Hamstring Strain Injury Risk Factors in Australian Football Change over the Course of the Season.

BACKGROUND/AIM: This study aimed to determine which factors were most predictive of hamstring strain injury (HSI) during different stages of the competition in professional Australian Football. METHODS: Across two competitive seasons, eccentric knee flexor strength and biceps femoris long head architecture of 311 Australian Football players (455 player seasons) were assessed at the start and end of preseason and in the middle of the competitive season. Details of any prospective HSI were collated by medical staff of participating teams. Multiple logistic regression models were built to identify important risk factors for HSI at the different time points across the season. RESULTS: There were 16, 33, and 21 new HSIs reported in preseason, early in-season, and late in-season, respectively, across two competitive seasons. Multivariate logistic regression and recursive feature selection revealed that risk factors were different for preseason, early in-season, and late in-season HSIs. A combination of previous HSI, age, height, and muscle thickness were most associated with preseason injuries (median area under the curve [AUC], 0.83). Pennation angle and fascicle length had the strongest association with early in-season injuries (median AUC, 0.86). None of the input variables were associated with late in-season injuries (median AUC, 0.46). The identification of early in-season HSI and late in-season HSI was not improved by the magnitude of change of data across preseason (median AUC, 0.67). CONCLUSIONS: Risk factors associated with prospective HSI were different across the season in Australian Rules Football, with nonmodifiable factors (previous HSI, age, and height) mostly associated with preseason injuries. Early in-season HSI were associated with modifiable factors, notably biceps femoris long head architectural measures. The prediction of in-season HSI was not improved by assessing the magnitude of change in data across preseason.

CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance.