Discovering novel systemic biomarkers in photos of the external eye
- URL: http://arxiv.org/abs/2207.08998v1
- Date: Tue, 19 Jul 2022 00:28:38 GMT
- Title: Discovering novel systemic biomarkers in photos of the external eye
- Abstract summary: Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA.
evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA.
Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium8.6, eGFR60, Hgb11, platelets150, ACR>=300, and WBC4
- Score: 5.1989194363663245
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: External eye photos were recently shown to reveal signs of diabetic retinal
disease and elevated HbA1c. In this paper, we evaluate if external eye photos
contain information about additional systemic medical conditions. We developed
a deep learning system (DLS) that takes external eye photos as input and
predicts multiple systemic parameters, such as those related to the liver
(albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI
creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH);
and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images
from 49,015 patients with diabetes undergoing diabetic eye screening in 11
sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified
systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869
patients with and without diabetes undergoing eye screening in 3 independent
sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared
against baseline models incorporating available clinicodemographic variables
(e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline,
the DLS achieved statistically significant superior performance at detecting
AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on
validation set A (a patient population similar to the development sets), where
the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B
and C, with substantial patient population differences compared to the
development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by
7.3-13.2%. Our findings provide further evidence that external eye photos
contain important biomarkers of systemic health spanning multiple organ
systems. Further work is needed to investigate whether and how these biomarkers
can be translated into clinical impact.
Related papers
- Diabetic Retinopathy Lesion Segmentation through Attention Mechanisms [0.0]
Diabetic Retinopathy (DR) is an eye disease which arises due to diabetes mellitus.<n>It might cause vision loss and blindness.<n>To prevent irreversible vision loss, early detection through systematic screening is crucial.<n>Our method provides pixel-level annotations for lesions, which practically supports Ophthalmologist to screen DR from fundus images.
arXiv Detail & Related papers (2026-02-07T01:25:47Z) - Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients [65.57160385098935]
Early detection of Diabetic Retinopathy can reduce the risk of vision loss by up to 95%.<n>We developed RAIS-DR, a Responsible AI System for DR screening that incorporates ethical principles across the AI lifecycle.<n>We evaluated RAIS-DR against the FDA-approved EyeArt system on a local dataset of 1,046 patients, unseen by both systems.
arXiv Detail & Related papers (2025-08-17T21:54:11Z) - A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study [2.9992821862882546]
False-positive recalls remain a concern in breast cancer screening.
We developed a multi-modal artificial intelligence system integrating full-field digital mammography and synthetic mammography.
Our AI system, trained on approximately 500,000 exams, achieved 0.945 AUROC on an internal test set.
arXiv Detail & Related papers (2025-04-08T03:29:40Z) - AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of AIDRSS in India [0.0]
Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural areas.
This study aims to evaluate the Artificial Intelligence-based Diabetic Retinopathy Screening System for DR detection and prevalence assessment.
arXiv Detail & Related papers (2025-01-10T10:03:56Z) - Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced
Feature Extraction Processing [0.0]
This research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model for timely DR identification.
The proposed model will detect various lesions from retinal images in the early stages.
arXiv Detail & Related papers (2023-05-08T14:17:33Z) - DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical
Coherence Tomography Angiography Images [51.27125547308154]
We organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)
The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading.
This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge.
arXiv Detail & Related papers (2023-04-05T12:04:55Z) - CIRCA: comprehensible online system in support of chest X-rays-based
COVID-19 diagnosis [37.41181188499616]
Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression.
Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training.
A U-Net-based model was developed to identify a clinically relevant region of the CXR.
arXiv Detail & Related papers (2022-10-11T13:30:34Z) - Multi-Scale Convolutional Neural Network for Automated AMD
Classification using Retinal OCT Images [1.299941371793082]
Age-related macular degeneration (AMD) is the most common cause of blindness in developed countries, especially in people over 60 years of age.
Recent developments in deep learning have provided a unique opportunity for the development of fully automated diagnosis frameworks.
We propose a multi-scale convolutional neural network (CNN) capable of distinguishing pathologies using receptive fields with various sizes.
arXiv Detail & Related papers (2021-10-06T18:20:58Z) - Detecting hidden signs of diabetes in external eye photographs [3.0972826606740558]
External photographs of the front of the eye can reveal insights into diabetic retinal diseases and blood glucose control.
We developed a deep learning system (DLS) using external eye photographs of 145,832 patients with diabetes.
Our results indicate that external eye photographs contain information useful for healthcare providers managing patients with diabetes.
arXiv Detail & Related papers (2020-11-23T21:14:34Z) - Identification of Ischemic Heart Disease by using machine learning
technique based on parameters measuring Heart Rate Variability [50.591267188664666]
In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN.
The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
arXiv Detail & Related papers (2020-10-29T19:14:41Z) - M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia
Screening from CT Imaging [85.00066186644466]
We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging.
In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
arXiv Detail & Related papers (2020-10-07T06:22:24Z) - A New Screening Method for COVID-19 based on Ocular Feature Recognition
by Machine Learning Tools [66.20818586629278]
Coronavirus disease 2019 (COVID-19) has affected several million people.
New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
arXiv Detail & Related papers (2020-09-04T00:50:27Z) - A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading,
and Transferability [76.64661091980531]
People with diabetes are at risk of developing diabetic retinopathy (DR)
Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading.
This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
arXiv Detail & Related papers (2020-08-22T07:48:04Z) - Fully Automated and Standardized Segmentation of Adipose Tissue
Compartments by Deep Learning in Three-dimensional Whole-body MRI of
Epidemiological Cohort Studies [11.706960468832301]
Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
We propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation.
Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap.
arXiv Detail & Related papers (2020-08-05T17:30:14Z) - JCS: An Explainable COVID-19 Diagnosis System by Joint Classification
and Segmentation [95.57532063232198]
coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries.
To control the infection, identifying and separating the infected people is the most crucial step.
This paper develops a novel Joint Classification and (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis.
arXiv Detail & Related papers (2020-04-15T12:30:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.