AIMC Topic: Retrospective Studies

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A Deep-Learning Algorithm-Enhanced System Integrating Electrocardiograms and Chest X-rays for Diagnosing Aortic Dissection.

The Canadian journal of cardiology
BACKGROUND: Chest pain is the most common symptom of aortic dissection (AD), but it is often confused with other prevalent cardiopulmonary diseases. We aimed to develop deep-learning models (DLMs) with electrocardiography (ECG) and chest x-ray (CXR) ...

Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms.

Abdominal radiology (New York)
BACKGROUND: Deep learning Computed Tomography (CT) reconstruction (DLR) algorithms promise to improve image quality but the impact on clinical diagnostic performance remains to be demonstrated. We aimed to compare DLR to standard iterative reconstruc...

A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage.

Stroke
BACKGROUND AND PURPOSE: Hematoma volume (HV) is a significant diagnosis for determining the clinical stage and therapeutic approach for intracerebral hemorrhage (ICH). The aim of this study is to develop a robust deep learning segmentation method for...

Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Diagnosis of esophageal squamous cell carcinoma (ESCC) is complicated and requires substantial expertise and experience. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC under multi...

The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.

Journal of digital imaging
With vast interest in machine learning applications, more investigators are proposing to assemble large datasets for machine learning applications. We aim to delineate multiple possible roadblocks to exam retrieval that may present themselves and lea...

Deep-learning model for screening sepsis using electrocardiography.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a d...

Retrospective study of deep learning to reduce noise in non-contrast head CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PURPOSE: Presented herein is a novel CT denoising method uses a skip residual encoder-decoder framework with group convolutions and a novel loss function to improve the subjective and objective image quality for improved disease detection in patients...

Refractive outcomes of second-eye adjustment methods on intraocular lens power calculation in second eye.

Clinical & experimental ophthalmology
BACKGROUND: To investigate the refractive outcomes of second-eye adjustment (SEA) methods in different intraocular lens (IOL) power calculation formulas for second eye following bilateral sequential cataract surgery.

Perioperative outcomes of intracorporeal robot-assisted radical cystectomy versus open radical cystectomy: A systematic review and meta-analysis of comparative studies.

International journal of surgery (London, England)
PURPOSE: To systematically review studies comparing the perioperative outcomes of intracorporeal robot-assisted radical cystectomy (iRARC) and open radical cystectomy (ORC).

Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

Surgical endoscopy
BACKGROUND: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may so...