AIMC Topic: Retrospective Studies

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Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Gastroenterology
BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNN...

Running pattern of choroidal vessel in en face OCT images determined by machine learning-based quantitative method.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To evaluate the new method to quantitate the running pattern of the vessels in Haller's layer in en face optical coherence tomographic (OCT) images using the new algorithm.

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

European journal of radiology
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with ...

Prediction of early colorectal cancer metastasis by machine learning using digital slide images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Prediction of lymph node metastasis (LNM) for early colorectal cancer (CRC) is critical for determining treatment strategies after endoscopic resection. Some histologic parameters for predicting LNM have been established, b...

Performance of a Deep-Learning Neural Network to Detect Intracranial Aneurysms from 3D TOF-MRA Compared to Human Readers.

Clinical neuroradiology
PURPOSE: To study the clinical potential of a deep learning neural network (convolutional neural networks [CNN]) as a supportive tool for detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA) by comparing...

Feasibility of Natural Language Processing-Assisted Auditing of Critical Findings in Chest Radiology.

Journal of the American College of Radiology : JACR
OBJECTIVE: Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance in...

Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

BMC cancer
BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical s...

Long-term mesh erosion rate following abdominal robotic reconstructive pelvic floor surgery: a prospective study and overview of the literature.

International urogynecology journal
INTRODUCTION AND HYPOTHESIS: The use of synthetic mesh in transvaginal pelvic floor surgery has been subject to debate internationally. Although mesh erosion appears to be less associated with an abdominal approach, the long-term outcome has not been...

A machine learning approach for predictive models of adverse events following spine surgery.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Rates of adverse events following spine surgery vary widely by patient-, diagnosis-, and procedure-related factors. It is critical to understand the expected rates of complications and to be able to implement targeted efforts at limiting ...