AIMC Topic: Female

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A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally fo...

Emerging uses of artificial intelligence in breast and axillary ultrasound.

Clinical imaging
Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also plays an important role in staging breast cancer by assessing axillary lymph nodes. However, its utilit...

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study.

Gut
OBJECTIVE: To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.

Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.

Journal of perinatal medicine
OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenat...

An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.

Scientific data
Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potent...

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI).

Physics in medicine and biology
Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is no...

Predicting preterm births from electrohysterogram recordings via deep learning.

PloS one
About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm bi...

The Minimally Invasive Inguinal Hernia: Current Trends and Considerations.

The Surgical clinics of North America
Inguinal hernias are one of the most common surgical pathologies faced by the general surgeon in modern medicine. The cumulative incidence of an inguinal hernia is around 25% in men and 3% in women. The majority of inguinal hernias can be repaired mi...

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach.

European radiology
OBJECTIVE: Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and qua...

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.

Hypertension research : official journal of the Japanese Society of Hypertension
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (...