AIMC Topic: Retrospective Studies

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Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence.

Colombia medica (Cali, Colombia)
BACKGROUND: Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy.

Modeling of valve-in-valve transcatheter aortic valve implantation after aortic root replacement using a 3-dimensional artificial intelligence algorithm.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: Aortic root replacement requires construction of a composite valve-graft and reimplantation of coronary arteries. This study assessed the feasibility of valve-in-valve transcatheter aortic valve implantation after aortic root replacement.

Artificial intelligence-assisted identification and retrieval of chronic hepatitis C patients lost to follow-up in the health area of Pontevedra and O Salnés (Spain).

Gastroenterologia y hepatologia
OBJECTIVE: Direct-acting antivirals (DAAs) to treat hepatitis C virus (HCV) infection offer an opportunity to eliminate the disease. This study aimed to identify and relink to care HCV patients previously lost to medical follow-up in the health area ...

A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models.

Academic radiology
RATIONALE AND OBJECTIVES: Pancreas segmentation accuracy at CT is critical for the identification of pancreatic pathologies and is essential for the development of imaging biomarkers. Our objective was to benchmark the performance of five high-perfor...

Gender Estimation from Morphometric Measurements of Mandibular Lingula by Using Machine Learning Algorithms and Artificial Neural Networks.

Nigerian journal of clinical practice
BACKGROUND: Sex determination from the bones is of great importance for forensic medicine and anthropology. The mandible is highly valued because it is the strongest, largest and most dimorphic bone in the skull.

Ultra-high resolution computed tomography with deep-learning-reconstruction: diagnostic ability in the assessment of gastric cancer and the depth of invasion.

Abdominal radiology (New York)
PURPOSE: To evaluate the image quality of ultra-high-resolution CT (U-HRCT) images reconstructed using an improved deep-learning-reconstruction (DLR) method. Additionally, we assessed the utility of U-HRCT in visualizing gastric wall structure, detec...

LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning.

Journal of imaging informatics in medicine
Postoperative complications of radical gastrectomy seriously affect postoperative recovery and require accurate risk prediction. Therefore, this study aimed to develop a prediction model specifically tailored to guide perioperative clinical decision-...

Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

Epilepsy research
BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is unc...

Predicting Therapeutic Response to Hypoglossal Nerve Stimulation Using Deep Learning.

The Laryngoscope
OBJECTIVES: To develop and validate machine learning (ML) and deep learning (DL) models using drug-induced sleep endoscopy (DISE) images to predict the therapeutic efficacy of hypoglossal nerve stimulator (HGNS) implantation.