Deep Learning Prediction Of Age And Sex From Optical Coherence Tomography

Hassan, Osama N.; Menten, Martin J.; Bogunović, Hrvoje; Schmidt‐Erfurth, Ursula; Lotery, Andrew; Rueckert, Daniel

doi:10.1109/isbi48211.2021.9434107

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…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. Second, notwithstanding the report by Hassan et al 30 that accurate prediction for age and acceptable performance for sex had been obtained using DL, their results also were highly variable by patient. Third, Munk et al 31 concluded that age and sex could be classified from OCT using DL-based methods for a broad spectrum of patients irrespective of underlying disease or image quality.…”

Section: Discussionmentioning

confidence: 90%

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: 90%

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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“…Using OCT images centered on the optic nerve head and fovea, the MAE of DLbased age prediction ranged between 3.3-6 years, [62][63][64][65] with the best result reported by Hassan et al [65]. Notably, in the study by Shigueoka et al, the CNN model revealed different correlations between the different retinal layers and age, [62] but this finding was not replicated in the study by Chueh et al [64].…”

Section: Age and Sexmentioning

confidence: 98%

Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review

Liu

2022

JCM

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The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images—most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, “super-human” predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes.

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“…BagNet has been used for a variety of medical image processing tasks in various modalities. It was deployed for sex and age prediction to generate heat maps for brain Magnetic Resonance Imaging (MRI) volumes 12 and retinal images 13,14 . In other work, BagNet was extended with a MIL branch and trained to generate interpretable heat maps for histology images describing malignant and benign 15 regions.…”

Section: Related Workmentioning

confidence: 99%

Estimating lung function from computed tomography at the patient and lobe level using machine learning

Boulogne,

Charbonnier,

Jacobs

et al. 2024

Medical Physics

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BackgroundAutomated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.PurposeTo automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function.MethodsWe propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe‐level and a patient‐level model. The lobe‐level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient‐level model directly estimates patient level lung function from a CT scan and is used to re‐scale the output of the lobe‐level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8 433 CT volumes for training, 1 775 CT volumes for validation, and 1 873 CT volumes for testing.ResultsFirst, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe‐level quantities, such as COVID‐19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient‐level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono‐oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377 L, 0.297 L, and 2.800 mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand‐challenge.org/algorithms/lobe‐wise‐lung‐function‐estimation/ConclusionsI3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.

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Deep Learning Prediction Of Age And Sex From Optical Coherence Tomography

Cited by 8 publications

References 17 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

Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review

Estimating lung function from computed tomography at the patient and lobe level using machine learning

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