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Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Scientific reports
Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS images with breast cancer bone metastasis have the characteristics of low resolution, small foregrou...

Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.

Scientific reports
Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a vital functional structure in the uterus. The importance of timely recognition cannot be overstated...

Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study.

Nature communications
Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-base...

Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.

Nature communications
Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a s...

A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.

Scientific reports
Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or female reproductive system, presents a global challenge due to its impact on one in six individuals worldwide. Given the high prevalence of infertility ...

Proposing a machine learning-based model for predicting nonreassuring fetal heart.

Scientific reports
The capacity to forecast nonreassuring fetal heart (NFH) is essential for minimizing perinatal complications; therefore, this research aims to establish if a machine learning (ML) model can predict NFH. This was a retrospective analysis of informatio...

Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.

Scientific reports
In recent times, severe acute malnutrition (SAM) in India is considered a serious issue as per UNICEF 2022 records. In that record, 35.5% of children under age 5 are stunted, 19.3% are wasted, and 32% are underweight. Malnutrition, defined as these t...

Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial.

JMIR human factors
BACKGROUND: Artificial intelligence (AI) chatbots have been customized to deliver on-demand support for people with mental health problems. However, the effectiveness of AI chatbots in tackling mental health problems among the general public in Hong ...

On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network.

IEEE journal of biomedical and health informatics
The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neural networks, taking advantage of EEG's temporal resolution and the computational capabilities of embe...

Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning.

IEEE journal of biomedical and health informatics
Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To addres...