Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

Objectives To compare spinal bone measures derived from automatic and manual assessment in routine CT with dual energy X-ray absorptiometry (DXA) in their association with prevalent osteoporotic vertebral fractures using our fully automated framework (https://anduin.bonescreen.de) to assess various bone measures in clinical CT. Methods We included 192 patients (141 women, 51 men; age 70.2 ± 9.7 years) who had lumbar DXA and CT available (within 1 year). Automatic assessment of spinal bone measures in CT included segmentation of vertebrae using a convolutional neural network (CNN), reduction to the vertebral body, and extraction of bone mineral content (BMC), trabecular and integral volumetric bone mineral density (vBMD), and CT-based areal BMD (aBMD) using asynchronous calibration. Moreover, trabecular bone was manually sampled (manual vBMD). Results A total of 148 patients (77%) had vertebral fractures and significantly lower values in all bone measures compared to patients without fractures (p ≤ 0.001). Except for BMC, all CT-based measures performed significantly better as predictors for vertebral fractures compared to DXA (e.g., AUC = 0.885 for trabecular vBMD and AUC = 0.86 for integral vBMD vs. AUC = 0.668 for DXA aBMD, respectively; both p < 0.001). Age- and sex-adjusted associations with fracture status were strongest for manual vBMD (OR = 7.3, [95%] CI 3.8–14.3) followed by automatically assessed trabecular vBMD (OR = 6.9, CI 3.5–13.4) and integral vBMD (OR = 4.3, CI 2.5–7.6). Diagnostic cutoffs of integral vBMD for osteoporosis (< 160 mg/cm3) or low bone mass (160 ≤ BMD < 190 mg/cm3) had sensitivity (84%/41%) and specificity (78%/95%) similar to trabecular vBMD. Conclusions Fully automatic osteoporosis screening in routine CT of the spine is feasible. CT-based measures can better identify individuals with reduced bone mass who suffered from vertebral fractures than DXA. Key Points • Opportunistic osteoporosis screening of spinal bone measures derived from clinical routine CT is feasible in a fully automatic fashion using a deep learning-driven framework (https://anduin.bonescreen.de). • Manually sampled volumetric BMD (vBMD) and automatically assessed trabecular and integral vBMD were the best predictors for prevalent vertebral fractures. • Except for bone mineral content, all CT-based bone measures performed significantly better than DXA-based measures. • We introduce diagnostic thresholds of integral vBMD for osteoporosis (< 160 mg/cm3) and low bone mass (160 ≤ BMD < 190 mg/cm3) with almost equal sensitivity and specificity compared to conventional thresholds of quantitative CT as proposed by the American College of Radiology (osteoporosis < 80 mg/cm3).

Medication-related osteonecrosis of the jaw (MRONJ) has become a well-known side effect of antiresorptive, and antiangiogenic drugs commonly used in cancer management. Despite a considerable amount of literature addressing MRONJ, it is still widely accepted that the underlying pathomechanism of MRONJ is unclear. However, several clinical and preclinical studies indicate that infection seems to have a major role in the pathogenesis of MRONJ. Although there is no conclusive evidence for the infection hypothesis yet, available data have shown a robust association between local infection and MRONJ development. This observation is very critical in order to implement policies to reduce the risk of MRONJ in patients under antiresorptive drugs. This critical review was conducted to collect the most reliable evidence regarding the link between local infection and MRONJ pathogenesis.

Reconstruction of bone defects represents a serious issue for orthopaedic and maxillofacial surgeons, especially in extensive bone loss. Adipose-derived mesenchymal stem cells (ADScs) with tri-calcium phosphates (tcp) are widely used for bone regeneration facilitating the formation of bone extracellular matrix to promote reparative osteogenesis. The present study assessed the potential of cell-scaffold constructs for the regeneration of extensive mandibular bone defects in a minipig model. Sixteen skeletally mature miniature pigs were divided into two groups: Control group and scaffolds seeded with osteogenic differentiated pADSCs (n = 8/group). TCP-PLGA scaffolds with or without cells were integrated in the mandibular critical size defects and fixed by titanium osteosynthesis plates. After 12 weeks, ADSCs seeded scaffolds (n = 7) demonstrated significantly higher bone volume (34.8% ± 4.80%) than scaffolds implanted without cells (n = 6, 22.4% ± 9.85%) in the micro-CT (p < 0.05). Moreover, an increased amount of osteocalcin deposition was found in the test group in comparison to the control group (27.98 ± 2.81% vs 17.10 ± 3.57%, p < 0.001). In conclusion, ADSCs seeding on ceramic/polymer scaffolds improves bone regeneration in large mandibular defects. However, further improvement with regard to the osteogenic capacity is necessary to transfer this concept into clinical use. Maxillofacial bone defects, which occur due to trauma, craniofacial deformities, tumour or infection can lead to facial deformities and severe maxillofacial dysfunctions provoking a dramatic decrease in the quality of life of the patients 1,2. The reconstruction of large bone defects poses many challenges in oral and maxillofacial surgery. Although autologous bone grafts are considered the gold standard in bone defect repair, there are some concerns related to the limited supply and donor site morbidity 3. Several alternatives as allografts, xenografts or synthetic bone substitutes have been brought by researchers and clinicians to restore the function and architecture of the defective bone but still cannot solve the problem due to various limitations. The search of new treatment alternatives has emerged enormously in the past few