CT-Based 3D Printing of the Glenoid Prior to Shoulder Arthroplasty: Bony Morphology and Model Evaluation.

To demonstrate the 3D printed appearance of glenoid morphologies relevant to shoulder replacement surgery and to evaluate the benefits of printed models of the glenoid with regard to surgical planning. A retrospective review of patients referred for shoulder CT was performed, leading to a cohort of nine patients without arthroplasty hardware and exhibiting glenoid changes relevant to shoulder arthroplasty planning. Thin slice CT images were used to create both humerus-subtracted volume renderings of the glenoid, as well as 3D surface models of the glenoid, and 11 printed models were created. Volume renderings, surface models, and printed models were reviewed by a musculoskeletal radiologist for accuracy. Four fellowship-trained orthopaedic surgeons specializing in shoulder surgery reviewed each case individually as follows: First, the source CT images were reviewed, and a score for the clarity of the bony morphologies relevant to shoulder arthroplasty surgery was given. The volume rendering was reviewed, and the clarity was again scored. Finally, the printed model was reviewed, and the clarity again scored. Each printed model was also scored for morphologic complexity, expected usefulness of the printed model, and physical properties of the model. Mann-Whitney-Wilcoxon signed rank tests of the clarity scores were calculated, and the Spearman's ρ correlation coefficient between complexity and usefulness scores was computed. Printed models demonstrated a range of glenoid bony changes including osteophytes, glenoid bone loss, retroversion, and biconcavity. Surgeons rated the glenoid morphology as more clear after review of humerus-subtracted volume rendering, compared with review of the source CT images (p = 0.00903). Clarity was also better with 3D printed models compared to CT (p = 0.00903) and better with 3D printed models compared to humerus-subtracted volume rendering (p = 0. 00879). The expected usefulness of printed models demonstrated a positive correlation with morphologic complexity, with Spearman's ρ 0.73 (p = 0.0108). 3D printing of the glenoid based on pre-operative CT provides a physical representation of patient anatomy. Printed models enabled shoulder surgeons to appreciate glenoid bony morphology more clearly compared to review of CT images or humerus-subtracted volume renderings. These models were more useful as glenoid complexity increased.

Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.

Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .

Changes in the plasmodial surface anion channel reduce leupeptin uptake and can confer drug resistance in Plasmodium falciparum-infected erythrocytes.

Cysteine protease inhibitors kill malaria parasites and are being pursued for development as antimalarial agents. Because they have multiple targets within bloodstream-stage parasites, workers have assumed that resistance to these inhibitors would not be acquired easily. In the present study, we used in vitro selection to generate a parasite resistant to growth inhibition by leupeptin, a broad-profile cysteine and serine protease inhibitor. Resistance was not associated with upregulation of cysteine protease activity, reduced leupeptin sensitivity of this activity, or expression level changes for putative cysteine or serine proteases in the parasite genome. Instead, it was associated with marked changes in the plasmodial surface anion channel (PSAC), an ion channel on infected erythrocytes that functions in nutrient and bulky organic solute uptake. Osmotic fragility measurements, electrophysiological recordings, and leupeptin uptake studies revealed selective reductions in organic solute permeability via PSAC, altered single-channel gating, and reduced inhibitor affinity. These changes yielded significantly reduced leupeptin uptake and could fully account for the acquired resistance. PSAC represents a novel route for the uptake of bulky hydrophilic compounds acting against intraerythrocytic parasite targets. Drug development based on such compounds should proceed cautiously in light of possible resistance development though the selection of PSAC mutants.