AIMC Topic: Retrospective Studies
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Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.
Clinical imaging
Nov 12, 2024
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasi...
A F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.
Cancer imaging : the official publication of the International Cancer Imaging Society
Nov 12, 2024
BACKGROUND: To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from F-fluorodeoxyglucose (F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph n...
Factors and machine learning models for predicting successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury: a retrospective cohort study based on MIMIC-IV database.
BMC nephrology
Nov 12, 2024
BACKGROUND: For critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the discontinuation of continuous renal replacement therapy (CRRT). This study aims to explore factors associ...
[Incidence and determinants of viral load rebound in people receiving multi-month dispensing of antiretroviral therapy at the Regional Annex Hospital of Dschang from 2018-2023].
The Pan African medical journal
Nov 12, 2024
INTRODUCTION: in Cameroon, multi-month dispensing (MMD) of antiretrovirals (ARVs) was introduced to improve treatment adherence among people living with HIV (PLHIV). However, this strategy has limitations that may lead to viral load rebound. The purp...
The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma.
Frontiers in immunology
Nov 12, 2024
BACKGROUND: Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced...
Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world.
Acta diabetologica
Nov 11, 2024
AIMS: Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk ...
High-precision MRI of liver and hepatic lesions on gadoxetic acid-enhanced hepatobiliary phase using a deep learning technique.
Japanese journal of radiology
Nov 11, 2024
PURPOSE: The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the ...
Development and Validation of a Machine Learning-Based Nomogram for Prediction of Unplanned Reoperation Postspinal Surgery Within 30 Days.
World neurosurgery
Nov 11, 2024
BACKGROUND: Unplanned reoperation postspinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction model...
Prediction of dialysis adequacy using data-driven machine learning algorithms.
Renal failure
Nov 11, 2024
BACKGROUND: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples preve...
Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.
BMC medical imaging
Nov 11, 2024
BACKGROUND: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI).
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