Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging. Patients who received both lumbar dual-energy X-ray absorptiometry (DEXA) and MRI (n = 120) or CT (n = 100) examinations were included in this study. Unimodal and multimodal convolutional neural networks (CNNs) with dual blocks were proposed to predict osteoporosis using lumbar vertebrae MR and CT examinations in separate and combined datasets. Bone mineral density values obtained by DEXA were used as reference data. The proposed models were compared with a CNN model and six benchmark pre-trained deep-learning models. The proposed unimodal model obtained 96.54%, 98.84%, and 96.76% balanced accuracy for MRI, CT, and combined datasets, respectively, while the multimodal model achieved 98.90% balanced accuracy in 5-fold cross-validation experiments. Furthermore, the models obtained 95.68%-97.91% accuracy with a hold-out validation dataset. In addition, comparative experiments demonstrated that the proposed models yielded superior results by providing more effective feature extraction in dual blocks to predict osteoporosis. This study demonstrated that osteoporosis was accurately predicted by the proposed models using both MR and CT images, and a multimodal approach improved the prediction of osteoporosis. With further research involving prospective studies with a larger number of patients, there may be an opportunity to implement these technologies into clinical practice.

Background: The aetiology of anterior knee pain is multifactorial, and any patellofemoral structure can be the cause of the symptoms. Introduction: For the last decades, the suprapatellar fat pad is studied as a potential cause of anterior knee pain. Since the results in the literature are contradictory, in this study, we tried to reevaluate the role of the suprapatellar fat pad in the development of anterior knee pain. Methods: Total 156 knee MRIs of 150 patients from December 2015 to July 2019 were retrospectively evaluated by a radiologist for the configuration of the suprapatellar fat pads. The population was divided into two groups according to the presence of anterior knee pain. Maximum craniocaudal, anteroposterior and oblique diameters, and relative signal intensity indexes of the suprapatellar fat pads were measured and compared between the groups. Configurations of the fat pads were recorded, and measurements of the triangular-shaped and convex posterior bordered suprapatellar fat pads were compared. A student t-test was used for statistical analysis. Statistically, significant differences and correlations were defined as p<0.05. Results: The diameters and relative signal intensity indexes of the fat pads showed no significant difference between the groups (p=0.588, 0.744, 0.874, and 0.201, respectively). Anteroposterior and oblique diameters and relative signal intensity indexes of the suprapatellar fat pads were correlated with the convex posterior border (p=0.001, 0.003, and 0.000, respectively). Conclusion: Oedema-like changes of the suprapatellar fat pad seen at knee MRIs are correlated with the configuration of the fat pad but are rarely associated with anterior knee pain.

Kucukciloglu Y, et al. Clin Orthop Surg. 2022 Jul;14:e37. https://doi.org/10.4055/cios21261

Axial spondyloarthritis (SpA) is an inflammatory disease that causes back pain by affecting the axial skeleton, and the sacroiliac (SI) joints are mostly involved. An early diagnosis is required to prevent structural damage. Magnetic resonance imaging (MRI) is the primary tool for the diagnosis of axial spondyloarthritis; however, the occurrence of noninflammatory degenerative changes might prevent the accurate diagnosis even for experts. Deep learning (DL) aims to assist radiologists in detecting and diagnosing sacroiliitis by providing distinct and effective feature extraction along MR sequences. This retrospective study considers a primary dataset with 50 clinical sacroiliitis and 50 control group patients and aims to diagnose sacroiliitis using 4 MRI sequences. For this purpose, a simplified convolutional neural network model is developed, and comprehensive comparative experiments and analyses are performed. Three pre-trained DL models are considered in a comparative study using a transfer learning approach. Image-based, sequence-based, and patient-based experiments are conducted to evaluate general diagnostic abilities, analyze further clinical implementations, and determine the most informative and challenging MRI sequences. The results showed that the proposed model has the ability to detect sacroiliitis with 0.951 and 0.977 accuracy in patient and image-based experiments, respectively. The deep learning models obtained promising results for future clinical implementation to assist radiologists in detecting sacroiliitis. It is also analyzed that the STIR coronal images are the most challenging, while T1 axial sequences are the most informative sequences for sacroiliitis diagnosis.

Herpes simplex viruses (HSVs) belong to the Herpesviridae family. Close contact is the primary mode of transmission for both HSV-1 and HSV-2, leading to a persistent lifelong infection. HSVs are widely recognized as causative agents of viral infections affecting the central nervous system, capable of presenting as both meningitis and encephalitis. Herpes simplex virus type 1 (HSV-1) is the predominant viral cause of encephalitis, accounting for the majority of cases. Here, a rare co-infection case of meningoencephalitis, associated with HSV-1 and rickettsia is described. A 42-year-old man presenting with non-remitting headache for 6 days, fever, sweating, and muscle aches was admitted to the Emergency Department. His Weil-Felix test was positive for Proteus OX2 indicating rickettsial infection. Therapy started promptly however patient’s condition deteriorated. Cerebrospinal fluid (CSF) analysis revealed lymphocytic pleocytosis, and elevated protein concentration. CSF molecular analysis was positive for HSV-1. His cranial MRI indicated cytotoxic edema and gyral enhancement at the right temporal lobe. He was administered acyclovir for 14 days during hospital stay and was successfully discharged. This case report highlights that HSV-1 meningoencephalitis can co-occur with rickettsia infection in immunocompetent individuals, and co-infection with other agents should always be considered to avoid the progression of the disease.