AIMC Topic: Retrospective Studies

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Intraoperative left-sided colorectal anastomotic testing in clinical practice: a multi-treatment machine-learning analysis of the iCral3 prospective cohort.

Updates in surgery
BACKGROUND: Current evidence about intraoperative anastomotic testing after left-sided colorectal resections is still controversial. The aim of this study was to analyze the impact of Indocyanine Green fluorescent angiography (ICG-FA) and air-leak te...

Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid.

RMD open
OBJECTIVES: Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Long-term opioid use is associated with several adverse outcomes. Identifying factors associated with long-term opioid use is the first step in developing...

Prediction of adolescent weight status by machine learning: a population-based study.

BMC public health
BACKGROUND: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have be...

Surveying haemoperfusion impact on COVID-19 from machine learning using Shapley values.

Inflammopharmacology
BACKGROUND: Haemoperfusion (HP) is an innovative extracorporeal therapy that utilizes special cartridges to filter the blood, effectively removing pro-inflammatory cytokines, toxins, and pathogens in COVID-19 patients. This retrospective cohort study...

Automated segmentation for early detection of uveal melanoma.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
OBJECTIVE: Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncol...

Performance of AI to exclude normal chest radiographs to reduce radiologists' workload.

European radiology
INTRODUCTION: This study investigates the performance of a commercially available artificial intelligence (AI) system to identify normal chest radiographs and its potential to reduce radiologist workload.

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

Abdominal radiology (New York)
PURPOSE: To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phas...

Machine Learning Differentiates Extracorporeal Membrane Oxygenation Mortality Risk Profiles Among Trauma Patients.

The American surgeon
BACKGROUND: Extracorporeal membrane oxygenation (ECMO) is resource intensive with high mortality. Identifying trauma patients most likely to derive a survival benefit remains elusive despite current ECMO guidelines. Our objective was to identify uniq...

Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study.

Clinical radiology
AIMS: The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imaging (MRI) radiomics and clinical features to distinguish intraspinal schwannomas from meningiomas.