Idrettssenteret (Norway)

Little is known about the influence of kinetic chain strength and power on shoulder problems in handball players or the impact of participation-related shoulder load (PSL) during a season. Suboptimal activity of the kinetic chain during throwing might make the shoulder more vulnerable. The purpose of this study was to assess 1) the association between pre-season measurements and shoulder problems among handball players and 2) whether pre-season strength and power influence the association between PSL and severity of shoulder problems. Prospective observational cohort study. Pre-season measurements were done using the Isometric mid-thigh pull for lower body strength, a seated test for trunk rotation power (TRP) and isometric testing of external (ER) and internal rotation shoulder strength on 42 male players. Shoulder problems (prevalence, substantial prevalence, and severity score) were documented weekly through a season (31 weeks) with The Oslo Sports Trauma Research Center Overuse Questionnaire (OSTRC-O2) and PSL with the modified Borg rate of perceived exertion scale. Spearman´s correlation coefficient was applied to examine the association within the first aim, while a mixed model ANOVA was conducted to analyze the second aim. A weak and negative correlation (rs=-0.34) was found between pre-season ER strength and the individual prevalence of shoulder problems (p=0.029). A main effect was found between PSL and the OSTRC-O2 severity score (p=<0.001) with higher severity scores observed with increased PSL. A significant interaction was found between PSL and pre-season TRP regarding their influence on the OSTRC-02 severity score (p=0.017). With higher PSL, a higher severity score was observed among players with pre-season TRP more than one standard deviation below the group's mean. The results indicate that low TRP may make the throwing shoulder more vulnerable to an increase in load. Looking beyond shoulder strength and load may therefore be important. 3.

Abstract Study question Can deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure? Summary answer Deep learning can be used as a tool to assist and organize the contents of an ICSI procedure. What is known already Sperm tracking has been a topic of research and practice for many years, especially in the context of computer-aided sperm analysis (CASA). Recent studies have proposed using deep learning algorithms to track spermatozoa for spermatozoon selection in human and animal samples. One critical part of performing ICSI involves the selection of the “best” spermatozoon for injection, but other parts of the procedure may also be of importance. However, as far as we know, tracking using deep learning has not been applied to the ICSI procedure, where detecting instruments and the oocyte could also be helpful in post-analysis and training. Study design, size, duration The study was performed using three anonymized videos of the ICSI procedure. The frames of the videos were manually annotated by data scientists and verified by an embryologist. The annotations were bounding boxes around specific parts of the ICSI procedure, including sperm, pipettes, and the oocyte. We trained a YOLOv5 model on the collected data, where two videos were used for training and one video for validation. Participants/materials, setting, methods The videos of the ICSI procedure were captured at 200x magnification with a DeltaPix camera at Fertilitetssenteret in Oslo, Norway. ICSI was performed using a Nikon ECLIPSE TE2000-S microscope connected with Eppendorf TransferMan 4m micromanipulators. The spermatozoa were immobilised in 5 µl Polyvinylpyrrolidone (PVP; CooperSurgical). The videos had a resolution of 1920x1080 and were resized to 640x640 before being processed by the YOLOv5 model. The data will be made public in a later study. Main results and the role of chance Mean average precision (mAP) with the threshold of 0.5 (mAP@.5) is the main quantitative parameter measured in the YOLOv5 model. All the experiments were performed using three-fold cross-validation, where we present the average metrics calculated over the three folds. Overall, the method showed an average mAP@.5 of 0.50 across all predicted classes, which means that the method can track the different components with good accuracy. Looking closer at the individual classes, we see that instruments like the holding pipette and ICSI pipette are detected with high accuracy with a mAP@.5 of 0.87 and 0.94, respectively. The oocyte is also easily tracked with a mAP@.5 of 0.92. The first polar body is well detected with a mAP@.5 of 0.65. The model has issues detecting and tracking individual sperm (both outside and within the pipette), where the method achieved a mAP@.5 of 0.46 for tracking sperm outside the pipette and 0.03 for the sperm inside the pipette. The low score of detecting the sperm in the pipette can be explained by the often unclear visibility of the sperm through the pipette and the low number of training samples. Limitations, reasons for caution The limited sample size makes the generalizability of the method difficult to determine. A more extensive evaluation is necessary. Moreover, as the currency study focuses on tracking, patient information and clinical outcome were not included in the analysis. Wider implications of the findings Deep learning has the potential to aid embryologists to perform successful ICSI through tracking and detection of spermatozoa, pipettes, and the oocyte. This could potentially lead to better internal quality control and teaching possibilities, and hopefully better results. Trial registration number not applicable

