AIMC Topic: Female

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Body composition predicts hypertension using machine learning methods: a cohort study.

Scientific reports
We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body ...

Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy.

American journal of obstetrics and gynecology
OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.

Fetal brain activity and the free energy principle.

Journal of perinatal medicine
OBJECTIVES: To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence.

The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.

Joint diseases and related surgery
OBJECTIVES: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs.

Growing pains: strategies for improving ergonomics in minimally invasive gynecologic surgery.

Current opinion in obstetrics & gynecology
PURPOSE OF REVIEW: To evaluate factors contributing to the development of work-related musculoskeletal disorders (WMSDs) and review strategies for mitigating ergonomic strain in minimally invasive gynecologic surgery.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning.

Biochimica et biophysica acta. Molecular basis of disease
Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we coll...

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

The Journal of physiology
Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods s...

Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: Algorithm design, validation, and implementation in 4 Spanish hospitals.

American journal of infection control
BACKGROUND: Surgical site infection (SSI) surveillance is a labor-intensive endeavor. We present the design and validation of an algorithm for SSI detection after hip replacement surgery, and a report of its successful implementation in 4 public hosp...

Effect of radiotherapy on phagocytosis percentage and index in patients with oral squamous cell carcinoma.

Journal of cancer research and therapeutics
BACKGROUND: Phagocytosis plays an important role in the fundamental process of immunity and maintains systemic tissue homeostasis. Phagocytosis function is assessed in radiotherapy to signify the prognosis of patient. Therefore, we designed a study t...

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of ro...