AIMC Topic: Retrospective Studies

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Effect of artificial intelligence implementation to the latest generation 4K colonoscopy.

Polski przeglad chirurgiczny
<b>Indroduction:</b> Colonoscopy is an acclaimed screening test to detect colorectal cancer (CRC). The most important quality indicators for colonoscopy are adenoma detection rate (ADR), cecal intubation rate (CIR), withdrawal time (WT), ...

Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.

Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale
OBJECTIVE: If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning...

High Prevalence of Artifacts in Optical Coherence Tomography With Adequate Signal Strength.

Translational vision science & technology
PURPOSE: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment...

Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography.

Radiology
Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. Purpose To evaluate AI localizations of interval cancers (ICs) on screening mammograms by IC category...

A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer.

Cancer medicine
BACKGROUND: To explore the efficacy of a prediction model based on diffusion-weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify...

Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Radiology
Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneur...

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.

Radiology
Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiograph...

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

Clinical cardiology
BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant clinical challenges. In recent years, machine learning (ML) models have demonstrated potential in disea...

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Radiology
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purp...