AIMC Topic: Adult

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Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a Population-based Screening Sample.

Journal of breast imaging
OBJECTIVE: To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample.

The Application Value of an Artificial Intelligence-Driven Intestinal Image Recognition Model to Evaluate Intestinal Preparation before Colonoscopy.

British journal of hospital medicine (London, England : 2005)
Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, a...

Comparing Artificial Intelligence Guided Image Assessment to Current Methods of Burn Assessment.

Journal of burn care & research : official publication of the American Burn Association
Appropriate identification of burn depth and size is paramount. Despite the development of burn depth assessment aids [eg, laser Doppler imaging (LDI)], clinical assessment, which assesses partial-thickness burn depth with 67% accuracy, currently rem...

Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome.

The Journal of clinical endocrinology and metabolism
CONTEXT: Artificial intelligence research in the field of neuroendocrinology has accelerated. It is possible to develop noninvasive, easy-to-use and cost-effective procedures that can replace invasive procedures for the differential diagnosis of adre...

Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Biomedical and environmental sciences : BES
OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.

Machine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China.

The oncologist
BACKGROUND: Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the...

Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predic...

Leveraging artificial intelligence-mediated communication for cancer prevention and control and drug addiction: A systematic review.

Translational behavioral medicine
OBJECTIVE: To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.

Artificial Intelligence Analysis of Periorbital Rejuvenation.

Aesthetic surgery journal
BACKGROUND: Periorbital rejuvenation surgery aims to restore a youthful appearance to the face. Despite the popularity of these procedures, few objective measurements exist to evaluate their impact on perceived facial aging.

What radio waves tell us about sleep!

Sleep
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow...