AIMC Topic: Abdomen

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Artificial Intelligence and ChatGPT in Abdominopelvic Surgery: A Systematic Review of Applications and Impact.

In vivo (Athens, Greece)
BACKGROUND/AIM: The integration of AI and natural language processing technologies, such as ChatGPT, into surgical practice has shown promising potential in enhancing various aspects of abdominopelvic surgical procedures. This systematic review aims ...

[Eight mm Port-Site Hernia Following Robot-Assisted Abdominoperineal Resection for Rectal Cancer-A Case Report].

Gan to kagaku ryoho. Cancer & chemotherapy
A 70s woman with a history of asthma and dyslipidemia underwent a robot-assisted abdominoperineal resection for rectal cancer. The ports were placed as per the method of Shizuoka Cancer Center and no intraoperative complications were observed. The co...

Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.

Mathematical biosciences and engineering : MBE
Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surroun...

Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image Quality.

Investigative radiology
OBJECTIVES: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradie...

Deep-learning-based Segmentation of Skeletal Muscle Mass in Routine Abdominal CT Scans.

In vivo (Athens, Greece)
BACKGROUND: For prediction of many types of clinical outcome, the skeletal muscle mass can be used as an independent biomarker. Manual segmentation of the skeletal muscles is time-consuming, therefore we present a deeplearning-based approach for the ...

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on re...

A Telerobotic Ultrasound Clinic Model of Ultrasound Service Delivery to Improve Access to Imaging in Rural and Remote Communities.

Journal of the American College of Radiology : JACR
OBJECTIVE: Patients living in many rural and remote areas do not have readily available access to ultrasound services because of a lack of sonographers and radiologists in these communities. The objective of this study was to determine the feasibilit...

Automatic segmentation of paravertebral muscles in abdominal CT scan by U-Net: The application of data augmentation technique to increase the Jaccard ratio of deep learning.

Medicine
Sarcopenia, characterized by a decline of skeletal muscle mass, has emerged as an important prognostic factor for cancer patients. Trunk computed tomography (CT) is a commonly used modality for assessment of cancer disease extent and treatment outcom...

ACTIVITY CONCENTRATION ESTIMATION IN AUTOMATED KIDNEY SEGMENTATION BASED ON CONVOLUTION NEURAL NETWORK METHOD FOR 177LU-SPECT/CT KIDNEY DOSIMETRY.

Radiation protection dosimetry
For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomography (CT) is accurate but time consuming and might be affected by misregistration between CT and SPECT images. This study develops a convolution neural n...

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

Investigative radiology
OBJECTIVE: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations ...