AIMC Topic:
Cross-Sectional Studies
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Comparison of concordance between chuna manual therapy diagnosis methods (palpation, X-ray, artificial intelligence program) in lumbar spine: An exploratory, cross-sectional, prospective observational study protocol.
Medicine
Dec 23, 2021
INTRODUCTION: Chuna manual therapy (CMT) is a type of manual medicine practiced by Korean medical doctors in South Korea. Spinal diagnosis in CMT uses a system that applies manual diagnostic and X-ray tests to detect specific vertebral malpositions, ...
[Analysis of the performance of a multi-view fusion and active contour constraint based deep learning algorithm for ossicles segmentation on 10 μm otology CT].
Zhonghua yi xue za zhi
Dec 21, 2021
To explore the performance of a deep learning algorithm that combined multi-view fusion with active contour constrained for ossicles segmentation on the 10 μm otology CT images. The 10 μm otology CT image data from 79 cases (56 cases were from volu...
Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field.
Translational vision science & technology
Nov 1, 2021
PURPOSE: To investigate whether a correction based on a Humphrey field analyzer (HFA) 24-2/30-2 visual field (VF) can improve the prediction performance of a deep learning model to predict the HFA 10-2 VF test from macular optical coherence tomograph...
In-Person Verification of Deep Learning Algorithm for Diabetic Retinopathy Screening Using Different Techniques Across Fundus Image Devices.
Translational vision science & technology
Nov 1, 2021
PURPOSE: To evaluate the clinical performance of an automated diabetic retinopathy (DR) screening model to detect referable cases at Siriraj Hospital, Bangkok, Thailand.
Feasibility and Accuracy of Artificial Intelligence-Assisted Sponge Cytology for Community-Based Esophageal Squamous Cell Carcinoma Screening in China.
The American journal of gastroenterology
Nov 1, 2021
INTRODUCTION: Screening is the pivotal strategy to relieve the burden of esophageal squamous cell carcinoma (ESCC) in high-risk areas. The cost, invasiveness, and accessibility of esophagogastroduodenoscopy (EGD) necessitate the development of prelim...
Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation.
JAMA cardiology
Nov 1, 2021
IMPORTANCE: Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning-based automated analysis against clinically accepted standards of care are lacking.
Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.
The Journal of nutrition
Oct 23, 2021
BACKGROUND: The prevalence of type 2 diabetes has increased substantially in India over the past 3 decades. Undiagnosed diabetes presents a public health challenge, especially in rural areas, where access to laboratory testing for diagnosis may not b...
Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life.
BMJ health & care informatics
Oct 1, 2021
OBJECTIVES: To clarify real-world linguistic nuances around dying in hospital as well as inaccuracy in individual-level prognostication to support advance care planning and personalised discussions on limitation of life sustaining treatment (LST).
Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images.
Journal of glaucoma
Sep 1, 2021
PURPOSE: (1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence tomography (SS-OCT) images. (2) To assess the performance of DL-based fusion methods in diagnosing gla...
Quantitative Assessment of Fundus Tessellated Density and Associated Factors in Fundus Images Using Artificial Intelligence.
Translational vision science & technology
Aug 2, 2021
PURPOSE: This study aimed to quantitative assess the fundus tessellated density (FTD) and associated factors on the basis of fundus photographs using artificial intelligence.
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