AIMC Topic: Female

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A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

Surgical innovation
INTRODUCTION: Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS).

DeepGA for automatically estimating fetal gestational age through ultrasound imaging.

Artificial intelligence in medicine
Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to a...

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging.

BMC medical informatics and decision making
BACKGROUND: Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of distinguishing between benign and malignant ovarian tumors. Numerous types of ovarian tumors exist, ...

Artificial Intelligence in Health: Enhancing a Return to Patient-Centered Communication.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
The medical environment is on the verge of a dramatic transformation as artificial intelligence (AI) evolves. With the inevitable shift toward AI in health care delivery, there are concerns around its implementation, including ethics, privacy, data r...

Applying Deep Learning for Breast Cancer Detection in Radiology.

Current oncology (Toronto, Ont.)
Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how va...

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Scientific reports
Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-b...

Evaluation of word embedding models to extract and predict surgical data in breast cancer.

BMC bioinformatics
BACKGROUND: Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated De...

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.

Radiology
Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted...

Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model.

Chinese journal of integrative medicine
OBJECTIVE: To develop a multimodal deep-learning model for classifying Chinese medicine constitution, i.e., the balanced and unbalanced constitutions, based on inspection of tongue and face images, pulse waves from palpation, and health information f...