AIMC Topic: Retrospective Studies

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Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

European radiology
OBJECTIVES: Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), app...

Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.

Clinical & experimental ophthalmology
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...

Optimal intensive care outcome prediction over time using machine learning.

PloS one
BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions ...

Surgical trainee impact on bariatric surgery safety.

Surgical endoscopy
BACKGROUND: Roux-en-Y-gastric bypass (RYGB) and sleeve gastrectomy (SG) are commonly performed bariatric procedures that are associated with a significant learning curve. The effect of surgeon experience on perioperative outcomes and safety is establ...

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs.

Radiology
Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-performance automated binary classification of chest radiographs. Materials and Methods In a retrospective study, 216 431 frontal chest radiographs obtained between ...

Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps.

Computers in biology and medicine
AIMS: The aim of this study was to determine whether deep convolutional neural network (DCNN)-based features can represent the difference between cardiac sarcoidosis (CS) and non-CS using polar maps.

A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.

Dento maxillo facial radiology
OBJECTIVES:: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification...

Combined Low Dose Rate Brachytherapy and External Beam Radiation Therapy for Intermediate-Risk Prostate Cancer.

Journal of medical imaging and radiation sciences
INTRODUCTION: This is a retrospective study conducted to report the tumor control and late toxicity outcomes of patients with intermediate-risk prostate cancer undergoing combination external beam radiation therapy and low dose rate brachytherapy (LD...

Determination of appropriate urine volume cutoff values for voided urine specimens to assess adequacy.

Journal of the American Society of Cytopathology
INTRODUCTION: Incorporating urine volume into adequacy assessment was recommended by The Paris System for Reporting Urinary Cytology. The concept was relatively new, however, and supportive studies were sparse. We accordingly aimed to determine the r...