Cargando…
Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models
While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including dee...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322827/ https://www.ncbi.nlm.nih.gov/pubmed/35885619 http://dx.doi.org/10.3390/diagnostics12071714 |
_version_ | 1784756400253042688 |
---|---|
author | Khan, Nergis C. Perera, Chandrashan Dow, Eliot R. Chen, Karen M. Mahajan, Vinit B. Mruthyunjaya, Prithvi Do, Diana V. Leng, Theodore Myung, David |
author_facet | Khan, Nergis C. Perera, Chandrashan Dow, Eliot R. Chen, Karen M. Mahajan, Vinit B. Mruthyunjaya, Prithvi Do, Diana V. Leng, Theodore Myung, David |
author_sort | Khan, Nergis C. |
collection | PubMed |
description | While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy. |
format | Online Article Text |
id | pubmed-9322827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93228272022-07-27 Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models Khan, Nergis C. Perera, Chandrashan Dow, Eliot R. Chen, Karen M. Mahajan, Vinit B. Mruthyunjaya, Prithvi Do, Diana V. Leng, Theodore Myung, David Diagnostics (Basel) Article While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy. MDPI 2022-07-14 /pmc/articles/PMC9322827/ /pubmed/35885619 http://dx.doi.org/10.3390/diagnostics12071714 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Khan, Nergis C. Perera, Chandrashan Dow, Eliot R. Chen, Karen M. Mahajan, Vinit B. Mruthyunjaya, Prithvi Do, Diana V. Leng, Theodore Myung, David Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models |
title | Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models |
title_full | Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models |
title_fullStr | Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models |
title_full_unstemmed | Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models |
title_short | Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models |
title_sort | predicting systemic health features from retinal fundus images using transfer-learning-based artificial intelligence models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322827/ https://www.ncbi.nlm.nih.gov/pubmed/35885619 http://dx.doi.org/10.3390/diagnostics12071714 |
work_keys_str_mv | AT khannergisc predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT pererachandrashan predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT doweliotr predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT chenkarenm predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT mahajanvinitb predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT mruthyunjayaprithvi predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT dodianav predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT lengtheodore predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels AT myungdavid predictingsystemichealthfeaturesfromretinalfundusimagesusingtransferlearningbasedartificialintelligencemodels |