Abstract Study question Can tracking cell division and predicting human embryo cleavage stages be automated in time-lapse videos (TLV) using AI object detection methods? Summary answer We developed software predicting blastomere count and tracking cell cleavages up until 4-5 stage. The software employs object detection technique called YOLOv5 to detect cells. What is known already Embryo morphology plays an important part in determining viability. Parameters such as number of cells present following fertilization, abnormal cell division (reverse/direct) and evaluating cleavage stages have correlation with pregnancy rates. However, continuous manual evaluation can be time-consuming, and automation will assist in embryo viability assessment. YOLOv5 has proven to accurately detect objects in videos. YOLOv5 uses mean average precision (mAP) as a metric to quantify the portions of frames in videos having the correct count of the objects. Study design, size, duration We have developed a software that uses YOLOv5 to detect cells present in frames of TLV, then marks each cell boundary with different colored circular overlays using OpenCV. We trained YOLOv5 to detect objects: cell, morula and blastocyst using 150 images of different cell-stages, morula, blastocyst. For object cell mAP was 0.65. Annotated location of objects in images and YOLOv5 predictions were reviewed by embryologists. We evaluated the software on TLV from 11 patients. Participants/materials, setting, methods After YOLOv5 detects cells in frames of TLV, our software computes cell count and assigns each cell a different color which is maintained until cell division into daughter cells. Later, daughter cells were also assigned different colors. If the frame has a preceding frame, software calculates detected cells' proximity with each cell in the preceding frame and copies color scheme provided proximity is within some threshold. The software provides TLV with colored overlays as output. Main results and the role of chance In starting frames of TLV with single cell, software accurately detected 1-cell (high precision=0.99, high recall=0.83, high F1-score=0.90). We observed some misclassification between 1-cell and morula. The reason could be that compacted morula looks like 1-cell. Best performance is observed for 2-cells (high precision=0.91, high recall=0.98, high F1-score=0.95). 4-cells were sometimes misclassified with 3 or 5-cells (high precision=0.88, low recall=0.59, high F1-score=0.71). One reason for the misclassification can be that overlapping between cells increases with number of cells. 3-cell and 5-cell are confused with other stages, still cleavage stage detection is better than random: 3-cell (average precision=0.43, high recall=0.83, average F1-score=0.49), 5-cell (average precision=0.44, average recall=0.40, average F1-score=0.40). For cell-stages&amp;gt;5, YOLOv5 detects less cells than actual count and software predicts cleavage later than actual by 9-10 frames on average. The proximity threshold used was 0.10 for cell-count&amp;lt;4 and 0.05 for count&amp;gt;4. In 5 TLV, overlay color for cells changes abruptly between frames, possibly because once YOLOv5 detected a stage, in consecutive frames less cell-number was recorded, and then again reported correct count. Sometimes, software selected the wrong parent for daughter cells (incorrect colored overlay). 2 TLV had direct and reverse cleavages and software could detect these two patterns. Limitations, reasons for caution Overall, our software can precisely detect cells, cell divisions and cleavage stages up to 4-cell stages. We hypothesize that training YOLOv5 on a bigger dataset and including several focal plane information will enable our software to detect overlapping cells and cleavage stages &amp;gt; =5. Wider implications of the findings Object detection proved to be pragmatic for ART and tracking cell division using our software will reduce time consumed in manual annotations, easier prediction of abnormal cleavages and more objective assessments. Qualitative evaluation by embryologists resulted in the overall verdict that this is useful and promising for further development. Trial registration number not applicable

