AIMC Topic: Female

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Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Radiology. Artificial intelligence
Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was devel...

External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.

Radiology. Artificial intelligence
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radio...

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

Radiology. Artificial intelligence
Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. M...

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

Radiology. Artificial intelligence
The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for acute intracranial hemorrhage (ICH) detection was assessed in a large teleradiology practice. The impact on radiologist read times and system efficie...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

The British journal of radiology
OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT).

Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

Radiology. Artificial intelligence
Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encepha...

Optimize individualized energy delivery for septic patients using predictive deep learning models.

Asia Pacific journal of clinical nutrition
BACKGROUND AND OBJECTIVES: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients.

Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?

Human reproduction (Oxford, England)
Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing par...