AIMC Topic: Humans

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A deep learning approach to identify the fetal head position using transperineal ultrasound during labor.

European journal of obstetrics, gynecology, and reproductive biology
OBJECTIVES: To develop a deep learning (DL)-model using convolutional neural networks (CNN) to automatically identify the fetal head position at transperineal ultrasound in the second stage of labor.

Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.

Breast (Edinburgh, Scotland)
PURPOSE: In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise meth...

Machine learning approaches to predict whether MEPs can be elicited via TMS.

Journal of neuroscience methods
BACKGROUND: Transcranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, which after transmission along cortico-muscular pat...

Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans.

La Radiologia medica
PURPOSE: To evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI).

Artificial intelligence in cardiovascular imaging and intervention.

Herz
Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular ...

[Automatic ICD-10 coding : Natural language processing for German MRI reports].

Radiologie (Heidelberg, Germany)
BACKGROUND: The medical coding of radiology reports is essential for a good quality of care and correct billing, but at the same time a complex and error-prone task.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

Journal of applied clinical medical physics
BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed fo...

A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.

Cancer science
A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologis...

VacSol-ML(ESKAPE) Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

Vaccine
The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensiv...