AI Medical Compendium Journal:
International journal of computer assisted radiology and surgery

Showing 1 to 10 of 481 articles

Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography.

International journal of computer assisted radiology and surgery
PURPOSE: Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This stud...

Robot-assisted ultrasound probe calibration for image-guided interventions.

International journal of computer assisted radiology and surgery
BACKGROUND: Trackable ultrasound probes facilitate ultrasound-guided procedures, allowing real-time fusion of augmented ultrasound images and live video streams. The integration aids surgeons in accurately locating lesions within organs, and this cou...

Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.

International journal of computer assisted radiology and surgery
PURPOSE: This study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery.

Multi-dimensional consistency learning between 2D Swin U-Net and 3D U-Net for intestine segmentation from CT volume.

International journal of computer assisted radiology and surgery
PURPOSE: The paper introduces a novel two-step network based on semi-supervised learning for intestine segmentation from CT volumes. The intestine folds in the abdomen with complex spatial structures and contact with neighboring organs that bring dif...

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. ...

Breaking barriers: noninvasive AI model for BRAF mutation identification.

International journal of computer assisted radiology and surgery
OBJECTIVE: BRAF is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic featu...

Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models.

International journal of computer assisted radiology and surgery
PURPOSE: Statistical shape models (SSMs) are widely used for morphological assessment of anatomical structures. However, a key limitation is the need for a clear relationship between the model's shape coefficients and clinically relevant anatomical p...

Double-mix pseudo-label framework: enhancing semi-supervised segmentation on category-imbalanced CT volumes.

International journal of computer assisted radiology and surgery
PURPOSE: Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited ...

A deep learning-driven method for safe and effective ERCP cannulation.

International journal of computer assisted radiology and surgery
PURPOSE: In recent years, the detection of the duodenal papilla and surgical cannula has become a critical task in computer-assisted endoscopic retrograde cholangiopancreatography (ERCP) cannulation operations. The complex surgical anatomy, coupled w...

German surgeons' perspective on the application of artificial intelligence in clinical decision-making.

International journal of computer assisted radiology and surgery
PURPOSE: Artificial intelligence (AI) is transforming clinical decision-making (CDM). This application of AI should be a conscious choice to avoid technological determinism. The surgeons' perspective is needed to guide further implementation.