AIMC Topic: Female

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Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study.

Journal of translational medicine
BACKGROUND: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve...

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

Scientific reports
Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in b...

Prediction of the response to photodynamic therapy in patients with chronic central serous chorioretinopathy based on optical coherence tomography using deep learning.

Photodiagnosis and photodynamic therapy
PURPOSE: To assess the prediction of the response to photodynamic therapy (PDT) in chronic central serous chorioretinopathy (CSCR) based on spectral-domain optical coherence tomography (SD-OCT) images using deep learning (DL).

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not strai...

Development and validation of a machine-learning algorithm to predict the relevance of scientific articles within the field of teratology.

Reproductive toxicology (Elmsford, N.Y.)
The Dutch Teratology Information Service Lareb counsels healthcare professionals and patients about medication use during pregnancy and lactation. To keep the evidence up to date, employees perform a standardized weekly PubMed query where relevant li...

Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioec...

The Impact of Engagement with the PARO Therapeutic Robot on the Psychological Benefits of Older Adults with Dementia.

Clinical gerontologist
OBJECTIVES: This study aimed to examine the effect of 8-weeks of a 60-minute PARO intervention to reduce depressive symptoms and loneliness in older adults with dementia and investigated changes in their emotional or behavioral expressions and level ...

Acute colonic flexures: the basis for developing an artificial intelligence-based tool for predicting the course of colonoscopy.

Anatomical science international
Tortuosity of the colon is an important parameter for predicting the course of colonoscopy. Computed tomography scans of the abdominal cavity were performed in 224 (94 female, 130 male) adult subjects. The number of acute (angle not exceeding 90°) be...

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.

Sensors (Basel, Switzerland)
Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data col...

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

AJR. American journal of roentgenology
CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. The aim of this study was ...