AI-Based Automated Quantification of Arterial Stenosis in Head and Neck CT Angiography: A Comparison with Manual Measurements from Digital Subtraction Angiography and CT Angiography.

Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm for automated quantification of arterial stenosis in head and neck CT angiography (CTA).

Methods: Patients who received head and neck CTA and DSA between January 2019 and December 2021 in two centers were included. The quantitative performance of CerebralDoc per-lesion was evaluated through intraclass correlation coefficients (ICCs) and Bland-Altman analysis, comparing automated stenosis measurements and manual measurements across 0-100%, < 50%, ≥ 50% and ≥ 70% thresholds.

Body physical parameters instead of water equivalent diameter to calculate size-specific dose estimate in adult chest CT.

This study aimed to investigate body physical parameters as substitutes for water equivalent diameter (Dw) while calculating size-specific dose estimates (SSDEs) during adult chest computed tomography (CT). A retrospective analysis was conducted on 776 patients. Patients were divided into training set (542 patients) and validation set (234 patients) according to a ratio of 7:3.

Potentiating dual-directional immunometabolic regulation with nanomedicine to enhance anti-tumor immunotherapy following incomplete photothermal ablation.

Photothermal therapy (PTT) is a promising cancer treatment method due to its ability to induce tumor-specific T cell responses and enhance therapeutic outcomes. However, incomplete PTT can leave residual tumors that often lead to new metastases and decreased patient survival in clinical scenarios. This is primarily due to the release of ATP, a damage-associated molecular pattern that quickly transforms into the immunosuppressive metabolite adenosine by CD39, prevalent in the tumor microenvironment, thus promoting tumor immune evasion.

A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.

Purpose: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP).

Methods: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc.

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

Objectives: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.

Methods: Patients with lumbar VCFs were retrospectively enrolled from January 2011 to May 2020. All patients underwent DR and MRI scanning.