To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore the role of multifeature fusion. Data from 137 breast cancer postoperative patients with new nodules...
International journal of computer assisted radiology and surgery
Feb 15, 2025
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...
PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the ...
OBJECTIVE: This study aims to develop and validate a predictive model for thyroid nodule malignancy risks using clinical and ultrasonography features and a machine learning (ML) approach.
PURPOSE: B-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a challenge, especially for novice point of care ultrasound (POCUS) practitioners. This study aimed t...
Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangioscopy (D-SOC) holds promise for improving diagnostic accuracy in indeterminate BS. This multicenter st...
OBJECTIVES: Radiology reports contain valuable information for research and audits, but relevant details are often buried within free-text fields. This makes them challenging and time-consuming to extract for secondary analyses, including training ar...
RATIONALE AND OBJECTIVES: To evaluate the value of artificial intelligence (AI) assisted diagnostic system in reconstructing axial lumbar disc CT images and diagnosing lumbar disc herniation.
BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts of text, enabling humanlike and semantical responses to text-based inputs ...
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