AIMC Topic: Machine Learning

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[A machine learning model on orthokeratology lens fitting based on the data of optometry examination].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
To get an orthokeratology lens fitting model according to the research of the optometry examination data, which can help to improve the work efficiency and increase the hitting rate of prescription. The relationship between the basic optometry exam...

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography.

Applied optics
In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. I...

Low coherence quantitative phase microscopy with machine learning model and Raman spectroscopy for the study of breast cancer cells and their classification.

Applied optics
Early-stage detection of breast cancer is the primary requirement in modern healthcare as it is the most common cancer among women worldwide. Histopathology is the most widely preferred method for the diagnosis of breast cancer, but it requires long ...

Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human-machine systems.

Proceedings of the National Academy of Sciences of the United States of America
Citizen science has proved to be a unique and effective tool in helping science and society cope with the ever-growing data rates and volumes that characterize the modern research landscape. It also serves a critical role in engaging the public with ...

Deep Learning for Prediction of AMD Progression: A Pilot Study.

Investigative ophthalmology & visual science
PURPOSE: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network.

Korean journal of radiology
OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram.

Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

Yonsei medical journal
PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predic...