz), reach low frequencies. Researchers have tried to understand the evolution of LF and infrasonic hearing in mysticetes by linking the shape of the inner ear cochlea or individual cochlear measurements to known hearing frequencies and making inferences to extinct species. Using landmark-based shape analysis of complete cochlear coiling, we show that cochlear coiling shape correlates with LF and high-frequency (HF; > 10 kHz) hearing limits in cetaceans. Very LF ( ≤ 50 Hz) and infrasonic hearing are associated with, for example, a protruding second turn, a descending apex, and a high number of turns. Correlations between cochlear and cranial variables and cochlear and cranial shape indicate that low LF hearing limits are furthermore connected to longer cochleae and relatively larger cranial widths. Very LF hearing in Mysticeti appeared in the middle Miocene, and mysticete infrasonic hearing had evolved by the late Miocene. Complete cochlear coiling is suitable for estimating hearing limits in cetaceans, closely approximated by cochlear length times number of cochlear turns.

The aim of this study was to determine if 3-wks of short-term, high-intensity exercise training (HIT) alters growth hormone (GH) release. Nineteen recreationally active males (Mean ± SD) (age = 24.9 ± 3.9 yrs, BF% = 20.1 ± 7.7) participated in this study. Each subject completed a 2-hr resting profile and a 2-hr acute sprint (AS) profile that consisted of one maximal 30-sec Wingate sprint on a cycle ergometer after 30 min of rest. Blood samples were taken every 15 min [Q15] during rest and more frequently [Q1-Q10] immediately following the sprint. Short-term, HIT consisted of 4-6 repetitions of 30-sec maximal sprints relative to body mass, 3 times•wk-1 with an additional AS profile at the end of each week of training for 3 wks. Peak power (PP) and fatigue index (FI) significantly increased while mean power (MP), minimum power (MinP), time to peak power (TTPP), and total work per sprint (TW) were unchanged after 3 wks of HIT. Total body mass significantly increased and was confirmed by a significant increase in lean mass of the lower extremities. Growth hormone area under the curve (AUC) and peak GH were significantly decreased after the first week of HIT despite no change in time to reach peak GH. One week of HIT significantly decreased GH release, with a simultaneous significant increase in anaerobic power and lean body mass of the lower extremities. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify the relationship between the acute exerciseinduced growth hormone (GH) response, gender, fitness, age, and body composition with non-exercise stimulated (NES) 24-hr integrated GH concentration (IGHC). Twenty-nine subjects (16 males, 13 females) completed a 24-hr control session without exercise (NES) on one occasion and a 30-min exercise session on a separate occasion. The GH release was analyzed from 10-min intervals using trapezoidal integration during both sessions. Multiple regression analysis in males revealed that significant variability in NES 24-hr IGHC could be adequately explained by subject age and BMI together (P<0.05), but could not be explained by exercise-induced peak GH or 30-min of exercise-induced IGHC on an unrelated day (P=0.750). However, significant variability in NES 24-hr IGHC in females could be uniquely associated with subject age (P<0.05) as well as peak GH and 30-min of exercise-induced IGHC (P<0.05). The findings indicate that in the females only the constant load exercise-induced growth hormone response can adequately predict total 24-hr growth hormone output on a separate day without an exercise stimulus. The data further exemplify gender disparities in exercise-induced growth hormone release and the importance of exercise for females in regards to enhancing total 24-hr growth hormone concentrations. [ABSTRACT FROM AUTHOR]

The article emphasizes the importance of leadership among exercise physiologists. In order for credibility to exist within the profession, certain key elements should be included. The main element is leadership. In the absence of a leader, followers have no common visions for their goals. The American Society of Exercise Physiologists was founded in 1997 and there is some sense that it is at a standstill regarding professionalism. Several important elements of leadership are outlined to help with the understanding of the importance of professional development of exercise physiology.

