The relationships between golf and health:A scoping review

OBJECTIVE: To assess the relationships between golf and health. DESIGN: Scoping review. DATA SOURCES: Published and unpublished reports of any age or language, identified by searching electronic databases, platforms, reference lists, websites and from consulting experts. REVIEW METHODS: A 3-step search strategy identified relevant published primary and secondary studies as well as grey literature. Identified studies were screened for final inclusion. Data were extracted using a standardised tool, to form (1) a descriptive analysis and (2) a thematic summary. RESULTS AND DISCUSSION: 4944 records were identified with an initial search. 301 studies met criteria for the scoping review. Golf can provide moderate intensity physical activity and is associated with physical health benefits that include improved cardiovascular, respiratory and metabolic profiles, and improved wellness. There is limited evidence related to golf and mental health. The incidence of golfing injury is moderate, with back injuries the most frequent. Accidental head injuries are rare, but can have serious consequences. CONCLUSIONS: Practitioners and policymakers can be encouraged to support more people to play golf, due to associated improved physical health and mental well-being, and a potential contribution to increased life expectancy. Injuries and illnesses associated with golf have been identified, and risk reduction strategies are warranted. Further research priorities include systematic reviews to further explore the cause and effect nature of the relationships described. Research characterising golf's contribution to muscular strengthening, balance and falls prevention as well as further assessing the associations and effects between golf and mental health are also indicated

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Left ventricular speckle tracking-derived cardiac strain and cardiac twist mechanics in athletes: a systematic review and meta-analysis of controlled studies

Background: The athlete’s heart is associated with physiological remodeling as a consequence of repetitive cardiac loading. The effect of exercise training on left ventricular (LV) cardiac strain and twist mechanics are equivocal, and no meta-analysis has been conducted to date. Objective: The objective of this systematic review and meta-analysis was to review the literature pertaining to the effect of different forms of athletic training on cardiac strain and twist mechanics and determine the influence of traditional and contemporary sporting classifications on cardiac strain and twist mechanics. Methods: We searched PubMed/MEDLINE, Web of Science, and ScienceDirect for controlled studies of aged-matched male participants aged 18–45 years that used two-dimensional (2D) speckle tracking with a defined athlete sporting discipline and a control group not engaged in training programs. Data were extracted independently by two reviewers. Random-effects meta-analyses, subgroup analyses, and meta-regressions were conducted. Results: Our review included 13 studies with 945 participants (controls n = 355; athletes n = 590). Meta-analyses showed no athlete–control differences in LV strain or twist mechanics. However, moderator analyses showed greater LV twist in high-static low-dynamic athletes (d = –0.76, 95% confidence interval [CI] –1.32 to –0.20; p < 0.01) than in controls. Peak untwisting velocity (PUV) was greater in high-static low-dynamic athletes (d = –0.43, 95% CI –0.84 to –0.03; p < 0.05) but less than controls in high-static high-dynamic athletes (d = 0.79, 95% CI 0.002–1.58; p = 0.05). Elite endurance athletes had significantly less twist and apical rotation than controls (d = 0.68, 95% CI 0.19–1.16, p < 0.01; d = 0.64, 95% CI 0.27–1.00, p = 0.001, respectively) but no differences in basal rotation. Meta-regressions showed LV mass index was positively associated with global longitudinal (b = 0.01, 95% CI 0.002–0.02; p < 0.05), whereas systolic blood pressure was negatively associated with PUV (b = –0.06, 95% CI –0.13 to –0.001; p = 0.05). Conclusion: Echocardiographic 2D speckle tracking can identify subtle physiological differences in adaptations to cardiac strain and twist mechanics between athletes and healthy controls. Differences in speckle tracking echocardiography-derived parameters can be identified using suitable sporting categorizations

Left and right ventricular longitudinal strain-volume/area relationships in elite athletes.

We propose a novel ultrasound approach with the primary aim of establishing the temporal relationship of structure and function in athletes of varying sporting demographics. 92 male athletes were studied [Group IA, (low static-low dynamic) (n = 20); Group IC, (low static-high dynamic) (n = 25); Group IIIA, (high static-low dynamic) (n = 21); Group IIIC, (high static-high dynamic) (n = 26)]. Conventional echocardiography of both the left ventricles (LV) and right ventricles (RV) was undertaken. An assessment of simultaneous longitudinal strain and LV volume/RV area was provided. Data was presented as derived strain for % end diastolic volume/area. Athletes in group IC and IIIC had larger LV end diastolic volumes compared to athletes in groups IA and IIIA (50 ± 6 and 54 ± 8 ml/(m(2))(1.5) versus 42 ± 7 and 43 ± 2 ml/(m(2))(1.5) respectively). Group IIIC also had significantly larger mean wall thickness (MWT) compared to all groups. Athletes from group IIIC required greater longitudinal strain for any given % volume which correlated to MWT (r = 0.4, p < 0.0001). Findings were similar in the RV with the exception that group IIIC athletes required lower strain for any given % area. There are physiological differences between athletes with the largest LV and RV in athletes from group IIIC. These athletes also have greater resting longitudinal contribution to volume change in the LV which, in part, is related to an increased wall thickness. A lower longitudinal contribution to area change in the RV is also apparent in these athletes

Assessing the Robustness of Image Registration Models Under Domain Shifts with Learnable Input Images

Deep learning models have revolutionized image registration but their accuracy can degrade under unforeseen data variations (domain shifts). It is crucial to assess model robustness under such shifts, often accomplished using simulated domain shifts and expert annotations, e.g., landmarks. This work presents ProactiV-Reg, an annotation-free approach that utilizes a learnable image mapping: it iteratively adjusts a moving image to align with a fixed image under simulated domain shifts. The distances between the perturbed and the optimized images reveal model robustness. We evaluate ProactiV-Reg on three models, showcasing its ability to detect robustness differences, identify dominant perturbations, and provide insights into the model’s input requirements.</p

The Importance of Realistic Training Deformations for Respiratory CT Registration

Deep learning enables fast deformable medical image registration but requires large training datasets, which are currently scarce. To overcome this, synthetic deformations can be generated to create and augment the training data. We propose a method that incorporates prior knowledge of the physiological motion to generate more realistic deformations. Specifically, our method is developed on thoracic computed tomography scans and incorporates respiratory motion. We evaluated the effect of various synthetic deformation methods on deep learning-based registration performance, achieving better performance when trained on realistic deformations, compared to when trained on random deformations. In general, the inclusion of realistic deformations, either real or synthetic, was found to be essential for achieving good registration performance.</p

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Speckle Tracking Echocardiography for the Assessment of the Athlete's Heart: Is It Ready for Daily Practice?

PURPOSE OF REVIEW: To describe the use of speckle tracking echocardiography (STE) in the biventricular assessment of athletes' heart (AH). Can STE aid differential diagnosis during pre-participation cardiac screening (PCS) of athletes? RECENT FINDINGS: Data from recent patient, population and athlete studies suggest potential discriminatory value of STE, alongside standard echocardiographic measurements, in the early detection of clinically relevant systolic dysfunction. STE can also contribute to subsequent prognosis and risk stratification. Despite some heterogeneity in STE data in athletes, left ventricular global longitudinal strain (GLS) and right ventricular longitudinal strain (RV ɛ) indices can add to differential diagnostic protocols in PCS. STE should be used in addition to standard echocardiographic tools and be conducted by an experienced operator with significant knowledge of the AH. Other indices, including left ventricular circumferential strain and twist, may provide insight, but further research in clinical and athletic populations is warranted. This review also raises the potential role for STE measures performed during exercise as well as in serial follow-up as a method to improve diagnostic yield