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Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

PloS one
BACKGROUND AND OBJECTIVE: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decisi...

Implementation challenges of artificial intelligence (AI) in primary care: Perspectives of general practitioners in London UK.

PloS one
INTRODUCTION: Implementing artificial intelligence (AI) in healthcare, particularly in primary care settings, raises crucial questions about practical challenges and opportunities. This study aimed to explore the perspectives of general practitioners...

Burnout crisis in Chinese radiology: will artificial intelligence help?

European radiology
OBJECTIVES: To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.

Integrating machine learning-predicted circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in metastatic breast cancer: A proof of principle study on endocrine resistance profiling.

Cancer letters
The study explored endocrine resistance by leveraging machine learning to establish the prognostic stratification of predicted Circulating tumor cells (CTCs), assessing its integration with circulating tumor DNA (ctDNA) features and contextually eval...

A hybrid healthy diet recommender system based on machine learning techniques.

Computers in biology and medicine
Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a...

Federated Learning for Predicting Postoperative Remission of Patients with Acromegaly: A Multicentered Study.

World neurosurgery
BACKGROUND: Decentralized federated learning (DFL) may serve as a useful framework for machine learning (ML) tasks in multicentered studies, maximizing the use of clinical data without data sharing. We aim to propose the first workflow of DFL for ML ...

Skin Phototype Classification with Machine Learning Based on Broadband Optical Measurements.

Sensors (Basel, Switzerland)
The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may c...

Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.

Scientific reports
Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making the timely identification of high-risk patients for BMs in ES-SCLC crucial. Patients diagnosed with E...