Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder.

Objective: Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely performed. Therefore, we evaluated a commercially available EEG data analysis system to help guide medication selection in a clinical setting.

Deep learning method for segmentation of rotator cuff muscles on MR images.

Objective: To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. We hypothesized a CNN approach can accurately perform both tasks compared with manual reference standards.

Material And Methods: We created 2 models: model A for Y-view selection and model B for muscle segmentation.

A Retrospective Outcomes Study of 20 Sacroiliac Joint Fusion Patients.

Study Design: Retrospective case series.

Purpose: To report a novel approach to open posterior sacroiliac (SI) joint arthrodesis using a threaded titanium cage containing rhBMP--2.

Materials & Methods: Twenty consecutive patients with a mean age of 57.