This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/.
Loturco, I, Kobal, R, Kitamura, K, Fernandes, V, Moura, N, Siqueira, F, Cal Abad, CC, and Pereira, LA. Predictive factors of elite sprint performance: influences of muscle mechanical properties and ...functional parameters. J Strength Cond Res 33(4): 974-986, 2019-Sprint performance relies on many different mechanical and physiological factors. The purpose of this study was to identify, among a variety of strength-power exercises and tensiomyography (TMG) parameters, the best predictors of maximum running speed in elite sprinters and jumpers. To test these relationships, 19 power track and field athletes, 4 long jumpers, and 15 sprinters (men: 12; 22.3 ± 2.4 years; 75.5 ± 8.3 kg; 176.5 ± 5.6 cm; women: 7; 23.8 ± 4.2 years; 56.9 ± 5.4 kg; 167.4 ± 5.8 cm) were assessed using different intensities of TMG-derived velocity of contraction (Vc), squat and countermovement jumps, drop jump at 45 and 75 cm; and a 60-meter sprint time. In addition, the mean propulsive power (MPP) and peak power (PP) outputs were collected in the jump squat (JS) and half-squat (HS) exercises. Based on the calculations of the Vc at 40 mA, the athletes were divided (by median split analysis) into 2 groups: higher and lower Vc 40 mA groups. The magnitude-based inference method was used to compare the differences between groups. The correlations between mechanical and functional measures were determined using the Pearson's test. A multiple regression analysis was performed to predict sprint performance, using the Vc at 40 mA, jump heights, and JS and HS power outputs as independent variables. The higher Vc 40 mA group demonstrated likely better performances than the lower Vc 40 mA group in all tested variables. Large to nearly perfect significant correlations were found between sprint time, jump heights, and power outputs in both JS and HS exercises. Notably, the Vc 40 mA associated with the vertical jump height and MPP in JS explained >70% of the shared variance in sprint times. In conclusion, it was found that faster athletes performed better in strength-power tests, in both loaded and unloaded conditions, as confirmed by the strong correlations observed between speed and power measures. Lastly, the Vc also showed a marked selective influence on sprint and power capacities. These findings reinforce the notion that maximum running speed is a very complex physical capacity, which should be assessed and trained using several methods and training strategies.
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. ...Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.
Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on ...NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r
> 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.
This study investigated the relationship between punching impact and selected strength and power variables in 15 amateur boxers from the Brazilian National Team (9 men and 6 women). Punching impact ...was assessed in the following conditions: 3 jabs starting from the standardized position, 3 crosses starting from the standardized position, 3 jabs starting from a self-selected position, and 3 crosses starting from a self-selected position. For punching tests, a force platform (1.02 × 0.76 m) covered by a body shield was mounted on the wall at a height of 1 m, perpendicular to the floor. The selected strength and power variables were vertical jump height (in squat jump and countermovement jump), mean propulsive power in the jump squat, bench press (BP), and bench throw, maximum isometric force in squat and BP, and rate of force development in the squat and BP. Sex and position main effects were observed, with higher impact for males compared with females (p ≤ 0.05) and the self-selected distance resulting in higher impact in the jab technique compared with the fixed distance (p ≤ 0.05). Finally, the correlations between strength/power variables and punching impact indices ranged between 0.67 and 0.85. Because of the strong associations between punching impact and strength/power variables (e.g., lower limb muscle power), this study provides important information for coaches to specifically design better training strategies to improve punching impact.
The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the ...technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging.
The aim of this study was to compare performance in the Yo-Yo IR1, 20-meter sprint, COD test, loaded and unloaded lower-limb muscle power tests (squat jump SJ, countermovement jump CMJ and jump squat ...JS tests), as well as resting and exercise heart rate variability parameters in high-level senior professional and under-20 (U-20) futsal players.
All the players (18 senior and 15 U-20 male players) performed the Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IR1), 20-m sprint, COD test, loaded and unloaded lower-limb power tests (SJ, CMJ and JS tests), as well as resting and post-exercise log-transformed root-mean-square difference of successive normal RR intervals (lnRMSSD) recording. The t-test for independent samples and magnitude-based inference were used to compare the groups.
Seniors were likely to very likely superior than U-20 in the Yo-Yo IR1 (1506.7±287.1 and 1264.0±397.9 m, P<0.05), and resting (3.43±0.32 and 3.21±0.37 ms) and post-exercise lnRMSSD (2.95±0.39 and 2.48±0.59 ms, P<0.05). Conversely, U-20 players performed very likely to almost certainly better than seniors in the relative mean propulsive power (10.39±1.60 and 9.05±1.57 W/kg, P<0.05), 20-m sprint time (2.92±0.10 and 3.05±0.10 s, P<0.05) and COD (5.50±0.15 and 5.71±0.22 s, P<0.05).
Findings from this cross-sectional study indicate that long-term exposure to futsal may lead to improvement in the aerobic fitness and cardiac autonomic regulation, while impairing the muscle power and speed performance of players. Future longitudinal studies are necessary to confirm the occurrence of such concurrent training adaptations.
PDF
