The Relationship Between Obesity and Lateral Condyle Fracture Healing: A Pilot Study.

Objective: Childhood obesity is a growing public health concern in the United States. Obesity has been shown to lead to increased complications with regards to orthopaedic injuries, such as more severe fracture patterns, notably observed in injuries like lateral condyle fractures of the humerus. However, there is currently a gap in the literature regarding the relationship between obesity and the healing potential of these fractures.

Test-retest reliability and longitudinal validity of drop vertical jump biomechanics during rehabilitation after ACL reconstruction.

Hip and knee biomechanics measured during a drop vertical jump (DVJ) can be used to assess patients undergoing rehabilitation after anterior cruciate ligament (ACL) reconstruction. To confidently interpret such data for use as outcome measures, additional information about reliability and validity is required. Therefore, the objective of this study was to estimate the test-retest reliability and longitudinal validity of selected lower limb biomechanics assessed during a DVJ in patients undergoing rehabilitation after ACL reconstruction.

Deep Learning-Based Abdominal Muscle Segmentation on CT Images of Surgical Patient Populations.

Computed tomography (CT) is commonly used for the characterization and tracking of abdominal muscle mass in surgical patients for both pre-surgical outcome predictions and post-surgical monitoring of response to therapy. In order to accurately track changes of abdominal muscle mass, radiologists must manually segment CT slices of patients, a time-consuming task with potential for variability. In this work, we combined a fully convolutional neural network (CNN) with high levels of preprocessing to improve segmentation quality.

Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features.

In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients.

CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum.

In severe cases, placenta accreta spectrum (PAS) requires emergency hysterectomy, endangering the life of both mother and fetus. Early prediction may reduce complications and aid in management decisions in these high-risk pregnancies. In this work, we developed a novel convolutional network architecture to combine MRI volumes, radiomic features, and custom feature maps to predict PAS severe enough to result in hysterectomy after fetal delivery in pregnant women.