Benefits and limitations of isokinetic force assessments in swimmer's shoulders: A systematic review.

OBJECTIVE: To indicate the benefits and limitations of the isokinetic test results for the performance of the main shoulder joint movements in swimmers, considering the different competitive levels, swimming techniques, race distances, and sex. METHODS: Search on the PubMed, CENTRAL, Medline, LILACS, and SCOPUS databases for the oldest records up to October 2022. Risk of bias, methodological quality, and level of evidence were evaluated based on the NHLBI checklist. RESULTS: 29 articles met the criteria and were included in this study. The quality analysis classified three as "good" and 26 as "regular", with a KAPPA index of 0.87. The main benefits found involved assessments of the clinical condition of the shoulder joint complex, relationships with performance, and reliability studies. The limitations found point to the participant's positioning in the instrument, use of angular velocity above 180°/s, and sample size. CONCLUSION: The use of the isokinetic dynamometer allows verifying the levels of strength, endurance, balance, and asymmetries among swimmers of different techniques, distances, competitive levels, and sex. Thus, it helps in the analysis and monitoring of the clinical conditions of swimmers' shoulder joints, contributing to the decision-making process of physiotherapists and coaches.

A benchmark for hypothalamus segmentation on T1-weighted MR images.

The hypothalamus is a small brain structure that plays essential roles in sleep regulation, body temperature control, and metabolic homeostasis. Hypothalamic structural abnormalities have been reported in neuropsychiatric disorders, such as schizophrenia, amyotrophic lateral sclerosis, and Alzheimer's disease. Although mag- netic resonance (MR) imaging is the standard examination method for evaluating this region, hypothalamic morphological landmarks are unclear, leading to subjec- tivity and high variability during manual segmentation. Due to these limitations, it is common to find contradicting results in the literature regarding hypothalamic volumetry. To the best of our knowledge, only two automated methods are available in the literature for hypothalamus segmentation, the first of which is our previous method based on U-Net. However, both methods present performance losses when predicting images from different datasets than those used in training. Therefore, this project presents a benchmark consisting of a diverse T1-weighted MR image dataset comprising 1381 subjects from IXI, CC359, OASIS, and MiLI (the latter created specifically for this benchmark). All data were provided using automatically generated hypothalamic masks and a subset containing manually annotated masks. As a baseline, a method for fully automated segmentation of the hypothalamus on T1-weighted MR images with a greater generalization ability is presented. The pro- posed method is a teacher-student-based model with two blocks: segmentation and correction, where the second corrects the imperfections of the first block. After using three datasets for training (MiLI, IXI, and CC359), the prediction performance of the model was measured on two test sets: the first was composed of data from IXI, CC359, and MiLI, achieving a Dice coefficient of 0.83; the second was from OASIS, a dataset not used for training, achieving a Dice coefficient of 0.74. The dataset, the baseline model, and all necessary codes to reproduce the experiments are available at https://github.com/MICLab-Unicamp/HypAST and https://sites.google.com/ view/calgary-campinas-dataset/hypothalamus-benchmarking. In addition, a leaderboard will be maintained with predictions for the test set submitted by anyone working on the same task.