Deep learning-driven macroscopic AI segmentation model for brain tumor detection via digital pathology: Foundations for terahertz imaging-based AI diagnostics.

We used deep learning methods to develop an AI model capable of autonomously delineating cancerous regions in digital pathology images (H&E-stained images). By using a transgenic brain tumor model derived from the TS13-64 brain tumor cell line, we digitized a total of 187 H&E-stained images and annotated the cancerous regions in these images to compile a dataset. A deep learning approach was executed through DEEP:PHI, which abstracts Python coding complexities, thereby simplifying the execution of AI training protocols for users.

AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation.

Spinal-pelvic parameters are utilized in orthopedics for assessing patients' curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs.

Deep Learning for the Automatic Segmentation of Extracranial Venous Malformations of the Head and Neck from MR Images Using 3D U-Net.

Background: It is difficult to characterize extracranial venous malformations (VMs) of the head and neck region from magnetic resonance imaging (MRI) manually and one at a time. We attempted to perform the automatic segmentation of lesions from MRI of extracranial VMs using a convolutional neural network as a deep learning tool.

Methods: T2-weighted MRI from 53 patients with extracranial VMs in the head and neck region was used for annotations.

Glymphatic system dysfunction in patients with early chronic kidney disease.

Introduction: It is a recent finding that glymphatic system dysfunction contributes to various neurological problems. The purpose of this research was to assess the function of the glymphatic system in neurologically asymptomatic early chronic kidney disease (CKD) patients and healthy controls, using diffusion tensor image analysis along perivascular space (DTI-ALPS) index.

Methods: In a prospective study, we included patients with early CKD who were asymptomatic for neurological issues and obtained clinical and laboratory data.

Glymphatic Dysfunction in Patients With End-Stage Renal Disease.

Background: We aimed to compare glymphatic dysfunction between patients with end-stage renal disease (ESRD) and healthy controls and analyze the correlation between the glymphatic function and clinical characteristics using the diffusion tensor image analysis along with the perivascular space (DTI-ALPS) index.

Methods: We prospectively enrolled neurologically asymptomatic 49 patients with ESRD undergoing dialysis and 38 healthy controls. Diffusion tensor image was conducted using the same 3T scanner, and the DTI-ALPS index was calculated.