AIMC Topic: Retrospective Studies

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Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm.

Clinical radiology
AIM: To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as a rule-out test for fully automated diagnosis in frontal adult chest radiographs (CXR) in an active clinical pathway.

Intraoral radiograph anatomical region classification using neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Dental radiography represents 13% of all radiological diagnostic imaging. Eliminating the need for manual classification of digital intraoral radiographs could be especially impactful in terms of time savings and metadata quality. However, a...

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence.

Oncology
BACKGROUND: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer. In clinical practice, about 20 to 30 clinicopathological factors and blood test data have been used. Various predictive factors for recurrence have b...

Robot-Assisted Radical Prostatectomy in Low-Volume Regions: Should It Be Abandoned or Adopted? A Multi-Institutional Outcome Study.

Journal of endourology
To present multinational experience in robot-assisted radical prostatectomy (RARP) by fellowship-trained expertise in low-volume regions in Gulf Cooperation Council (GCC) countries and to compare the current results with global outcomes reported in ...

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

Endokrynologia Polska
INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT, and compared the diagnostic performance of CNN models with that of radiologists.

Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Contrast-enhanced magnetic resonance angiography (MRA) is used to assess various cardiovascular conditions. However, gadolinium-based contrast agents (GBCAs) carry a risk of dose-related adverse effects.

Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).

Day-case catheterless and drainless minimal-access pyeloplasty in adults: A single-center experience of 13 years.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: To analyze our practice of drainless and catheterless day-case minimal-access pyeloplasty with regard to feasibility, safety and long-term outcomes.

Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study.

BMC nephrology
BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study's primary objective is to help identify which ICU patients are at high ris...