Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning

Chueh, Kuan-Ming; Hsieh, Yi‐Ting; Chen, Homer H.; Ma, I-Hsin; Huang, Sheng-Lung

doi:10.1016/j.ajo.2021.09.015

Cited by 18 publications

(12 citation statements)

References 27 publications

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“…We found that our DL model was able to predict age accurately from AS-OCT images, whereas it performed poorly for sex, height, weight, and BMI. Although several earlier studies using OCT images reported that a DL model accurately predicted demographic characteristics of age or sex, 29 – 32 there are several limitations that should be noted ( Supplementary Table ). First, whereas Shigueoka et al 29 reported that a DL algorithm was able to accurately predict age from whole peripapillary OCT, with high correlation between predicted and true chronological ages, their patient-to-patient results were highly variable.…”

Section: Discussionmentioning

confidence: 97%

“…For age prediction, however, they used a combination of methods whereby the network outputs age bins that are normalized using a softmax activation w and multiplied by the lower edge of the bins, d x , which could cause overfitting and result in a DL model vulnerable to domain shift. Finally, although Chueh et al 32 reported that age and sex could be identified from macular OCT with good accuracy using DL, they did not separate a test dataset when applying 10-fold cross-validation, which could have exaggerated the performance of the DL model. Moreover, all of these studies utilized OCT images in their analyses, which limits the direct comparability of their results with our AS-OCT–based ones.…”

Section: Discussionmentioning

confidence: 98%

“…Recent studies using deep learning (DL) also have reported that the DL algorithm employed successfully predicted demographic characteristics including age and sex from OCT images ( Supplementary Table ). 29 – 32 In addition, Vision Transformer (ViT), which is an extended application of Transformer to computer vision, has shown its usefulness in ophthalmology, as well. 33 , 34 …”

Section: Introductionmentioning

confidence: 99%

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Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study

Lee

Choe

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose To predict demographic characteristics from anterior segment optical coherence tomography (AS-OCT) images of eyes using a Vision Transformer (ViT) model. Methods A total of 2970 AS-OCT images were used to train, validate, and test a ViT to predict age and sex, and 2616 images were used for height, weight, and body mass index (BMI). The main outcome measure was the area under the receiver operating characteristic curve (AUC) of the ViT. Results The ViT achieved the largest AUC (0.910) for differentiating age ≤75 versus >75 years, followed by age ≤60 versus 60–75 versus >75 years (AUC, 0.844), and for discriminating sex (AUC, 0.665). The prediction abilities for the other demographic characteristics were lower: an AUC of 0.521 for classifying height ≤170 versus >170 cm in males and ≤155 versus >155 cm in females; 0.522 for weight <70 versus ≥70 kg in males and 0.503 for <55 versus ≥55 kg in females, and 0.517 for BMI <23 versus 23–25 versus ≥25 kg/m 2 . Heatmaps highlighted the area of the iridocorneal angle for its contribution to the prediction of age ≤75 versus >75 years. Conclusions Although the ViT demonstrated a good ability to classify age from AS-OCT images, it performed poorly for sex, height, weight, and BMI. The heatmap obtained of the prediction will provide clues to understanding the age-related anterior segment changes in eyes. Translational Relevance The ViT can determine age-related anterior segment structural changes using AS-OCT images, which will aid clinicians in the management of ocular diseases.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study

Lee

Choe

et al. 2022

Trans. Vis. Sci. Tech.

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“…Deep learning approaches have been shown to be able to detect imaging patterns that are not amenable to human identification and which can assist with prediction tasks (Radhakrishnan 2023). For example, neural networks can predict sex and age with good accuracy from retinal OCT images (Chueh 2022; Le Goallec 2022) whereas human experts find these tasks impossible. Here, we investigated if autoencoders can identify OCT parameters that can be used to predict health outcomes (glaucoma and cardiovascular disease).…”

Section: Discussionmentioning

confidence: 99%

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers

Sergouniotis

Diakité

Gaurav

et al. 2023

Preprint

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Genome-wide association studies (GWAS) have been remarkably successful in identifying associations between genetic variation and imaging-derived phenotypes. To date, the main focus of these analyses has been established, clinically-used imaging features. Here, we sought to investigate if deep learning approaches can help detect more nuanced patterns of image variability. To this end, we used an autoencoder to represent retinal optical coherence tomography (OCT) images from 31,135 UK Biobank participants. For each study subject, we obtained a 64-dimensional vector representing features of retinal structure. GWAS of these autoencoder-derived imaging parameters identified 113 genome-wide-significant loci. These encompassed variants previously linked with retinal thickness measurements, ophthalmic disorders and/or neurodegenerative conditions (including dementia). Notably, the generated retinal phenotypes were found to contribute to predictive models for glaucoma and cardiovascular disorders. Overall, we demonstrate that self-supervised phenotyping of OCT images enhances the discoverability of genetic factors influencing retinal morphology and provides epidemiologically informative biomarkers.

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“…The last several years have seen an explosion of deep learning models applied to ophthalmic clinical technologies including OCT and fundus imaging. These applications may be divided into the broad areas of classification/diagnosis [8–18], segmentation [19–26], image quality [27], and demographics prediction [28]. The current ophthalmic deep learning models focus primarily on diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, and glaucoma [29–31].…”

Section: Introductionmentioning

confidence: 99%

Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans

Wheeler,

Hunter,

Garcia

et al. 2023

Preprint

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There is a growing interest in using computer-assisted models for the detection of macular conditions using optical coherence tomography (OCT) data. As the quantity of clinical scan data of specific conditions is limited, these models are typically developed by fine-tuning a generalized network to classify specific macular conditions of interest. Full thickness macular holes (FTMH) present a condition requiring timely surgical intervention to prevent permanent vision loss. Other works on automated FTMH classification have tended to use supervised ImageNet pre-trained networks with good results but leave room for improvement. In this paper, we develop a model for FTMH classification using OCT slices around the central foveal region to pre-train a naïve network using contrastive self-supervised learning. We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, 51 FTMH+ eyes, 3 slices from each eye). 3D spatial contrast pre-training yields a model with an F1-score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared ImageNet pre-trained models, respectively. These results demonstrate that even limited data may be applied toward self-supervised pre-training to substantially improve performance for FTMH classification, indicating applicability toward other OCT-based problems.Author SummaryFull thickness macular holes (FTMH) are a sight-threatening condition that involves the fovea, the area of the eye involved in central vision. Timely diagnosis is paramount because of the risk of permanent vision loss. In clinical practice, full thickness macular holes are commonly diagnosed with the aid of optical coherence tomography (OCT) images of the fovea. However, certain conditions such as pseudoholes and epiretinal membranes may complicate the diagnosis of full thickness macular holes on imaging. Here, we employ the use of artificial intelligence and present a machine-learning model for full thickness macular hole classification and distinction from conditions that may present similarly upon image review. Despite training our model with a smaller data set, it outperformed traditional models previously seen in other works. We provide a strong framework for a self-supervised pre-trained model that can accurately distinguish full thickness macular holes from epiretinal membranes and pseudoholes. Overall, our study provides evidence of the benefit and efficacy with the introduction of artificial intelligence for image classification.

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Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning

Cited by 18 publications

References 27 publications

Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study

Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers

Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans

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