AIMC Topic: Humans

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The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study.

Archives of gynecology and obstetrics
PURPOSE: Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance...

Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive cap...

The Emerging Influence of Artificial Intelligence on Traditional Medical Textbooks.

The Annals of thoracic surgery
This review provides a comprehensive exploration of how artificial intelligence (AI) is reshaping medical education and the role of traditional textbooks. The historical context underscores the evolution of medical knowledge bridging past advances wi...

Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.

Endoscopy
BACKGROUND:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the div...

BCT-Net: semantic-guided breast cancer segmentation on BUS.

Medical & biological engineering & computing
Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely employed methods in clinical practice. However, due to challenges such as low contrast, blurred boundari...

Graph convolution network-based eeg signal analysis: a review.

Medical & biological engineering & computing
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The appl...

Enhancing surgical precision in squamous cell carcinoma of the head and neck: Hyperspectral imaging and artificial intelligence for improved margin assessment in an ex vivo setting.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
BACKGROUND: Head and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete resection (R0) is crucial, but current methods, relying on white light endoscopy and microscopy, have ...

The Liver Intensive Care Unit.

Clinics in liver disease
Major advances in managing critically ill patients with liver disease have improved their prognosis and access to intensive care facilities. Acute-on-chronic liver failure (ACLF) is now a well-defined disease and these patients can be fast-tracked fo...

Machine learning to detect recent recreational drug use in intensive cardiac care units.

Archives of cardiovascular diseases
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...

Sample-efficient and occlusion-robust reinforcement learning for robotic manipulation via multimodal fusion dualization and representation normalization.

Neural networks : the official journal of the International Neural Network Society
Recent advances in visual reinforcement learning (visual RL), which learns from high-dimensional image observations, have narrowed the gap between state-based and image-based training. However, visual RL continues to face significant challenges in ro...