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

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

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 urgent surgical repair to prevent 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 B-scans 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 B-scans from each eye). On three replicate data splits, 3D spatial contrast pre-training yields a model with an average F1-score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared to an average F1-score of 0.831 for FTMH detection by ImageNet pre-trained models. 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.

Recognition of Patient Gender: A Machine Learning Preliminary Analysis Using Heart Sounds from Children and Adolescents

Research has shown that X-rays and fundus images can classify gender, age group, and race, raising concerns about bias and fairness in medical AI applications. However, the potential for physiological sounds to classify sociodemographic traits has not been investigated. Exploring this gap is crucial for understanding the implications and ensuring fairness in the field of medical sound analysis. We aimed to develop classifiers to determine gender (men/women) based on heart sound recordings and using machine learning (ML). Data-driven ML analysis. We utilized the open-access CirCor DigiScope Phonocardiogram Dataset obtained from cardiac screening programs in Brazil. Volunteers < 21 years of age. Each participant completed a questionnaire and underwent a clinical examination, including electronic auscultation at four cardiac points: aortic (AV), mitral (MV), pulmonary (PV), and tricuspid (TV). We used Mel-frequency cepstral coefficients (MFCCs) to develop the ML classifiers. From each patient and from each auscultation sound recording, we extracted 10 MFCCs. In sensitivity analysis, we additionally extracted 20, 30, 40, and 50 MFCCs. The most effective gender classifier was developed using PV recordings (AUC ROC = 70.3%). The second best came from MV recordings (AUC ROC = 58.8%). AV and TV recordings produced classifiers with an AUC ROC of 56.4% and 56.1%, respectively. Using more MFCCs did not substantially improve the classifiers. It is possible to classify between males and females using phonocardiogram data. As health-related audio recordings become more prominent in ML applications, research is required to explore if these recordings contain signals that could distinguish sociodemographic features.

Deep neural network-estimated age using optical coherence tomography predicts mortality

The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

A Mini-review on New Developments in Nanocarriers and Polymers for Ophthalmic Drug Delivery Strategies

The eye is an important and vital organ of the human body consisting of two segments - anterior and posterior segments and these segments are associated with many diseases. This review elaborates upon the various eye-related diseases with their medications and carriers used to deliver them. Delivery strategies include drugs encapsulated into liposomes, polymeric micelles of drugs, solid lipid nanoparticles, nanostructured lipid carriers, nano emulsions, and Nanosuspension used to improve penetrating properties, bioavailability, and residence time of the drugs as examples available in the literature. With regard to this, different forms of ocular drug delivery are classified and elaborated. Additionally, the possibility of addressing the physical and chemical complexities of ocular diseases and how they could be overcome with environmentally stable nanoformulations are briefly discussed. Enhanced drug delivery efficiency with various novel pharmaceuticals along with enhanced uptake by different routes/modes of drug administration. Current advancements in drug carrier systems, i.e., nanocarriers, have shown promise for improving the retention time, drug permeation and prolonging the duration of release of the drug in the ocular site. Bio-degradable polymers investigated for the preparation of nanocarriers for the entrapment of drugs and to enhance the efficacy through improved adherence of tissue in the eye, sustained release measures, enhanced bioavailability, lower toxicity, and targeted delivery is applicable. This review covers the introduction of various nanocarriers and polymers for ocular drug delivery with the purpose of enhancing the absorption, retention and bioavailability of medications in the eye.

eXplainable Artificial Intelligence (XAI) in aging clock models

XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.

Sex-related difference in the retinal structure of young adults: a machine learning approach

Purpose

To compare the accuracy of machine learning (ML) algorithms to classify the sex of the participant from retinal thickness datasets in different retinal layers.

Methods

This cross-sectional study involved 26 male and 38 female subjects. Data were acquired using HRA + OCT Spectralis, and the thickness and volume of 10 retinal layers were quantified. A total of 10 features were extracted from each retinal layer. The accuracy of various algorithms, including k-nearest-neighbor, support vector classifier, logistic regression, linear discriminant analysis, random forest, decision tree, and Gaussian Naïve Bayes, was quantified. A two-way ANOVA was conducted to assess the ML accuracy, considering both the classifier type and the retinal layer as factors.

Results

A comparison of the accuracies achieved by various algorithms in classifying participant sex revealed superior results in datasets related to total retinal thickness and the retinal nerve fiber layer. In these instances, no significant differences in algorithm performance were observed (p > 0.05). Conversely, in other layers, a decrease in classification accuracy was noted as the layer moved outward in the retina. Here, the random forest (RF) algorithm demonstrated superior performance compared to the others (p < 0.05).

Conclusion

The current research highlights the distinctive potential of various retinal layers in sex classification. Different layers and ML algorithms yield distinct accuracies. The RF algorithm's consistent superiority suggests its effectiveness in identifying sex-related features from a range of retinal layers.

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

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.

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.

Long-term outcomes of modified glued versus flanged intrascleral haptic fixation techniques for secondary intraocular lenses

PURPOSE

To report the long-term refractive outcomes and complications of two scleral fixation techniques for secondary intraocular lenses (IOL).

METHODS

Consecutive patients who underwent secondary IOL insertion at a tertiary care academic hospital using either modified glued ("glued") or flanged intrascleral haptic fixation (FISHF) techniques with over 12 months of follow-up were retrospectively reviewed. Pre- and postoperative corrected distance visual acuity (CDVA), postoperative complications, and refractive surprises were reported.

RESULTS

Thirty-eight patients underwent "glued" fixation and 22 underwent FISHF, with mean follow-up times of 3.1 ± 0.5 and 2.0 ± 1.2 years, respectively. Aphakia secondary to trauma was the main surgical indication. MA50BM or MA60AC IOLs (Alcon Laboratories Inc., Fort Worth, TX) were implanted in 92% of "glued" patients, while CT Lucia 602 IOLs (Carl Zeiss Meditec Inc., Dublin, CA) were used in 96% of FISHF patients. Postoperative spherical equivalent significantly improved compared to preoperative values (p < 0.001). No significant difference in CDVA was seen between the two techniques. FISHF resulted in mean hyperopic surprises of + 0.81D and + 0.69D using the Holladay 2 and Barrett Universal II formulae, respectively, which was significantly greater than the "glued" patients. A higher rate of IOL dislocation was seen in the "glued" cohort (13%) compared to FISHF (0%).

CONCLUSIONS

Retrospective long-term outcomes of patients with complex ocular comorbidities undergoing a modified "glued" technique demonstrated a higher rate of IOL dislocation but more predictable refractive outcomes compared to the FISHF technique. The FISHF technique resulted in a significant hyperopic shift using fourth-generation IOL calculators.