AIMC Topic: Follow-Up Studies
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Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients.
International journal of cardiology
May 20, 2024
BACKGROUND: Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneou...
Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.
Respiratory investigation
May 20, 2024
BACKGROUND: A machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (C...
The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images.
Cardiology journal
May 14, 2024
BACKGROUND: Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome.
Male
Middle Aged
Time Factors
Follow-Up Studies
Artificial Intelligence
Myocardium
Predictive Value of Tests
Female
Percutaneous Coronary Intervention
Reproducibility of Results
Myocardial Infarction
Aged
Acute Coronary Syndrome
Echocardiography, Stress
Humans
Neural Networks, Computer
Magnetic Resonance Imaging, Cine
Tissue Survival
Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.
ESC heart failure
May 13, 2024
AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared wit...
Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.
Journal of surgical oncology
May 7, 2024
BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on pre...
Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.
American journal of ophthalmology
May 3, 2024
PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.
Retinal Ganglion Cells
Visual Fields
Optic Nerve Diseases
Glaucoma, Open-Angle
Deep Learning
Follow-Up Studies
Tomography, Optical Coherence
Nerve Fibers
Optic Disk
Glaucoma
Algorithms
Retrospective Studies
Female
Aged
Intraocular Pressure
Male
Aging
Middle Aged
Disease Progression
Humans
Neural Networks, Computer
Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network.
Annals of surgical oncology
May 3, 2024
BACKGROUND: Rectal tumors display varying degrees of response to total neoadjuvant therapy (TNT). We evaluated the performance of a convolutional neural network (CNN) in interpreting endoscopic images of either a non-complete response to TNT or local...
Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.
Frontiers in endocrinology
Apr 30, 2024
BACKGROUND: Prognostic risk stratification in older adults with type 2 diabetes (T2D) is important for guiding decisions concerning advance care planning.
Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.
Acta ophthalmologica
Apr 29, 2024
PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and ...
Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.
World journal of surgical oncology
Apr 25, 2024
BACKGROUND: The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that ...
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