AIMC Topic: Female

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Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study.

Scientific reports
Autologous reconstruction using abdominal flaps remains the most popular method for breast reconstruction worldwide. We aimed to evaluate a prediction model using machine-learning methods and to determine which factors increase abdominal flap donor s...

Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsuperv...

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

BMC cancer
BACKGROUND: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates ma...

Assessment of breast positioning criteria in mammographic screening: Agreement between artificial intelligence software and radiographers.

Journal of medical screening
OBJECTIVES: To determine the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and digital breast tomosynthesis.

Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.

Radiology
Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for ...

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease.

International journal of molecular sciences
BACKGROUND: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) lev...

A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

BMC medical imaging
OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.

Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.

BMC cardiovascular disorders
BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandem...