AIMC Topic: Retrospective Studies

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Radiomics in Carotid Plaque: A Systematic Review and Radiomics Quality Score Assessment.

Ultrasound in medicine & biology
Imaging modalities provide information on plaque morphology and vulnerability; however, they are operator dependent and miss a great deal of microscopic information. Recently, many radiomics models for carotid plaque that identify unstable plaques an...

Beyond SEP-1 Compliance: Assessing the Impact of Antibiotic Overtreatment and Fluid Overload in Suspected Septic Patients.

The Journal of emergency medicine
BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) developed the Severe Sepsis and Septic Shock Performance Measure bundle (SEP-1) metric to improve sepsis care, but evidence supporting this bundle is limited and harms secondary to comp...

Using deep learning models in magnetic resonance cholangiopancreatography images to diagnose common bile duct stones.

Scandinavian journal of gastroenterology
BACKGROUNDS AND AIMS: Magnetic resonance cholangiopancreatography (MRCP) plays a significant role in diagnosing common bile duct stones (CBDS). Currently, there are no studies to detect CBDS by using the deep learning (DL) model in MRCP. This study a...

Adapting to a Robotic Era: The Transferability of Open and Laparoscopic Skills to Robotic Surgery.

Journal of surgical education
BACKGROUND: The learning curve of robotic surgical skills is poorly understood. There is a lack of data on the transferability of skills from open and laparoscopic training to robotic surgery. In this retrospective cohort study, we investigated the i...

100 Complex posterior spinal fusion cases performed with robotic instrumentation.

Journal of robotic surgery
Robotic navigation has been shown to increase precision, accuracy, and safety during spinal reconstructive procedures. There is a paucity of literature describing the best techniques for robotic-assisted spine surgery for complex, multilevel cases or...

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

Abdominal radiology (New York)
PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases.

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

PloS one
OBJECTIVES: The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).

Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study wa...

The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study.

Journal of gastrointestinal cancer
PURPOSE: Due to significant growth in the volume of information produced by cancer research, staying abreast of recent developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of l...