AIMC Topic: Female

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Dose-response association between OGTT and adverse perinatal outcomes after IVF treatment: A cohort study based on a twin population.

Journal of endocrinological investigation
BACKGROUND: Investigate the association between Oral Glucose Tolerance Test (OGTT) after in vitro fertilization (IVF) treatment and adverse maternal and neonatal outcomes in twin pregnancies.

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

Scientific reports
We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. This retrospective cohort study included neonates born before 32 weeks of gestation at Seoul National...

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Scientific reports
As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of ...

Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder.

Scientific reports
Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims to explore the pathogenic pathway of MDD and its relationship with immune responses and to discover i...

Explainable machine learning for predicting lung metastasis of colorectal cancer.

Scientific reports
Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning...

Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images.

Scientific reports
The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient's survival. Mammography has recently been recommended as diag...

Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.

Scientific reports
This study aimed to explore potential risk factors for severe endometriosis and to develop a model to predict the risk of severe endometriosis. A total of 308 patients with endometriosis were analyzed. Least absolute shrinkage and selection operator ...

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Scientific reports
Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their s...

Machine learning-based multiparametric CT radiomics for predicting microvascular invasion before nephrectomy in clear cell renal cell carcinoma.

Abdominal radiology (New York)
PURPOSE: This study aimed to investigate the value of integrating computed tomography (CT)-based tumor radiomics features with clinical parameters for preoperative prediction of microvascular invasion (MVI) in clear cell renal cell carcinoma (ccRCC).

Deep learning reconstruction of diffusion-weighted imaging with single-shot echo-planar imaging in endometrial cancer: a comparison with multi-shot echo-planar imaging.

Abdominal radiology (New York)
PURPOSE: To evaluate the efficacy of deep learning reconstruction (DLR) in diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (SSEPI) for endometrial cancer, compared to multiplexed sensitivity-encoding (MUSE) DWI.