Distal migration of a floating carotid thrombus in a patient using oral contraceptives: a case report

<p>Abstract</p> <p>Introduction</p> <p>We report the case of a patient with distal migration of a floating carotid thrombus caused by oral contraceptives.</p> <p>Case presentation</p> <p>A 48-year-old woman using oral contraceptives suffered from dysarthria and gait disturbance. Examinations, including ultrasound and cerebral arteriogram, revealed a floating thrombus at the left carotid bifurcation with no stenosis. Despite antithrombotic therapy, the floating carotid thrombus migrated to the ipsilateral middle cerebral artery, resulting in a severe stroke.</p> <p>Conclusion</p> <p>Some floating thrombi are resistant to conservative therapy and have a risk of distal migration, which may cause a major stroke in the acute stage.</p

Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model

Background: This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice. Methods: We collected 4691 X-ray images of the cervical spine of the 906 patients with RA. Among these images, 3480 were used for training the deep learning model, 803 were used for validating the model during the training process, and the remaining 408 were used for testing the performance of the trained model. The two-dimensional key points’ detection model of Deep High-Resolution Representation Learning for Human Pose Estimation was adopted as the base convolutional neural network model. The model inferred four coordinates to calculate the atlantodental interval (ADI) and space available for the spinal cord (SAC). Finally, these values were compared with those by clinicians to evaluate the performance of the model. Results: Among the 408 cervical images for testing the performance, the trained model correctly identified the four coordinates in 99.5% of the dataset. The values of ADI and SAC were positively correlated among the model and two clinicians. The sensitivity of AAS diagnosis with ADI or SAC by the model was 0.86 and 0.97 respectively. The specificity of that was 0.57 and 0.5 respectively. Conclusions: We present the development of a deep learning model for the evaluation of cervical lesions of patients with RA. The model was demonstrably shown to be useful for quantitative evaluation.Okita Y., Hirano T., Wang B., et al. Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model. Arthritis Research and Therapy 25, 181 (2023); 10.1186/s13075-023-03172-x