Eccentric and Isometric Hip Adduction Strength in Male Soccer Players With and Without Adductor-Related Groin Pain An Assessor-Blinded Comparison:An Assessor-Blinded Comparison

BACKGROUND: Adductor-related pain is the most common clinical finding in soccer players with groin pain and can be a long-standing problem affecting physical function and performance. Hip adductor weakness has been suggested to be associated with this clinical entity, although it has never been investigated. PURPOSE: To investigate whether isometric and eccentric hip strength are decreased in soccer players with adductor-related groin pain compared with asymptomatic soccer controls. The hypothesis was that players with adductor-related groin pain would have lower isometric and eccentric hip adduction strength than players without adductor-related groin pain. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Male elite and subelite players from 40 teams were contacted. In total, 28 soccer players with adductor-related groin pain and 16 soccer players without adductor-related groin pain (asymptomatic controls) were included in the study. In primary analysis, the dominant legs of 21 soccer players with adductor-related groin pain (≥4 weeks duration) were compared with the dominant legs of 16 asymptomatic controls using a cross-sectional design. The mean age of the symptomatic players was 24.5 ± 2.5 years, and the mean age of the asymptomatic controls was 22.9 ± 2.4 years. Isometric hip strength (adduction, abduction, and flexion) and eccentric hip strength (adduction) were assessed with a handheld dynamometer using reliable test procedures and a blinded assessor. RESULTS: Eccentric hip adduction strength was lower in soccer players with adductor-related groin pain in the dominant leg (n = 21) compared with asymptomatic controls (n = 16), namely 2.47 ± 0.49 versus 3.12 ± 0.43 N·m/kg, respectively (P < .001). No other hip strength differences were observed between symptomatic players and asymptomatic controls for the dominant leg (P = .35-.84). CONCLUSION: Large eccentric hip adduction strength deficits were found in soccer players with adductor-related groin pain compared with asymptomatic soccer players, while no isometric strength differences were observed between the groups

cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis

Recently, attempts have been made to reduce annotation requirements in feature-based self-explanatory models for lung nodule diagnosis. As a representative, cRedAnno achieves competitive performance with considerably reduced annotation needs by introducing self-supervised contrastive learning to do unsupervised feature extraction. However, it exhibits unstable performance under scarce annotation conditions. To improve the accuracy and robustness of cRedAnno, we propose an annotation exploitation mechanism by conducting semi-supervised active learning with sparse seeding and training quenching in the learned semantically meaningful reasoning space to jointly utilise the extracted features, annotations, and unlabelled data. The proposed approach achieves comparable or even higher malignancy prediction accuracy with 10x fewer annotations, meanwhile showing better robustness and nodule attribute prediction accuracy under the condition of 1% annotations. Our complete code is open-source available: https://github.com/diku-dk/credanno.Comment: 5 pages, 5 figures, 2 tables. arXiv admin note: text overlap with arXiv:2206.1360

Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis

Feature-based self-explanatory methods explain their classification in terms of human-understandable features. In the medical imaging community, this semantic matching of clinical knowledge adds significantly to the trustworthiness of the AI. However, the cost of additional annotation of features remains a pressing issue. We address this problem by proposing cRedAnno, a data-/annotation-efficient self-explanatory approach for lung nodule diagnosis. cRedAnno considerably reduces the annotation need by introducing self-supervised contrastive learning to alleviate the burden of learning most parameters from annotation, replacing end-to-end training with two-stage training. When training with hundreds of nodule samples and only 1% of their annotations, cRedAnno achieves competitive accuracy in predicting malignancy, meanwhile significantly surpassing most previous works in predicting nodule attributes. Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge. Our complete code is open-source available: https://github.com/diku-dk/credanno.Comment: 10 pages, 4 figures, 2 table