Abstract Assisted reproductive technology (ART) refers to treatments of infertility which include the handling of eggs, sperm and embryos. The success of ART procedures depends on several factors, including the quality of the embryo transferred to the woman. The assessment of embryos is mostly based on the morphokinetic parameters of their development, which include the number of cells at a given time point indicating the cell stage and the duration of each cell stage. In many clinics, time-lapse imaging systems are used for continuous visual inspection of the embryo development. However, the analysis of time-lapse data still requires the evaluation, by embryologists, of the morphokinetic parameters and cleavage patterns, making the assessment subjective. Recently the application of object detection in the field of medical imaging enabled the accurate detection of lesion or object of interest. Motivated by this research direction, we proposed a methodology to detect and track cells present inside embryos in time-lapse image series. The methodology employed an object detection technique called YOLO v5 and annotated the start of observed cell stages based on the cell count. Our approach could identify cell division to detect cell cleavage or start of next cell stage accurately up to the 5-cell stage. The methodology also highlighted instances of embryos development with abnormal cell cleavage patterns. On an average the methodology used 8 s to annotate a video frame (20 frames per second), which will not pose any delay for the embryologists while assessing embryo quality. The results were validated by embryologists, and they considered the methodology as a useful tool for their clinical practice.

Tidligere studier har antydet at idrettstrenere ikke får sitt behov for læring og kompetanseutvikling tilfredsstilt av formelle utdanningssystemer. Med bakgrunn i dette er målet med denne studien å undersøke faktiske og foretrukne læringskilder blant norske trenere. I et strukturert spørreskjema vurderte 510 trenere 19 ulike typer læringskilder på en 7-delt Likert-skala, med utgangspunkt i læringskildens betydning henholdsvis for deres kompetanseutvikling hittil i karrieren, og for deres videre utvikling. Kilder knyttet til kommunikasjon med kolleger og refleksjon rundt egen erfaring ble høyest vurdert. Når læringskildene ble kategorisert som «formelle», «uformelle» eller «selvstyrte», fremsto selvstyrte læringskilder som signifikant viktigere enn formelle og uformelle læringskilder, både for trenernes utvikling hittil i karrieren og for videre utvikling (p < 0,01). Formelle læringskilder ble vurdert som signifikant mindre viktig for videre utvikling, sammenlignet med for utvikling hittil i trenerkarrieren (p < 0,01). Vurderingen av formelle og selvstyrte læringskilders betydning for utvikling hittil i karrieren og for videre utvikling var dessuten signifikant avhengig av nivået trenerne praktiserte på (p < 0,05). Funnene diskuteres i lys av tidligere forskning, og forsøkes sett i et helhetlig perspektiv både når det gjelder trenernes egenutvikling og utdanningsinstitusjonenes rolle i denne sammenheng. Retninger for videre forskning foreslås med utgangspunkt i dette.

It should be pretty obvious; movement is good, sitting still is bad. However, physical activity rates continue to fall across the world.1 As sports and exercise medicine practitioners, we could be seen as frontline soldiers in the fight against inactivity. As such, we need to be well armed with knowledge about the consequences of inactivity, about what can be done to combat it and about who we should be targeting. These themes are all covered in this issue which shines the spotlight on an increasingly researched topic in Norway—the relationship between physical activity and health. We start with a study documenting the declining activity levels of British school children as they age from 10 to 14 years ( see page 730 ). We hope their increased sedentary time is not spent in front of a computer or watching TV, because in their huge meta-analysis, Zhai et al show that these behaviours are …

BackgroundThe development of strategies to prevent illnesses before and during Olympic Games provides a basis for improved health and Olympic results.Objective(1) To document the efficacy of a prevention programme on illness in a national Olympic team before and during the 2010 Vancouver Olympic Winter Games (OWG), (2) to compare the illness incidence in the Norwegian team with Norwegian incidence data during the Turin 2006 OWG and (3) to compare the illness incidence in the Norwegian team with illness rates of other nations in the Vancouver OWG.MethodsInformation on prevention measures of illnesses in the Norwegian Olympic team was based on interviews with the Chief Medical Officer (CMO) and the Chief Nutrition and Sport Psychology Officers, and on a review of CMO reports before and after the 2010 OWG. The prevalence data on illness were obtained from the daily reports on injuries and illness to the International Olympic Committee.ResultsThe illness rate was 5.1% (five of 99 athletes) compared with 17.3% (13 out of 75 athletes) in Turin (p=0.008). A total of four athletes missed one competition during the Vancouver Games owing to illness, compared with eight in Turin. The average illness rate for all nations in the Vancouver OWG was 7.2%.ConclusionsAlthough no definite cause-and-effect link between the implementation of preventive measures and the prevalence of illness in the 2010 OWG could be established, the reduced illness rate compared with the 2006 OWG, and the low prevalence of illnesses compared with other nations in the Vancouver OWG suggest that the preparations were effective.