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OBJECTIVES: To determine the association between baseline cam morphology and self-reported hip pain assessed at annual visits over a 10-year follow-up period stratified by biological sex. The secondary aim was to study the association between the magnitude of cam morphology and the severity of pain in symptomatic hips. METHODS: The nationwide prospective Cohort Hip and Cohort Knee (CHECK) study includes 1,002 participants aged 45-65 years. Logistic regression with generalized estimating equations were used to determine the strength of the associations between (1) baseline cam morphology (both alpha angle ≥60° and as a continuous measure) and the presence of hip pain at 10 annual follow-up visits and (2) the alpha angle (continuous) and the severity of pain as classified by Numerical Rating Scale at 5-,8-, 9-, and 10-years. The results are expressed as odds ratios (OR), adjusted for age, biological sex (only in the sex-combined group), body mass index, and follow-up Kellgren and Lawrence grade. RESULTS: In total, 1,658 hips were included at baseline (1,335 female hips (79.2%)). The prevalence of cam morphology was 11.1% among all hips (29.1% in males; 6.4% in females). No association was found between cam morphology at baseline and the presence of hip pain at any follow-up in the female or sex-combined group. In males, only at 5-year follow-up, significant adjusted ORs were observed for the presence of cam morphology (1.77 (95%CI: 1.01-3.09)) and the alpha angle (1.02 (95%CI:1.00-1.04)). No evidence of associations was found between the alpha angle and the severity of hip pain in any of three groups. CONCLUSION: Within this study, no consistent associations were found between cam morphology and hip pain at multiple follow-ups. There might be a weak relationship between cam morphology and hip pain in males, while no such relation was found in females. We did not identify an association between the alpha angle and severity of hip pain.

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PURPOSE: Hip osteoarthritis (OA) is a major cause of pain and disability worldwide. Lack of effective therapies may reflect poor knowledge on its aetiology and risk factors, and result in the management of end-stage hip OA with costly joint replacement. The Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) consortium was established to pool and harmonise individual participant data from prospective cohort studies. The consortium aims to better understand determinants and risk factors for the development and progression of hip OA, to optimise and automate methods for (imaging) analysis, and to develop a personalised prediction model for hip OA. PARTICIPANTS: World COACH aimed to include participants of prospective cohort studies with ≥200 participants, that have hip imaging data available from at least 2 time points at least 4 years apart. All individual participant data, including clinical data, imaging (data), biochemical markers, questionnaires and genetic data, were collected and pooled into a single, individual-level database. FINDINGS TO DATE: World COACH currently consists of 9 cohorts, with 38 021 participants aged 18-80 years at baseline. Overall, 71% of the participants were women and mean baseline age was 65.3±8.6 years. Over 34 000 participants had baseline pelvic radiographs available, and over 22 000 had an additional pelvic radiograph after 8-12 years of follow-up. Even longer radiographic follow-up (15-25 years) is available for over 6000 of these participants. FUTURE PLANS: The World COACH consortium offers unique opportunities for studies on the relationship between determinants/risk factors and the development or progression of hip OA, by using harmonised data on clinical findings, imaging, biomarkers, genetics and lifestyle. This provides a unique opportunity to develop a personalised hip OA risk prediction model and to optimise methods for imaging analysis of the hip.

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Corneal thickness (pachymetry) maps can be used to monitor restoration of corneal endothelial function, for example after Descemet's membrane endothelial keratoplasty (DMEK). Automated delineation of the corneal interfaces in anterior segment optical coherence tomography (AS-OCT) can be challenging for corneas that are irregularly shaped due to pathology, or as a consequence of surgery, leading to incorrect thickness measurements. In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three different deep learning strategies were developed based on 960 B-scans from 50 patients. On an independent test set of 320 B-scans, corneal thickness could be measured with an error of 13.98 to 15.50 µm for the central 9 mm range, which is less than 3% of the average corneal thickness. The accurate thickness measurements were used to construct detailed pachymetry maps. Moreover, follow-up scans could be registered based on anatomical landmarks to obtain differential pachymetry maps. These maps may enable a more comprehensive understanding of the restoration of the endothelial function after DMEK, where thickness often varies throughout different regions of the cornea, and subsequently contribute to a standardized postoperative regime.

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