AIMC Topic: Humans

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Deep learning in cancer diagnosis, prognosis and treatment selection.

Genome medicine
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across health...

An Automatic Knee Osteoarthritis Diagnosis Method Based on Deep Learning: Data from the Osteoarthritis Initiative.

Journal of healthcare engineering
Osteoarthritis (OA) is the most common form of arthritis. According to the evidence presented on both sides of the knee bones, radiologists assess the severity of OA based on the Kellgren-Lawrence (KL) grading system. Recently, computer-aided methods...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

PloS one
Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized f...

Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Messages from an Internet forum are raw material that emerges in a natural setting (i.e., non-induced by a research situation).

Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that ta...

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.

BMC cancer
BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal can...

Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting.

The Annals of thoracic surgery
BACKGROUND: Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative ti...

Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning.

American journal of ophthalmology
PURPOSE: To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age.

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between e...

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.

Journal of medical virology
COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from Janu...