• Natural regeneration density was highest in forests and lowest in shrublands. • Regeneration failure occurred across land cover types, strongest in shrublands. • Cork oak regeneration increased with slope gradient across land cover types. • Tree regeneration in montados seems to be limited by lack of shrub cover. • Topography and soils affect competition dynamics of tree recruits and shrubs. Agricultural intensification and abandonment of traditional uses have led to the degradation of Mediterranean oak landscapes, most apparent in a severe lack of tree regeneration. In the Alentejo region of Southwestern Portugal the landscape mosaic is composed of three prevalent land cover types: cork oak forests, open cork oak woodlands (montados) and shrublands dominated by Cistus ladanifer. The objective of this study is to understand how natural tree regeneration differs among these three land cover types and how stand characteristics and site conditions affect regeneration at each land cover type. We collected data on stand characteristics (adult tree layer), site conditions (topography, soil conditions, understory vegetation and light conditions) and tree regeneration (species composition, density, size structure), and analysed differences among the three land cover types. An information-theoretic approach and model averaging were applied to a set of generalized linear mixed models (GLMMs) to identify stand characteristics and site conditions that best explain regeneration occurrence of cork oak (Quercus suber) and strawberry tree (Arbutus unedo) at each land cover type. Our results show that regeneration failure was widespread at all land cover types, being strongest in shrublands. Regeneration density was higher in forests than in montados, and marginal in shrublands. Differences in regeneration patterns among land cover types seem to be related to differential processes of seed availability, seed dispersal and the availability of safe sites for recruit establishment, which are affected by vegetation cover and (past) land use practices. Cork oak regeneration was more likely to occur at steeper slopes in both forests and montados, with lower soil fertility and higher available water storage capacity in forests, and with higher proportion of ground cover in montados. In shrublands, cork oak regeneration was more likely to occur with higher soil fertility and rock content, and with less solar radiation. Regeneration of strawberry tree in forests was more likely to occur with lower available water storage capacity, and with the presence of adult strawberry trees, while in shrublands it was more likely to occur at flatter slopes. Overall, our results indicate that differences in cork oak and strawberry tree regeneration within land cover types seem to be a result of indirect effects of stand and site factors on competition and facilitation between tree recruits and coexisting plants. This study provides recommendations that can support sustainable management and restoration efforts in Mediterranean oak landscapes. [ABSTRACT FROM AUTHOR]

The Peak Design Ltd hyperspectral radiometer (HSR1) was tested at the Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site in Lamont, Oklahoma, for 2 months from May to July 2022. The HSR1 is a prototype instrument that measures total (Ftotal) and diffuse (Fdiffuse) spectral irradiance from 360 to 1100 nm with a spectral resolution of 3 nm. The HSR1 spectral irradiance measurements are compared to nearby collocated spectral radiometers, including two multifilter rotating shadowband radiometers (MFRSRs) and the Shortwave Array Spectroradiometer–Hemispheric (SASHe) radiometer. The Ftotal at 500 nm for the HSR1 compared to the MFRSRs has a mean (relative) difference of 0.01 W m−2 nm−1 (1 %–2 %). The HSR1 mean Fdiffuse at 500 nm is smaller than the MFRSRs' by 0.03–0.04 (10 %) W m−2 nm−1. The HSR1 clear-sky aerosol optical depth (AOD) is also retrieved by considering Langley regressions and compared to collocated instruments such as the Cimel sunphotometer (CSPHOT), MFRSRs, and SASHe. The mean HSR1 AOD at 500 nm is larger than the CSPHOT's by 0.010 (8 %) and larger than the MFRSRs' by 0.007–0.017 (6 %–18 %). In general, good agreement between the HSR1 and other instruments is found in terms of the Ftotal, Fdiffuse, and AODs at 500 nm. The HSR1 quantities are also compared at other wavelengths to the collocated instruments. The comparisons are within ∼ 10 % for the Ftotal and Fdiffuse, except for 940 nm, where there is relatively larger disagreement. The AOD comparisons are within ∼ 10 % at 415 and 440 nm; however, a relatively larger disagreement in the AOD comparison is found for higher wavelengths.

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.

