AIMC Topic: Humans

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Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study.

European radiology
OBJECTIVES: To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time.

FT-GAT: Graph neural network for predicting spontaneous breathing trial success in patients with mechanical ventilation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Intensive care unit (ICU) physicians perform weaning procedures considering complex clinical situations and weaning protocols; however, liberating critical patients from mechanical ventilation (MV) remains challenging. Ther...

Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery.

The journal of trauma and acute care surgery
BACKGROUND: Artificial intelligence (AI) risk prediction algorithms such as the smartphone-available Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) for emergency general surgery (EGS) are superior to traditional risk calculators because ...

A New Era in Cardiometabolic Management: Unlocking the Potential of Artificial Intelligence for Improved Patient Outcomes.

Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists

Jejunum Patch Technique During Robot-Assisted Central Pancreatectomy: A Lesson from Open Procedure Experience.

Annals of surgical oncology
BACKGROUND: Central pancreatectomy (CP) has been established as the most common type of parenchyma-sparing pancreatectomy; however, CP is associated with higher morbidity and a higher pancreatic fistula (PF) rate than distal pancreatectomy or pancrea...

Machine learning in computational histopathology: Challenges and opportunities.

Genes, chromosomes & cancer
Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. ...

A novel collaborative self-supervised learning method for radiomic data.

NeuroImage
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this...