Bankstown Hospital - Grand Rounds - Further Reading

Welcome to the Grand Rounds Further Reading List, Ophtalmology edition, brought to you by the Clinical Library, on Level 4, next to the Auditorium.

This library guide is to help support you in your professional development. Please give us feedback so we can improve this list in the future.

If you are presenting at a Grand Rounds, please contact lynne.roberts3@health.nsw.gov.au and tell us about the content of your paper so we can add appropriate resources to the list for when you present your paper.

If you have any questions, please contact the Clinical Library on 9722 8250 or email SWSLHD-BankstownLibrary@health.nsw.gov.au or visit us Monday to Fridays, 8.30am - 5.00pm.

Retina

Gencer, G. and K. Gencer (2025). "Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model." PLoS One 20(2): e0318657 https://doi.org/10.1371/journal.pone.0318657  PDF AT LINK

Background: Retinal problems are critical because they can cause severe vision loss if not treated. Traditional methods for diagnosing retinal disorders often rely heavily on manual interpretation of optical coherence tomography (OCT) images, which can be time-consuming and dependent on the expertise of ophthalmologists. This leads to challenges in early diagnosis, especially as retinal diseases like diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV) become more prevalent. OCT helps ophthalmologists diagnose patients more accurately by allowing for early detection. This paper offers a hybrid SE (Squeeze-and-Excitation)-Enhanced Hybrid Model for detecting retinal disorders from OCT images, including DME, Drusen, and CNV, using artificial intelligence and deep learning. Methods The model integrates SE blocks with EfficientNetB0 and Xception architectures, which provide high success in image classification tasks. EfficientNetB0 achieves high accuracy with fewer parameters through model scaling strategies, while Xception offers powerful feature extraction using deep separable convolutions. The combination of these architectures enhances both the efficiency and classification performance of the model, enabling more accurate detection of retinal disorders from OCT images. Additionally, SE blocks increase the representational ability of the network by adaptively recalibrating per-channel feature responses. Results The combined features from EfficientNetB0 and Xception are processed via fully connected layers and categorized using the Softmax algorithm. The methodology was tested on UCSD and Duke’s OCT datasets and produced excellent results. The proposed SE-Improved Hybrid Model outperformed the current best-known approaches, with accuracy rates of 99.58% on the UCSD dataset and 99.18% on the Duke dataset. Conclusion These findings emphasize the model’s ability to effectively diagnose retinal disorders using OCT images and indicate substantial promise for the development of computer-aided diagnostic tools in the field of ophthalmology.

Stöhr, H. and B. H. F. Weber (2025). "Focus on degenerative retinal disorders." Medizinische Genetik 37(1): 1-2 https://doi.org/10.1515/medgen-2024-2063 PDF AT LINK

Yavas, C., et al. (2025). "Revealing Molecular Diagnosis With Whole Exome Sequencing in Patients With Inherited Retinal Disorders." Clinical Genetics 108(1): 14-21 https://onlinelibrary.wiley.com/doi/abs/10.1111/cge.14708  PDF AT LINK

ABSTRACT Inherited retinal diseases (IRDs) constitute a heterogeneous group of clinically and genetically diverse conditions, standing as a primary cause of visual impairment among individuals aged 15–45, with an estimated incidence of 1:2000. Our study aimed to comprehensively evaluate the genetic variants underlying IRDs in the Turkish population. This study included 50 unrelated Turkish IRD patients and their families. Genomic DNA was extracted from each participant, and candidate variants were identified via next-generation sequencing to determine their pathogenicity. We detected variants in 58% of the patients, of which six novel variants were identified. Among these, 16 cases exhibited variants associated with retinitis pigmentosa and Stargardt disease, while 13 presented variants linked to other retinal diseases. The spectrum of identified variants included 21 homozygous cases and five compound heterozygous variants, both indicative of autosomal recessive inheritance. Three cases revealed heterozygous variants suggestive of autosomal dominant inheritance, and two cases featured hemizygous variants suggestive of X-linked inheritance. Importantly, no matches with copy number variants were detected in our analysis. This study comprehensively portrays clinical and genetic profiles within the Turkish population affected by IRDs. Identifying novel variants and delineating inheritance patterns contribute to a deeper understanding of the genetic diagnosis of IRDs, paving the way for more precise diagnostic and therapeutic interventions.

Zhang, Z., et al. (2025). "Causal Relationships Between Retinal Diseases and Psychiatric Disorders Have Implications for Precision Psychiatry." Molecular Neurobiology 62(3): 3182-3194 https://doi.org/10.1007/s12035-024-04456-2 PDF AT LINK

Observational studies and clinical trials have reported potential associations between retinal diseases and psychiatric disorders. However, the causal associations between them have remained elusive. In this study, we used bi-directional two-sample Mendelian randomization (MR) analysis to explore unconfounded causal relationships between retinal diseases and psychiatric disorders using large-scale genome-wide association study (GWAS) summary statistics of over 500,000 participants of European ancestry from the FinnGen project, the Psychiatric Genomics Consortium, the European Bioinformatics Institute, and the UK Biobank. Our MR analysis revealed significant causal relationships between major retinal diseases and specific psychiatric disorders. Specifically, susceptibility to dry age-related macular degeneration was associated with a reduced risk of anorexia nervosa (OR = 0.970; 95% CI = 0.930 ~ 0.994; P = 0.025). Furthermore, we found some evidence that exposure to diabetic retinopathy was associated with an increased risk of schizophrenia (OR = 1.021; 95% CI 1.012 ~ 1.049; P = 0.001), and exposure to retinal detachments and breaks was associated with an increased risk of attention deficit hyperactivity disorder (OR = 1.190; 95% CI 1.063 ~ 1.333; P = 0.003). These causal relationships were not confounded by biases of pleiotropy and reverse causation. Our study highlights the importance of preventing and managing retinal disease as a potential avenue for improving the prevention, management and treatment of major psychiatric disorders.