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Introduction Both Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming. To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases. Methods We performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm 3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning. Results (1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent. (3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion. Conclusions Automatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

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Active electronic implants are powered by primary batteries, which induces the necessity of implant replacement after battery depletion. This causes repeated interventions in a patients’ life, which bears the risk of complications and is costly. By using energy harvesting devices to power the implant, device replacements may be avoided and the device size may be reduced dramatically. Recently, several groups presented prototypes of implants powered by subcutaneous solar cells. However, data about the expected real-life power output of subcutaneously implanted solar cells was lacking so far. In this study, we report the first real-life validation data of energy harvesting by subcutaneous solar cells. Portable light measurement devices that feature solar cells (cell area = 3.6 cm2) and continuously measure a subcutaneous solar cell’s output power were built. The measurement devices were worn by volunteers in their daily routine in summer, autumn and winter. In addition to the measured output power, influences such as season, weather and human activity were analyzed. The obtained mean power over the whole study period was 67 µW (=19 µW cm−2), which is sufficient to power e.g. a cardiac pacemaker.