Classification Algorithms Used in Predicting Glaucoma Progression

Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans

PURPOSE

This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.

METHODS

The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups. The best neural network model was optimized for better outcomes. Prediction accuracy, the area under the curve, sensitivity, specificity, precision, and F-1 score values were obtained.

RESULTS

The Wilcoxon Signed-Rank test provided significantly higher validation accuracy for neural networks than decision trees, discriminant analysis, support vector machines, and k-nearest neighbor classifiers (p = 0.005 for all). For C57BL/6-DBA/2J classification, a mean validation accuracy of 88.11 ± 3.92% (95% CI: 86.99-89.22) was achieved for the neural network when the optimized neural network had 92.31% final test accuracy with an area under the curve value of 0.9762, 94.44% sensitivity, 90.48% specificity, 89.47% precision, and 0.92 F-1 score. The optimized neural network model for age group differentiation had a final test accuracy of 82.05% with a 0.9064 area under the curve value, 77.27% sensitivity, 88.24% specificity, 89.47% precision, and 0.83 F-1 score.

CONCLUSIONS

These findings validate that machine learning, using segmentation metrics instead of images, can effectively analyze retinal OCT scans in mice for categorical predictions in experimental models. Expanding this approach with additional features, including histopathological and functional correlations, is expected to improve the prediction power further, promising valuable applications to predict more complex outcomes in experimental and clinical studies.

The use of artificial neural networks in studying the progression of glaucoma

In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilities and support physicians in diagnosing and treating patients. This paper focuses on modelling glaucoma evolution because it requires early diagnosis, individualized treatment, and lifelong monitoring. Glaucoma is a chronic, progressive, irreversible, multifactorial optic neuropathy that primarily affects elderly individuals. It is important to emphasize that the processed data are taken from medical records, unlike other studies in the literature that rely on image acquisition and processing. Although more challenging to handle, this approach has the advantage of including a wide range of parameters in large numbers, which can highlight their potential influence. Artificial neural networks are used to study glaucoma progression, designed through successive trials for near-optimal configurations using the NeuroSolutions and PyTorch frameworks. Furthermore, different problems are formulated to demonstrate the influence of various structural and functional parameters on the study of glaucoma progression. Optimal neural networks were obtained using a program written in Python using the PyTorch deep learning framework. For various tasks, very small errors in training and validation, under 5%, were obtained. It has been demonstrated that very good results can be achieved, making them credible and useful for medical practice.

USP33 promotes nonalcoholic fatty acid disease-associated fibrosis in gerbils via the c-myc signaling

Nonalcoholic fatty acid disease (NAFLD) is a common complication of obesity associated with liver fibrosis. The underlying molecular mechanisms involved in the progression from normal to fibrosis remain unclear. Liver tissues from the liver fibrosis model identified the USP33 gene as a key gene in NAFLD-associated fibrosis. USP33 knockdown inhibited hepatic stellate cell activation and glycolysis in gerbils with NAFLD-associated fibrosis. Conversely, overexpression of USP33 caused a contrast function on hepatic stellate cell activation and glycolysis activation, which was inhibited by c-Myc inhibitor 10058-F4. The copy number of short-chain fatty acids-producing bacterium Alistipes sp. AL-1, Mucispirillum schaedleri, Helicobacter hepaticus in the feces, and the total bile acid level in serum were higher in gerbils with NAFLD-associated fibrosis. Bile acid promoted USP33 expression and inhibiting its receptor reversed hepatic stellate cell activation in gerbils with NAFLD-associated fibrosis. These results suggest that the expression of USP33, an important deubiquitinating enzyme, is increased in NAFLD fibrosis. These data also point to hepatic stellate cells as a key cell type that may respond to liver fibrosis via USP33-induced cell activation and glycolysis.

The Discriminatory Ability of Ganglion Cell Inner Plexiform Layer Complex Thickness in Patients with Preperimetric Glaucoma

Purpose

To evaluate diagnostic performance of ganglion cell inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL) parameters measured with Cirrus high-definition optical coherence tomography (OCT) in patients with preperimetric glaucoma.

Methods

In this multicenter cross-sectional study, 150 eyes of 83 patients with preperimetric glaucoma were compared with 200 eyes of age and sex matched healthy subjects. All patients had visual field testing and OCT scanning of GCIPL and RNFL in all quadrants. The independent Samples t-test was used to determine if a difference exists between the means of two independent groups on a continuous dependent variable. The area under the receiver operating characteristic (ROC) curve (AUC) of each parameter was calculated for discriminatory ability between normal controls and preperimetric glaucoma. The sensitivity and specificity were estimated by point coordinates on ROC curve.

Results

The best parameters for distinguishing preperimetric glaucoma from healthy eyes were the combined average GCIPL + average RNFL, followed by average RNFL + GCIPL (inferotemporal), and average RNFL + GCIPL (minimum). The GCIPL parameters with the highest to lowest AUC (in decreasing order) were inferotemporal, followed by average, minimum, superior, inferior, superonasal, inferonasal, superotemporal, and quadrants. The RNFL parameters with the highest to lowest AUC (in decreasing order) were average, followed by nasal, temporal, superior, and inferior quadrants. The sensitivity of combined GCIPL + RNFL parameters ranged 85%-88% and the specificity ranged 76%-88%. The sensitivity for RNFL parameters ranged 80%-90% and the specificity ranged 64%-88%.

Conclusion

GCIPL and RNFL have good discriminatory ability; the sensitivity and specificity increase when both parameters are combined for early detection of glaucoma.

Use of artificial intelligence in forecasting glaucoma progression

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

BACKGROUND

Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

MATERIALS AND METHODS

We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

RESULTS

Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

CONCLUSION

In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models.