Objective: Deep 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., Methods: Quadriceps 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 participants (n = 153, age 38.2, range 13-78) that the DeepACSA was developed from. All models were tested on unseen external validation images (n = 100) from ACL-injured participants. Model predicted CSA results were compared to manual segmentation results., Results: All models showed good comparability (ICC > 0.81, < 14.1% standard error of measurement, mean differences of <1.56 cm 2 ) to manual segmentation. Removal of the erroneous predictions resulted in excellent comparability (ICC > 0.94, < 7.40% standard error of measurement, mean differences of <0.57 cm 2 ). Erroneous predictions were 17% for combined VL, 11% for combined RF, and 20% for ACL-only VM models., Conclusion: The new CNN models provided can be used in ACL-injured populations to measure CSA of VL, RF, and VM muscles automatically. The models yield high comparability to manual segmentation results and reduce the burden of manual segmentation., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: B-mode ultrasound can be used to image musculoskeletal tissues, but one major bottleneck is analyses of muscle architectural parameters (i.e., muscle thickness, pennation angle and fascicle length), which are most often performed manually., Methods: In this study we trained two different neural networks (classic U-Net and U-Net with VGG16 pre-trained encoder) to detect muscle fascicles and aponeuroses using a set of labeled musculoskeletal ultrasound images. We determined the best-performing model based on intersection over union and loss metrics. We then compared neural network predictions on an unseen test set with those obtained via manual analysis and two existing semi/automated analysis approaches (simple muscle architecture analysis [SMA] and UltraTrack). DL&#95;Track&#95;US detects the locations of the superficial and deep aponeuroses, as well as multiple fascicle fragments per image., Results: For single images, DL&#95;Track&#95;US yielded results similar to those produced by a non-trainable automated method (SMA; mean difference in fascicle length: 5.1 mm) and human manual analysis (mean difference: -2.4 mm). Between-method differences in pennation angle were within 1.5°, and mean differences in muscle thickness were less than 1 mm. Similarly, for videos, there was overlap between the results produced with UltraTrack and DL&#95;Track&#95;US, with intraclass correlations ranging between 0.19 and 0.88., Conclusion: DL&#95;Track&#95;US is fully automated and open source and can estimate fascicle length, pennation angle and muscle thickness from single images or videos, as well as from multiple superficial muscles. We also provide a user interface and all necessary code and training data for custom model development., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2023 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Purpose: Muscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the human rectus femoris (RF), vastus lateralis (VL), gastrocnemius medialis (GM) and lateralis (GL) muscles., Methods: We trained three muscle-specific convolutional neural networks (CNN) using 1772 ultrasound images from 153 participants (age = 38.2 yr, range = 13-78). Images were acquired in 10% increments from 30% to 70% of femur length for RF and VL and at 30% and 50% of muscle length for GM and GL. During training, CNN performance was evaluated using intersection-over-union scores. We compared the performance of DeepACSA to manual analysis and a semiautomated algorithm using an unseen test set., Results: Comparing DeepACSA analysis of the RF to manual analysis with erroneous predictions removed (3.3%) resulted in intraclass correlation (ICC) of 0.989 (95% confidence interval = 0.983-0.992), mean difference of 0.20 cm 2 (0.10-0.30), and SEM of 0.33 cm 2 (0.26-0.41). For the VL, ICC was 0.97 (0.96-0.968), mean difference was 0.85 cm 2 (-0.4 to 1.31), and SEM was 0.92 cm 2 (0.73-1.09) after removal of erroneous predictions (7.7%). After removal of erroneous predictions (12.3%), GM/GL muscles demonstrated an ICC of 0.98 (0.96-0.99), a mean difference of 0.43 cm 2 (0.21-0.65), and an SEM of 0.41 cm 2 (0.29-0.51). Analysis duration was 4.0 ± 0.43 s (mean ± SD) for analysis of one image in our test set using DeepACSA., Conclusions: DeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable with manual segmentation. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high-quality image for accurate prediction., (Copyright © 2022 by the American College of Sports Medicine.)

High end ultrasonography devices lack in portability and are expensive. We investigated the agreement and reliability of a handheld and portable ultrasound system for human lower limb muscle architecture measurements. We captured ultrasound 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). 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. Measurement of muscle thickness demonstrated the highest agreement (ICC ≥0.82) and reliability (ICC ≥0.75) across all muscles. The Lumify system was comparable to a high-end device and reliable for GM measurements. However, agreement and reliability were lower for the RF and VL. Of all evaluated architectural parameters, muscle thickness exhibited highest agreement and reliability., Competing Interests: PR and OF were under contractual agreement with Philips Healthcare. However, Philips Healthcare was neither involved in the design and conduct of the study, nor the analysis. Further, Philips Healthcare did not have any decision making or approval in the submission of this manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ritsche, Schmid, Franchi and Faude.)

Purpose: Hamstring muscle architecture may be associated with sprint performance and the risk of sustaining a muscle injury, both of which increase during puberty. In this study, we investigated the m. biceps femoris long head (BFlh) cross-sectional area (ACSA), fascicle length (FL) and pennation angle (PA), and sprint performance as well as their relationship in under 13 to 15 youth soccer players., Methods: We measured 85 players in under-13 (n = 29, age = 12.5 [0.1] y, height = 155.3 [6.2] cm, weight = 43.9 [7.6] kg), under-14 (n = 25, age = 13.5 [0.3] y, height = 160.6 [7.7] cm, weight = 47.0 [6.8] kg), and under-15 (n = 31, age = 14.4 [0.3] y, height = 170.0 [7.7] cm, weight = 58.1 [8.8] kg) teams. We used ultrasound to measure BFlh ACSA, FL and PA, and sprint tests to assess 10- and 30-m sprint time, maximal velocity  (vmax), and maximal acceleration (αmax). We calculated Pearson r to assess the relationship between sprint ability and architectural parameters., Results: All muscle architectural parameters increased from the under-13 to the under-15 age group (BFlh ACSA = 37%, BFlh FL = 11%, BFlh PA = 8%). All sprint performance parameters improved from the under-13 to under-15 age categories (30-m time = 7%, 10-m time = 4%, vmax = 9%, αmax = 7%). The BFlh ACSA was correlated with 30-m sprint time (r = -.61 (95% compatibility interval [CI] [-.73, -.45]) and vmax (r = .61, 95% CI [.45, .72]). A combination of BFlh ACSA and age best predicted 30-m time (R² = .47 [.33, .62]) and 10-m time (R² = .23 [.08, .38])., Conclusions: Muscle architectural as well as sprint performance parameters increase from the under-13 to under-15 age groups. Even though we found correlations for all assessed architectural parameters, BFlh ACSA was best related to the assessed sprint parameters.