AIMC Topic: Female

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Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application.

Critical reviews in oncology/hematology
INTRODUCTION: Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors und...

A Learning Paradigm for Selecting Few Discriminative Stimuli in Eye-Tracking Research.

IEEE transactions on pattern analysis and machine intelligence
Eye-tracking is a reliable method for quantifying visual information processing and holds significant potential for group recognition, such as identifying autism spectrum disorder (ASD). However, eye-tracking research typically faces the heterogeneit...

Automated detection of severe cerebral edema using explainable deep transfer learning after hypoxic ischemic brain injury.

Resuscitation
BACKGROUND: Substantial gaps exist in the neuroprognostication of cardiac arrest patients who remain comatose after the restoration of spontaneous circulation. Most studies focus on predicting survival, a measure confounded by the withdrawal of life-...

Deep Learning and Radiomic Signatures Associated with Tumor Immune Heterogeneity Predict Microvascular Invasion in Colon Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to develop and validate a deep learning radiomics signature (DLRS) that integrates radiomics and deep learning features for the non-invasive prediction of microvascular invasion (MVI) in patients with colon c...

A heterogeneity analysis of health-related quality of life in early adults born very preterm or very low birthweight across the sociodemographic spectrum.

Social science & medicine (1982)
Preterm birth and very low birthweight (VP/VLBW) are associated with poorer health-related quality of life (HRQoL) outcomes extending into adulthood, yet it remains unclear how these effects differ across sociodemographic subgroups. This study aimed ...

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic informa...

Right Ventricular Strain as a Key Feature in Interpretable Machine Learning for Identification of Takotsubo Syndrome: A Multicenter CMR-based Study.

Academic radiology
RATIONALE AND OBJECTIVES: To develop an interpretable machine learning (ML) model based on cardiac magnetic resonance (CMR) multimodal parameters and clinical data to discriminate Takotsubo syndrome (TTS), acute myocardial infarction (AMI), and acute...

The effect of selection bias on the performance of a deep learning-based intraoperative hypotension prediction model using real-world samples from a publicly available database.

British journal of anaesthesia
BACKGROUND: There are models to predict intraoperative hypotension from arterial pressure waveforms. Selection bias in datasets used for model development and validation could impact model performance. We aimed to evaluate how selection bias affects ...

[Is the application of digital technologies the game changer for surgical training of the future? A Germany-wide analysis].

Chirurgie (Heidelberg, Germany)
BACKGROUND: The use of digital technologies is becoming increasingly important in medicine and is having a significant impact on developments in the surgical field. However, there is a great need to improve and implement those new techniques in surgi...