Background: Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited. Methods: We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets. Results: Using different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024). Conclusions: The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.

Objective Since the first introduction of the MOCART (Magnetic Resonance Observation of Cartilage Repair Tissue) score, significant progress has been made with regard to surgical treatment options for cartilage defects, as well as magnetic resonance imaging (MRI) of such defects. Thus, the aim of this study was to introduce the MOCART 2.0 knee score — an incremental update on the original MOCART score — that incorporates this progression. Materials and Methods The volume of cartilage defect filling is now assessed in 25% increments, with hypertrophic filling of up to 150% receiving the same scoring as complete repair. Integration now assesses only the integration to neighboring native cartilage, and the severity of surface irregularities is assessed in reference to cartilage repair length rather than depth. The signal intensity of the repair tissue differentiates normal signal, minor abnormal, or severely abnormal signal alterations. The assessment of the variables “subchondral lamina,” “adhesions,” and “synovitis” was removed and the points were reallocated to the new variable “bony defect or bony overgrowth.” The variable “subchondral bone” was renamed to “subchondral changes” and assesses minor and severe edema-like marrow signal, as well as subchondral cysts or osteonecrosis-like signal. Overall, a MOCART 2.0 knee score ranging from 0 to 100 points may be reached. Four independent readers (two expert readers and two radiology residents with limited experience) assessed the 3 T MRI examinations of 24 patients, who had undergone cartilage repair of a femoral cartilage defect using the new MOCART 2.0 knee score. One of the expert readers and both inexperienced readers performed two readings, separated by a four-week interval. For the inexperienced readers, the first reading was based on the evaluation sheet only. For the second reading, a newly introduced atlas was used as an additional reference. Intrarater and interrater reliability was assessed using intraclass correlation coefficients (ICCs) and weighted kappa statistics. ICCs were interpreted according to Koo and Li; weighted kappa statistics were interpreted according to the criteria of Landis and Koch. Results The overall intrarater (ICC = 0.88, P < 0.001) as well as the interrater (ICC = 0.84, P < 0.001) reliability of the expert readers was almost perfect. Based on the evaluation sheet of the MOCART 2.0 knee score, the overall interrater reliability of the inexperienced readers was poor (ICC = 0.34, P < 0.019) and improved to moderate (ICC = 0.59, P = 0.001) with the use of the atlas. Conclusions The MOCART 2.0 knee score was updated to account for changes in the past decade and demonstrates almost perfect interrater and intrarater reliability in expert readers. In inexperienced readers, use of the atlas may improve interrater reliability and, thus, increase the comparability of results across studies.

Many women in Latin America lack knowledge of postmenopausal vaginal atrophy, not appreciating the chronic nature of the condition, and may benefit from dialog initiated by health-care professionals to facilitate greater understanding and increased awareness of the availability of effective treatment.

The term "intercondylar space" is introduced as a morphologic description of the osseous intercondylar notch and adjacent structures. The femur as a whole is subject to substantial plastic deformation throughout life, not only in its proximal part, with respect to torsion, but also in its distal extent.

Several studies have shown that adherence to growth hormone therapy (GHT) is not optimal. There are several reasons why patients may not fully adhere to their treatment regimen and this may have implications on treatment success, patient outcomes and healthcare spending and resourcing. A change in healthcare practices, from a physician paternalistic to a more patient autonomous approach to healthcare, has encouraged a greater onus on a shared decision-making (SDM) process whereby patients are actively encouraged to participate in their own healthcare decisions. There is growing evidence to suggest that SDM may facilitate patient adherence to GHT. Improved adherence to therapy in this way may consequently positively impact treatment outcomes for patients. Whilst SDM is widely regarded as a healthcare imperative, there is little guidance on how it should be best implemented. Despite this, there are many opportunities for the implementation of SDM during the treatment journey of a patient with a GH-related disorder. Barriers to the successful practice of SDM within the clinic may include poor patient education surrounding their condition and treatment options, limited healthcare professional time, lack of support from clinics to use SDM, and healthcare resourcing restrictions. Here we discuss the opportunities for the implementation of SDM and the barriers that challenge its effective use within the clinic. We also review some of the potential solutions to overcome these challenges that may prove key to effective patient participation in treatment decisions. Encouraging a sense of empowerment for patients will ultimately enhance treatment adherence and improve clinical outcomes in GHT.