Diagnosis on Ultrasound Images for Developmental Dysplasia of the Hip with a Deep Learning-Based Model Focusing on Signal Heterogeneity in the Bone Region.

Developmental dysplasia of the hip (DDH) is a prevalent issue in infants, with ultrasound crucial for early detection. Existing automatic diagnostic models lack precision due to noise, but 3D technology may enhance it. This study aimed to create and assess a deep-learning-based model for automated DDH diagnosis by using 3D transformation technology on two-dimensional ultrasound images.

Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography.

Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. However, the imaging features that are clinically crucial for lymph node metastasis have not been fully elucidated. Hence, we developed a bimodal model to predict ALN metastasis in patients with early breast cancer by integrating CEUS images with the annotated imaging features.

Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements.

Bimodal convolutional neural networks (CNNs) are frequently combined with patient information or several medical images to enhance the diagnostic performance. However, the technologies that integrate automatically generated clinical measurements within the images are scarce. Hence, we developed a bimodal model that produced automatic algorithm for clinical measurement (aaCM) from radiographic images and integrated the model with CNNs.

Fatal case of subdural empyema caused by and .

We report a fatal subdural empyema caused by in a 66-year-old female who developed acute onset of confusion, dysarthria, and paresis in her left extremities. A CT scan showed hypodensity in a crescentic formation with a mild mid-line shift. She had a bruise on her forehead caused by a fall several days before admission, which initially raised subdural hematoma (SDH) diagnosis, and a burr hole procedure was planned.

Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture.

Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we collected a registry-based longitudinal dataset that contained more than 7000 patients with fragility fractures treated surgically to detect potential predictors for clinical refracture.