AIMC Topic: Prospective Studies
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Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.
Internal and emergency medicine
Nov 3, 2021
Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods t...
DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.
Journal of digital imaging
Nov 2, 2021
In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that...
Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia.
Scientific reports
Nov 2, 2021
Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment ...
Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.
JAMA network open
Nov 1, 2021
IMPORTANCE: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owi...
Kernel Methods for Predicting Yields of Chemical Reactions.
Journal of chemical information and modeling
Oct 26, 2021
The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in ...
Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.
Radiology
Oct 26, 2021
Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinic...
Neoplasm Staging
Survival Analysis
Neoplasms, Second Primary
Cohort Studies
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Predictive Value of Tests
Middle Aged
Risk Assessment
Deep Learning
Humans
Female
Prospective Studies
Biomarkers, Tumor
Reproducibility of Results
Prognosis
Male
Retrospective Studies
Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.
Nutrients
Oct 26, 2021
INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, RE...
DeepSense: A Physics-Guided Deep Learning Paradigm for Anomaly Detection in Soil Gas Data at Geologic CO Storage Sites.
Environmental science & technology
Oct 25, 2021
Driven by the collection of enormous amounts of streaming data from sensors, and with the emergence of the internet of things, the need for developing robust detection techniques to identify data anomalies has increased recently. The algorithms for a...
Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.
Prenatal diagnosis
Oct 18, 2021
OBJECTIVE: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools.
Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?
The British journal of radiology
Oct 18, 2021
OBJECTIVE: To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound-aided mammograms.
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