Medical Xpress

Algorithms based on deep learning can improve medical image analysis

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the ...
Medical Device and Diagnostic Industry (MD+DI)

The AI Era: How Deep Learning is Overcoming Data Scarcity in Medical Imaging

AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...
Medical Xpress

Deep-learning algorithms enhance mutation detection in cancer and RNA sequencing

Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic ...
Nature

An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things

The Internet of Things (IoT) plays a significant part in the healthcare field. The growth of smart devices, smart sensors, and advanced lightweight communication protocols has created an opportunity ...
Nature

Development and validation of a deep reinforcement learning algorithm for auto-delineation of organs at risk in cervical cancer radiotherapy

Cervical cancer is the fourth most common cancer among female persons worldwide and the second most common cancer in female persons aged 15–44 years 1,2,3. Radiotherapy is used as primary or adjuvant ...
News Medical

Deep learning algorithm uses mammograms and age for heart disease prediction

A new machine learning model developed by The George Institute for Global Health can successfully predict heart disease risk in women by analyzing mammograms. The findings were published today in ...
University of Geneva

Precision medicine’s inevitable trajectory toward rare-disease-sized cohorts: implications for machine learning and deep learning

As patients are divided into ever more narrowly defined subgroups, the number of individuals available for research shrinks dramatically. While this approach improves personalization, it also creates ...