Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.

Background: Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices.

Transition to Metallic and Superconducting States Induced by Thermal or Electrical Deoxidation of the Dislocation Network in the Surface Region of SrTiO.

The question as to why deoxidized SrTiO becomes metallic and superconducting at extremely low levels of oxygen vacancy concentration has been a mystery for many decades. Here, we show that the real amount of effused oxygen during thermal reduction, which is needed to induce superconducting properties, is in the range of only 10/cm and thus even lower than the critical carrier concentrations assumed previously (10-10/cm). By performing detailed investigations of the optical and electrical properties down to the nanoscale, we reveal that filaments are forming during reduction along a network of dislocations in the surface layer.

Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning.

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023 [1]. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens.

Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial.

Objectives: Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms.

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

Background And Purpose: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation.